| 1 | Sequential Causal Imitation Learning with Unobserved Confounders | 7.00 | 0.71 | 8, 7, 7, 6 | Oral | Poster | ✔ |
| 2 | Sequential Causal Imitation Learning with Unobserved Confounders | 7.40 | 0.80 | 9, 7, 7, 7, 7 | Oral | Oral | ✔ |
| 3 | Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification | 8.00 | 0.71 | 8, 7, 9, 8 | Oral | Oral | ✔ |
| 4 | Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification | 7.33 | 0.47 | 8, 7, 7 | Oral | Poster | ✔ |
| 5 | Learning Frequency Domain Approximation for Binary Neural Networks | 7.00 | 0.82 | 6, 7, 8 | Oral | Poster | ✔ |
| 6 | Learning Frequency Domain Approximation for Binary Neural Networks | 7.75 | 0.43 | 7, 8, 8, 8 | Oral | Oral | ✔ |
| 7 | Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons | 6.67 | 2.62 | 3, 8, 9 | Oral | Poster | ✔ |
| 8 | Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons | 8.00 | 0.71 | 9, 8, 7, 8 | Oral | Oral | ✔ |
| 9 | Causal Identification with Matrix Equations | 6.00 | 0.00 | 6, 6, 6, 6 | Oral | Poster | ✔ |
| 10 | Causal Identification with Matrix Equations | 7.00 | 0.82 | 7, 6, 8 | Oral | Oral | ✔ |
| 11 | Variational Inference for Continuous-Time Switching Dynamical Systems | 7.25 | 0.43 | 7, 7, 7, 8 | Spotlight | Spotlight | ✔ |
| 12 | Variational Inference for Continuous-Time Switching Dynamical Systems | 6.50 | 0.87 | 8, 6, 6, 6 | Spotlight | Poster | ✔ |
| 13 | Statistical Query Lower Bounds for List-Decodable Linear Regression | 7.50 | 0.50 | 8, 8, 7, 7 | Spotlight | Spotlight | ✔ |
| 14 | Statistical Query Lower Bounds for List-Decodable Linear Regression | 7.75 | 0.43 | 8, 8, 8, 7 | Spotlight | Spotlight | ✔ |
| 15 | Sliced Mutual Information: A Scalable Measure of Statistical Dependence | 7.00 | 0.71 | 8, 7, 7, 6 | Spotlight | Spotlight | ✔ |
| 16 | Sliced Mutual Information: A Scalable Measure of Statistical Dependence | 6.00 | 1.22 | 7, 6, 7, 4 | Spotlight | Poster | ✔ |
| 17 | Sequence-to-Sequence Learning with Latent Neural Grammars | 6.75 | 0.83 | 6, 7, 8, 6 | Spotlight | Poster | ✔ |
| 18 | Sequence-to-Sequence Learning with Latent Neural Grammars | 7.50 | 0.87 | 9, 7, 7, 7 | Spotlight | Spotlight | ✔ |
| 19 | Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation | 5.25 | 0.43 | 5, 5, 6, 5 | Spotlight | Reject | ✔ |
| 20 | Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation | 6.75 | 0.43 | 7, 7, 6, 7 | Spotlight | Spotlight | ✔ |
| 21 | Program Synthesis Guided Reinforcement Learning for Partially Observed Environments | 4.80 | 1.17 | 5, 4, 4, 4, 7 | Spotlight | Reject | ✔ |
| 22 | Program Synthesis Guided Reinforcement Learning for Partially Observed Environments | 6.25 | 1.30 | 7, 7, 4, 7 | Spotlight | Spotlight | ✔ |
| 23 | On the Value of Infinite Gradients in Variational Autoencoder Models | 7.33 | 0.47 | 8, 7, 7 | Spotlight | Spotlight | ✔ |
| 24 | On the Value of Infinite Gradients in Variational Autoencoder Models | 5.75 | 0.43 | 6, 5, 6, 6 | Spotlight | Reject | ✔ |
| 25 | On the Power of Differentiable Learning versus PAC and SQ Learning | 7.00 | 0.00 | 7, 7, 7 | Spotlight | Spotlight | ✔ |
| 26 | On the Power of Differentiable Learning versus PAC and SQ Learning | 7.00 | 0.00 | 7, 7, 7, 7 | Spotlight | Poster | ✔ |
| 27 | Offline RL Without Off-Policy Evaluation | 6.25 | 1.92 | 8, 7, 7, 3 | Spotlight | Poster | ✔ |
| 28 | Offline RL Without Off-Policy Evaluation | 7.75 | 0.83 | 8, 9, 7, 7 | Spotlight | Spotlight | ✔ |
| 29 | Learning with Holographic Reduced Representations | 6.67 | 0.47 | 7, 6, 7 | Spotlight | Poster | ✔ |
| 30 | Learning with Holographic Reduced Representations | 7.00 | 0.71 | 7, 6, 7, 8 | Spotlight | Spotlight | ✔ |
| 31 | Learning Generalized Gumbel-max Causal Mechanisms | 6.50 | 0.87 | 5, 7, 7, 7 | Spotlight | Spotlight | ✔ |
| 32 | Learning Generalized Gumbel-max Causal Mechanisms | 4.33 | 0.94 | 5, 5, 3 | Spotlight | Reject | ✔ |
| 33 | Learning Disentangled Behavior Embeddings | 5.00 | 0.82 | 5, 6, 4 | Spotlight | Reject | ✔ |
| 34 | Learning Disentangled Behavior Embeddings | 7.25 | 0.43 | 7, 7, 7, 8 | Spotlight | Spotlight | ✔ |
| 35 | Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks | 6.67 | 0.47 | 7, 6, 7 | Spotlight | Spotlight | ✔ |
| 36 | Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks | 5.50 | 1.50 | 7, 6, 6, 3 | Spotlight | Poster | ✔ |
| 37 | Forster Decomposition and Learning Halfspaces with Noise | 7.25 | 0.83 | 8, 8, 6, 7 | Spotlight | Spotlight | ✔ |
| 38 | Forster Decomposition and Learning Halfspaces with Noise | 6.25 | 0.43 | 6, 6, 6, 7 | Spotlight | Poster | ✔ |
| 39 | Early-stopped neural networks are consistent | 6.25 | 0.43 | 7, 6, 6, 6 | Spotlight | Spotlight | ✔ |
| 40 | Early-stopped neural networks are consistent | 7.00 | 0.89 | 6, 8, 8, 6, 7 | Spotlight | Spotlight | ✔ |
| 41 | Diffusion Models Beat GANs on Image Synthesis | 7.50 | 1.12 | 9, 8, 7, 6 | Spotlight | Spotlight | ✔ |
| 42 | Diffusion Models Beat GANs on Image Synthesis | 7.50 | 0.87 | 9, 7, 7, 7 | Spotlight | Spotlight | ✔ |
| 43 | Coresets for Decision Trees of Signals | 6.50 | 1.50 | 8, 7, 7, 4 | Spotlight | Oral | ✔ |
| 44 | Coresets for Decision Trees of Signals | 6.67 | 0.47 | 7, 7, 6 | Spotlight | Poster | ✔ |
| 45 | Continuous vs. Discrete Optimization of Deep Neural Networks | 5.00 | 1.58 | 6, 7, 3, 4 | Spotlight | Reject | ✔ |
| 46 | Continuous vs. Discrete Optimization of Deep Neural Networks | 7.00 | 0.63 | 8, 7, 7, 6, 7 | Spotlight | Spotlight | ✔ |
| 47 | Collaborating with Humans without Human Data | 6.00 | 1.41 | 5, 8, 5 | Spotlight | Reject | ✔ |
| 48 | Collaborating with Humans without Human Data | 7.25 | 1.48 | 7, 8, 5, 9 | Spotlight | Spotlight | ✔ |
| 49 | Clustering Effect of Adversarial Robust Models | 6.00 | 0.82 | 6, 7, 5 | Spotlight | Poster | ✔ |
| 50 | Clustering Effect of Adversarial Robust Models | 6.33 | 0.47 | 6, 7, 6 | Spotlight | Spotlight | ✔ |
| 51 | Closing the Gap: Tighter Analysis of Alternating Stochastic Gradient Methods for Bilevel Problems | 6.25 | 0.43 | 6, 6, 6, 7 | Spotlight | Poster | ✔ |
| 52 | Closing the Gap: Tighter Analysis of Alternating Stochastic Gradient Methods for Bilevel Problems | 7.75 | 0.43 | 7, 8, 8, 8 | Spotlight | Spotlight | ✔ |
| 53 | Beyond Tikhonov: faster learning with self-concordant losses, via iterative regularization | 7.25 | 0.43 | 8, 7, 7, 7 | Spotlight | Poster | ✔ |
| 54 | Beyond Tikhonov: faster learning with self-concordant losses, via iterative regularization | 7.75 | 0.43 | 7, 8, 8, 8 | Spotlight | Spotlight | ✔ |
| 55 | Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning | 7.00 | 0.00 | 7, 7, 7, 7 | Spotlight | Spotlight | ✔ |
| 56 | Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning | 5.50 | 0.50 | 6, 5, 5, 6 | Spotlight | Reject | ✔ |
| 57 | An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence | 7.00 | 0.00 | 7, 7, 7, 7 | Spotlight | Spotlight | ✔ |
| 58 | An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence | 6.75 | 0.83 | 6, 8, 7, 6 | Spotlight | Poster | ✔ |
| 59 | Aligned Structured Sparsity Learning for Efficient Image Super-Resolution | 8.00 | 0.00 | 8, 8, 8 | Spotlight | Spotlight | ✔ |
| 60 | Aligned Structured Sparsity Learning for Efficient Image Super-Resolution | 4.33 | 0.47 | 4, 4, 5 | Spotlight | Reject | ✔ |
| 61 | α
-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression | 4.33 | 0.47 | 4, 5, 4 | Poster | Reject | ✔ |
| 62 | α
-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression | 6.71 | 0.45 | 7, 7, 7, 7, 7, 6, 6 | Poster | Poster | ✔ |
| 63 | Zero Time Waste: Recycling Predictions in Early Exit Neural Networks | 6.25 | 1.30 | 7, 5, 5, 8 | Poster | Poster | ✔ |
| 64 | Zero Time Waste: Recycling Predictions in Early Exit Neural Networks | 4.50 | 0.87 | 6, 4, 4, 4 | Poster | Reject | ✔ |
| 65 | XDO: A Double Oracle Algorithm for Extensive-Form Games | 5.50 | 0.50 | 5, 6, 6, 5 | Poster | Poster | ✔ |
| 66 | XDO: A Double Oracle Algorithm for Extensive-Form Games | 6.00 | 0.71 | 7, 6, 6, 5 | Poster | Poster | ✔ |
| 67 | Which Mutual-Information Representation Learning Objectives are Sufficient for Control? | 6.25 | 0.43 | 7, 6, 6, 6 | Poster | Poster | ✔ |
| 68 | Which Mutual-Information Representation Learning Objectives are Sufficient for Control? | 6.00 | 0.71 | 5, 6, 6, 7 | Poster | Reject | ✔ |
| 69 | When Expressivity Meets Trainability: Fewer than
n
Neurons Can Work | 6.75 | 0.83 | 6, 7, 8, 6 | Poster | Poster | ✔ |
| 70 | When Expressivity Meets Trainability: Fewer than
n
Neurons Can Work | 5.80 | 0.40 | 5, 6, 6, 6, 6 | Poster | Reject | ✔ |
| 71 | What training reveals about neural network complexity | 6.60 | 0.49 | 6, 7, 7, 7, 6 | Poster | Poster | ✔ |
| 72 | What training reveals about neural network complexity | 6.75 | 1.30 | 6, 5, 8, 8 | Poster | Poster | ✔ |
| 73 | Weak-shot Fine-grained Classification via Similarity Transfer | 4.25 | 0.83 | 3, 5, 5, 4 | Poster | Reject | ✔ |
| 74 | Weak-shot Fine-grained Classification via Similarity Transfer | 6.33 | 0.47 | 7, 6, 6 | Poster | Poster | ✔ |
| 75 | VoiceMixer: Adversarial Voice Style Mixup | 6.75 | 1.64 | 8, 8, 7, 4 | Poster | Poster | ✔ |
| 76 | VoiceMixer: Adversarial Voice Style Mixup | 6.50 | 0.87 | 7, 7, 5, 7 | Poster | Poster | ✔ |
| 77 | Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases | 6.75 | 1.09 | 8, 5, 7, 7 | Poster | Poster | ✔ |
| 78 | Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases | 7.00 | 0.82 | 8, 6, 7 | Poster | Poster | ✔ |
| 79 | VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media | 5.00 | 0.71 | 4, 5, 5, 6 | Poster | Reject | ✔ |
| 80 | VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media | 6.50 | 0.87 | 7, 7, 7, 5 | Poster | Poster | ✔ |
| 81 | Video Instance Segmentation using Inter-Frame Communication Transformers | 6.25 | 0.43 | 6, 6, 7, 6 | Poster | Poster | ✔ |
| 82 | Video Instance Segmentation using Inter-Frame Communication Transformers | 4.50 | 0.87 | 5, 5, 3, 5 | Poster | Reject | ✔ |
| 83 | ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias | 5.33 | 0.47 | 5, 5, 6 | Poster | Reject | ✔ |
| 84 | ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias | 7.25 | 0.83 | 6, 8, 7, 8 | Poster | Poster | ✔ |
| 85 | User-Level Differentially Private Learning via Correlated Sampling | 7.20 | 0.40 | 7, 7, 7, 7, 8 | Poster | Poster | ✔ |
| 86 | User-Level Differentially Private Learning via Correlated Sampling | 7.00 | 0.00 | 7, 7, 7, 7 | Poster | Poster | ✔ |
| 87 | Unsupervised Object-Based Transition Models For 3D Partially Observable Environments | 5.75 | 1.48 | 4, 5, 8, 6 | Poster | Reject | ✔ |
| 88 | Unsupervised Object-Based Transition Models For 3D Partially Observable Environments | 6.50 | 0.96 | 5, 7, 8, 6, 6, 7 | Poster | Poster | ✔ |
| 89 | Universal Approximation Using Well-Conditioned Normalizing Flows | 6.86 | 0.35 | 7, 7, 6, 7, 7, 7, 7 | Poster | Poster | ✔ |
| 90 | Universal Approximation Using Well-Conditioned Normalizing Flows | 6.00 | 0.00 | 6, 6, 6 | Poster | Poster | ✔ |
| 91 | Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization | 4.33 | 1.11 | 3, 3, 4, 6, 5, 5 | Poster | Reject | ✔ |
| 92 | Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization | 6.50 | 0.50 | 6, 6, 7, 7 | Poster | Poster | ✔ |
| 93 | Understanding Bandits with Graph Feedback | 6.50 | 0.50 | 7, 6, 7, 6 | Poster | Poster | ✔ |
| 94 | Understanding Bandits with Graph Feedback | 6.00 | 0.71 | 5, 6, 6, 7 | Poster | Poster | ✔ |
| 95 | Uncertainty-Driven Loss for Single Image Super-Resolution | 4.50 | 0.50 | 4, 4, 5, 5 | Poster | Reject | ✔ |
| 96 | Uncertainty-Driven Loss for Single Image Super-Resolution | 6.67 | 0.47 | 7, 7, 6 | Poster | Poster | ✔ |
| 97 | Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution | 6.50 | 0.50 | 6, 7, 6, 7 | Poster | Poster | ✔ |
| 98 | Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution | 6.00 | 1.22 | 6, 8, 5, 5 | Poster | Poster | ✔ |
| 99 | Towards a Theoretical Framework of Out-of-Distribution Generalization | 6.25 | 1.09 | 6, 5, 8, 6 | Poster | Poster | ✔ |
| 100 | Towards a Theoretical Framework of Out-of-Distribution Generalization | 5.00 | 0.71 | 5, 4, 6, 5 | Poster | Reject | ✔ |
| 101 | Towards Multi-Grained Explainability for Graph Neural Networks | 6.25 | 0.43 | 6, 6, 7, 6 | Poster | Poster | ✔ |
| 102 | Towards Multi-Grained Explainability for Graph Neural Networks | 5.75 | 1.30 | 5, 7, 7, 4 | Poster | Reject | ✔ |
| 103 | Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples | 6.00 | 0.00 | 6, 6, 6, 6 | Poster | Poster | ✔ |
| 104 | Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples | 4.00 | 0.71 | 4, 4, 3, 5 | Poster | Reject | ✔ |
| 105 | Topology-Imbalance Learning for Semi-Supervised Node Classification | 6.00 | 0.82 | 7, 5, 6 | Poster | Poster | ✔ |
| 106 | Topology-Imbalance Learning for Semi-Supervised Node Classification | 5.33 | 0.47 | 5, 6, 5 | Poster | Reject | ✔ |
| 107 | The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle | 6.25 | 0.43 | 6, 7, 6, 6 | Poster | Poster | ✔ |
| 108 | The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle | 5.00 | 1.22 | 4, 5, 4, 7 | Poster | Reject | ✔ |
| 109 | Support Recovery of Sparse Signals from a Mixture of Linear Measurements | 6.25 | 0.43 | 6, 6, 7, 6 | Poster | Reject | ✔ |
| 110 | Support Recovery of Sparse Signals from a Mixture of Linear Measurements | 7.00 | 0.00 | 7, 7, 7 | Poster | Poster | ✔ |
| 111 | Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families | 6.20 | 0.75 | 7, 7, 6, 6, 5 | Poster | Poster | ✔ |
| 112 | Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families | 5.75 | 1.48 | 4, 8, 6, 5 | Poster | Reject | ✔ |
| 113 | Stronger NAS with Weaker Predictors | 5.80 | 0.98 | 4, 6, 7, 6, 6 | Poster | Poster | ✔ |
| 114 | Stronger NAS with Weaker Predictors | 6.80 | 0.75 | 6, 7, 6, 7, 8 | Poster | Poster | ✔ |
| 115 | Streaming Belief Propagation for Community Detection | 6.00 | 1.22 | 5, 5, 6, 8 | Poster | Reject | ✔ |
| 116 | Streaming Belief Propagation for Community Detection | 6.67 | 0.47 | 7, 6, 7 | Poster | Poster | ✔ |
| 117 | Stochastic Online Linear Regression: the Forward Algorithm to Replace Ridge | 7.25 | 0.43 | 8, 7, 7, 7 | Poster | Spotlight | ✔ |
| 118 | Stochastic Online Linear Regression: the Forward Algorithm to Replace Ridge | 5.25 | 0.43 | 5, 5, 6, 5 | Poster | Reject | ✔ |
| 119 | Stochastic Anderson Mixing for Nonconvex Stochastic Optimization | 6.25 | 1.30 | 7, 7, 4, 7 | Poster | Poster | ✔ |
| 120 | Stochastic Anderson Mixing for Nonconvex Stochastic Optimization | 6.25 | 0.43 | 6, 6, 6, 7 | Poster | Poster | ✔ |
| 121 | Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space | 6.67 | 1.49 | 9, 8, 5, 6, 5, 7 | Poster | Poster | ✔ |
| 122 | Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space | 6.25 | 0.43 | 7, 6, 6, 6 | Poster | Poster | ✔ |
| 123 | Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation | 6.00 | 0.00 | 6, 6, 6, 6 | Poster | Poster | ✔ |
| 124 | Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation | 6.33 | 0.47 | 6, 6, 7 | Poster | Poster | ✔ |
| 125 | Skipping the Frame-Level: Event-Based Piano Transcription With Neural Semi-CRFs | 7.00 | 0.00 | 7, 7, 7 | Poster | Poster | ✔ |
| 126 | Skipping the Frame-Level: Event-Based Piano Transcription With Neural Semi-CRFs | 5.00 | 0.71 | 5, 6, 5, 4 | Poster | Reject | ✔ |
| 127 | Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning | 7.00 | 1.63 | 7, 5, 9 | Poster | Poster | ✔ |
| 128 | Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning | 7.00 | 0.71 | 8, 7, 7, 6 | Poster | Poster | ✔ |
| 129 | Shared Independent Component Analysis for Multi-Subject Neuroimaging | 7.00 | 0.00 | 7, 7, 7, 7 | Poster | Poster | ✔ |
| 130 | Shared Independent Component Analysis for Multi-Subject Neuroimaging | 5.25 | 0.83 | 6, 4, 5, 6 | Poster | Poster | ✔ |
| 131 | Self-Supervised Bug Detection and Repair | 7.00 | 1.63 | 5, 9, 7 | Poster | Poster | ✔ |
| 132 | Self-Supervised Bug Detection and Repair | 6.67 | 0.47 | 6, 7, 7 | Poster | Poster | ✔ |
| 133 | Scaling Vision with Sparse Mixture of Experts | 6.75 | 0.83 | 6, 8, 6, 7 | Poster | Poster | ✔ |
| 134 | Scaling Vision with Sparse Mixture of Experts | 7.25 | 1.09 | 7, 9, 7, 6 | Poster | Poster | ✔ |
| 135 | Scaling Neural Tangent Kernels via Sketching and Random Features | 6.60 | 0.49 | 7, 6, 7, 7, 6 | Poster | Poster | ✔ |
| 136 | Scaling Neural Tangent Kernels via Sketching and Random Features | 6.25 | 0.43 | 6, 6, 6, 7 | Poster | Poster | ✔ |
| 137 | Scalars are universal: Equivariant machine learning, structured like classical physics | 6.25 | 0.83 | 5, 7, 6, 7 | Poster | Poster | ✔ |
| 138 | Scalars are universal: Equivariant machine learning, structured like classical physics | 5.80 | 0.98 | 4, 6, 7, 6, 6 | Poster | Poster | ✔ |
| 139 | Scalable Thompson Sampling using Sparse Gaussian Process Models | 6.25 | 0.83 | 7, 7, 6, 5 | Poster | Poster | ✔ |
| 140 | Scalable Thompson Sampling using Sparse Gaussian Process Models | 5.75 | 1.09 | 4, 6, 7, 6 | Poster | Poster | ✔ |
| 141 | Sample Selection for Fair and Robust Training | 6.00 | 0.00 | 6, 6, 6, 6 | Poster | Poster | ✔ |
| 142 | Sample Selection for Fair and Robust Training | 4.50 | 0.50 | 5, 4, 4, 5 | Poster | Reject | ✔ |
| 143 | STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data | 4.25 | 1.30 | 6, 3, 5, 3 | Poster | Reject | ✔ |
| 144 | STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data | 7.50 | 1.12 | 9, 8, 6, 7 | Poster | Poster | ✔ |
| 145 | STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning | 5.75 | 0.43 | 6, 6, 5, 6 | Poster | Reject | ✔ |
| 146 | STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning | 6.67 | 0.94 | 6, 6, 8 | Poster | Poster | ✔ |
| 147 | SSMF: Shifting Seasonal Matrix Factorization | 6.25 | 0.83 | 6, 7, 5, 7 | Poster | Poster | ✔ |
| 148 | SSMF: Shifting Seasonal Matrix Factorization | 4.67 | 0.47 | 5, 4, 5 | Poster | Reject | ✔ |
| 149 | SNIPS: Solving Noisy Inverse Problems Stochastically | 4.50 | 0.50 | 4, 4, 5, 5 | Poster | Reject | ✔ |
| 150 | SNIPS: Solving Noisy Inverse Problems Stochastically | 6.75 | 1.09 | 7, 7, 8, 5 | Poster | Spotlight | ✔ |
| 151 | SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios | 6.50 | 0.50 | 6, 6, 7, 7 | Poster | Poster | ✔ |
| 152 | SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios | 5.25 | 0.43 | 6, 5, 5, 5 | Poster | Reject | ✔ |
| 153 | SILG: The Multi-domain Symbolic Interactive Language Grounding Benchmark | 6.25 | 1.30 | 4, 7, 7, 7 | Poster | Reject | ✔ |
| 154 | SILG: The Multi-domain Symbolic Interactive Language Grounding Benchmark | 6.75 | 0.43 | 7, 7, 6, 7 | Poster | Poster | ✔ |
| 155 | SBO-RNN: Reformulating Recurrent Neural Networks via Stochastic Bilevel Optimization | 4.75 | 0.43 | 5, 5, 5, 4 | Poster | Reject | ✔ |
| 156 | SBO-RNN: Reformulating Recurrent Neural Networks via Stochastic Bilevel Optimization | 7.00 | 0.71 | 7, 8, 6, 7 | Poster | Poster | ✔ |
| 157 | SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization | 6.75 | 1.09 | 5, 7, 8, 7 | Poster | Poster | ✔ |
| 158 | SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization | 7.50 | 1.12 | 7, 6, 9, 8 | Poster | Poster | ✔ |
| 159 | SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL | 5.00 | 0.71 | 4, 5, 5, 6 | Poster | Reject | ✔ |
| 160 | SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL | 6.50 | 0.50 | 6, 6, 7, 7 | Poster | Poster | ✔ |
| 161 | Row-clustering of a Point Process-valued Matrix | 6.25 | 0.43 | 6, 7, 6, 6 | Poster | Poster | ✔ |
| 162 | Row-clustering of a Point Process-valued Matrix | 5.25 | 1.09 | 4, 7, 5, 5 | Poster | Reject | ✔ |
| 163 | Robust Counterfactual Explanations on Graph Neural Networks | 6.00 | 0.71 | 5, 6, 7, 6 | Poster | Poster | ✔ |
| 164 | Robust Counterfactual Explanations on Graph Neural Networks | 4.67 | 1.25 | 6, 3, 5 | Poster | Reject | ✔ |
| 165 | Risk-Averse Bayes-Adaptive Reinforcement Learning | 6.50 | 0.50 | 6, 7, 7, 6 | Poster | Poster | ✔ |
| 166 | Risk-Averse Bayes-Adaptive Reinforcement Learning | 5.60 | 1.02 | 6, 7, 5, 4, 6 | Poster | Reject | ✔ |
| 167 | Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning | 6.75 | 0.83 | 6, 8, 7, 6 | Poster | Poster | ✔ |
| 168 | Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning | 6.00 | 0.89 | 5, 6, 7, 7, 5 | Poster | Reject | ✔ |
| 169 | Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation | 5.50 | 0.50 | 5, 6, 6, 5 | Poster | Reject | ✔ |
| 170 | Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation | 6.00 | 0.00 | 6, 6, 6, 6 | Poster | Poster | ✔ |
| 171 | Reusing Combinatorial Structure: Faster Iterative Projections over Submodular Base Polytopes | 6.25 | 0.83 | 5, 6, 7, 7 | Poster | Poster | ✔ |
| 172 | Reusing Combinatorial Structure: Faster Iterative Projections over Submodular Base Polytopes | 5.25 | 0.83 | 6, 5, 4, 6 | Poster | Reject | ✔ |
| 173 | Residual2Vec: Debiasing graph embedding with random graphs | 4.75 | 0.83 | 6, 4, 5, 4 | Poster | Reject | ✔ |
| 174 | Residual2Vec: Debiasing graph embedding with random graphs | 6.75 | 0.83 | 7, 8, 6, 6 | Poster | Spotlight | ✔ |
| 175 | Representation Costs of Linear Neural Networks: Analysis and Design | 6.00 | 0.00 | 6, 6, 6 | Poster | Reject | ✔ |
| 176 | Representation Costs of Linear Neural Networks: Analysis and Design | 7.00 | 0.71 | 8, 6, 7, 7 | Poster | Poster | ✔ |
| 177 | Reliable Post hoc Explanations: Modeling Uncertainty in Explainability | 6.67 | 0.47 | 7, 6, 7 | Poster | Poster | ✔ |
| 178 | Reliable Post hoc Explanations: Modeling Uncertainty in Explainability | 6.50 | 2.06 | 8, 8, 3, 7 | Poster | Poster | ✔ |
| 179 | Reliable Decisions with Threshold Calibration | 6.25 | 1.79 | 4, 8, 5, 8 | Poster | Poster | ✔ |
| 180 | Reliable Decisions with Threshold Calibration | 6.75 | 1.79 | 9, 4, 7, 7 | Poster | Poster | ✔ |
| 181 | Relational Self-Attention: What's Missing in Attention for Video Understanding | 6.67 | 0.47 | 7, 6, 7 | Poster | Spotlight | ✔ |
| 182 | Relational Self-Attention: What's Missing in Attention for Video Understanding | 6.00 | 0.71 | 6, 5, 7, 6 | Poster | Reject | ✔ |
| 183 | Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection | 6.00 | 1.00 | 7, 5, 7, 5 | Poster | Poster | ✔ |
| 184 | Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection | 5.33 | 0.47 | 6, 5, 5 | Poster | Reject | ✔ |
| 185 | Regret Minimization Experience Replay in Off-Policy Reinforcement Learning | 6.50 | 2.29 | 8, 9, 3, 6 | Poster | Poster | ✔ |
| 186 | Regret Minimization Experience Replay in Off-Policy Reinforcement Learning | 6.00 | 1.87 | 6, 5, 4, 9 | Poster | Reject | ✔ |
| 187 | Referring Transformer: A One-step Approach to Multi-task Visual Grounding | 5.67 | 0.94 | 5, 7, 5 | Poster | Reject | ✔ |
| 188 | Referring Transformer: A One-step Approach to Multi-task Visual Grounding | 6.00 | 0.00 | 6, 6, 6, 6 | Poster | Poster | ✔ |
| 189 | Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias | 7.00 | 0.00 | 7, 7, 7, 7 | Poster | Poster | ✔ |
| 190 | Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias | 6.33 | 0.94 | 5, 7, 7 | Poster | Poster | ✔ |
| 191 | Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networks | 4.25 | 0.43 | 5, 4, 4, 4 | Poster | Reject | ✔ |
| 192 | Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networks | 6.50 | 1.12 | 5, 6, 8, 7 | Poster | Spotlight | ✔ |
| 193 | Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training | 5.00 | 1.22 | 5, 7, 4, 4 | Poster | Reject | ✔ |
| 194 | Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training | 6.50 | 0.50 | 7, 6, 6, 7 | Poster | Poster | ✔ |
| 195 | QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning | 6.25 | 0.43 | 7, 6, 6, 6 | Poster | Poster | ✔ |
| 196 | QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning | 6.00 | 1.00 | 5, 7, 5, 7 | Poster | Poster | ✔ |
| 197 | Provably efficient, succinct, and precise explanations | 6.75 | 1.30 | 8, 8, 5, 6 | Poster | Reject | ✔ |
| 198 | Provably efficient, succinct, and precise explanations | 6.67 | 0.47 | 7, 6, 7 | Poster | Poster | ✔ |
| 199 | Provably Strict Generalisation Benefit for Invariance in Kernel Methods | 6.00 | 1.22 | 8, 5, 5, 6 | Poster | Poster | ✔ |
| 200 | Provably Strict Generalisation Benefit for Invariance in Kernel Methods | 7.00 | 1.41 | 7, 9, 5, 7 | Poster | Poster | ✔ |
| 201 | Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints | 6.00 | 0.00 | 6, 6, 6, 6 | Poster | Poster | ✔ |
| 202 | Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints | 5.50 | 0.50 | 5, 6, 6, 5 | Poster | Reject | ✔ |
| 203 | Probability Paths and the Structure of Predictions over Time | 5.50 | 0.87 | 7, 5, 5, 5 | Poster | Reject | ✔ |
| 204 | Probability Paths and the Structure of Predictions over Time | 5.75 | 0.43 | 6, 6, 5, 6 | Poster | Poster | ✔ |
| 205 | Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs | 6.50 | 0.50 | 7, 6, 6, 7 | Poster | Poster | ✔ |
| 206 | Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs | 4.75 | 0.43 | 5, 4, 5, 5 | Poster | Reject | ✔ |
| 207 | Private and Non-private Uniformity Testing for Ranking Data | 6.50 | 0.50 | 6, 6, 7, 7 | Poster | Poster | ✔ |
| 208 | Private and Non-private Uniformity Testing for Ranking Data | 6.67 | 0.47 | 7, 6, 7 | Poster | Poster | ✔ |
| 209 | Post-processing for Individual Fairness | 5.50 | 1.12 | 4, 6, 5, 7 | Poster | Reject | ✔ |
| 210 | Post-processing for Individual Fairness | 6.50 | 0.50 | 6, 6, 7, 7 | Poster | Poster | ✔ |
| 211 | Post-Training Quantization for Vision Transformer | 5.75 | 1.64 | 3, 7, 6, 7 | Poster | Poster | ✔ |
| 212 | Post-Training Quantization for Vision Transformer | 5.25 | 0.83 | 4, 6, 6, 5 | Poster | Reject | ✔ |
| 213 | Pooling by Sliced-Wasserstein Embedding | 5.67 | 1.70 | 5, 8, 4 | Poster | Reject | ✔ |
| 214 | Pooling by Sliced-Wasserstein Embedding | 6.25 | 0.83 | 6, 7, 5, 7 | Poster | Poster | ✔ |
| 215 | PolarStream: Streaming Object Detection and Segmentation with Polar Pillars | 5.00 | 0.00 | 5, 5, 5 | Poster | Reject | ✔ |
| 216 | PolarStream: Streaming Object Detection and Segmentation with Polar Pillars | 6.25 | 0.43 | 6, 6, 7, 6 | Poster | Poster | ✔ |
| 217 | PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learning | 6.67 | 0.47 | 6, 7, 7 | Poster | Poster | ✔ |
| 218 | PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learning | 6.25 | 0.43 | 6, 7, 6, 6 | Poster | Poster | ✔ |
| 219 | Play to Grade: Testing Coding Games as Classifying Markov Decision Process | 7.00 | 1.41 | 9, 6, 6 | Poster | Poster | ✔ |
| 220 | Play to Grade: Testing Coding Games as Classifying Markov Decision Process | 7.00 | 0.82 | 8, 6, 7 | Poster | Poster | ✔ |
| 221 | Pipeline Combinators for Gradual AutoML | 6.00 | 1.22 | 4, 7, 6, 7 | Poster | Poster | ✔ |
| 222 | Pipeline Combinators for Gradual AutoML | 5.00 | 1.87 | 3, 8, 5, 4 | Poster | Reject | ✔ |
| 223 | Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning | 5.25 | 0.83 | 6, 5, 4, 6 | Poster | Reject | ✔ |
| 224 | Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning | 7.00 | 1.00 | 8, 6, 6, 8 | Poster | Poster | ✔ |
| 225 | Periodic Activation Functions Induce Stationarity | 6.75 | 0.43 | 7, 6, 7, 7 | Poster | Poster | ✔ |
| 226 | Periodic Activation Functions Induce Stationarity | 6.75 | 0.43 | 7, 7, 6, 7 | Poster | Poster | ✔ |
| 227 | Pay Better Attention to Attention: Head Selection in Multilingual and Multi-Domain Sequence Modeling | 6.50 | 0.50 | 7, 7, 6, 6 | Poster | Poster | ✔ |
| 228 | Pay Better Attention to Attention: Head Selection in Multilingual and Multi-Domain Sequence Modeling | 6.00 | 0.71 | 6, 5, 7, 6 | Poster | Reject | ✔ |
| 229 | Parallelizing Thompson Sampling | 6.25 | 1.48 | 7, 6, 4, 8 | Poster | Poster | ✔ |
| 230 | Parallelizing Thompson Sampling | 4.75 | 1.09 | 6, 3, 5, 5 | Poster | Reject | ✔ |
| 231 | ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions | 6.50 | 0.50 | 6, 6, 7, 7 | Poster | Poster | ✔ |
| 232 | ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions | 5.50 | 0.87 | 7, 5, 5, 5 | Poster | Reject | ✔ |
| 233 | POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples | 5.75 | 1.09 | 6, 7, 6, 4 | Poster | Poster | ✔ |
| 234 | POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples | 5.00 | 0.71 | 5, 6, 5, 4 | Poster | Reject | ✔ |
| 235 | Optimizing Reusable Knowledge for Continual Learning via Metalearning | 5.50 | 0.50 | 5, 6, 5, 6 | Poster | Poster | ✔ |
| 236 | Optimizing Reusable Knowledge for Continual Learning via Metalearning | 5.75 | 0.83 | 7, 6, 5, 5 | Poster | Reject | ✔ |
| 237 | Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning | 5.00 | 1.58 | 4, 7, 6, 3 | Poster | Reject | ✔ |
| 238 | Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning | 5.33 | 0.94 | 6, 6, 4 | Poster | Poster | ✔ |
| 239 | Open Rule Induction | 5.75 | 0.83 | 7, 5, 5, 6 | Poster | Reject | ✔ |
| 240 | Open Rule Induction | 6.25 | 0.43 | 6, 6, 7, 6 | Poster | Poster | ✔ |
| 241 | Online Knapsack with Frequency Predictions | 6.75 | 0.83 | 7, 6, 8, 6 | Poster | Spotlight | ✔ |
| 242 | Online Knapsack with Frequency Predictions | 5.00 | 1.41 | 4, 4, 7 | Poster | Reject | ✔ |
| 243 | Online Adaptation to Label Distribution Shift | 6.00 | 0.71 | 5, 6, 6, 7 | Poster | Poster | ✔ |
| 244 | Online Adaptation to Label Distribution Shift | 6.25 | 0.43 | 6, 6, 6, 7 | Poster | Poster | ✔ |
| 245 | One More Step Towards Reality: Cooperative Bandits with Imperfect Communication | 5.33 | 0.47 | 6, 5, 5 | Poster | Reject | ✔ |
| 246 | One More Step Towards Reality: Cooperative Bandits with Imperfect Communication | 6.33 | 0.94 | 5, 7, 7 | Poster | Poster | ✔ |
| 247 | One Explanation is Not Enough: Structured Attention Graphs for Image Classification | 4.50 | 0.50 | 4, 5, 5, 4 | Poster | Reject | ✔ |
| 248 | One Explanation is Not Enough: Structured Attention Graphs for Image Classification | 5.75 | 1.09 | 6, 7, 6, 4 | Poster | Poster | ✔ |
| 249 | On the Theory of Reinforcement Learning with Once-per-Episode Feedback | 5.50 | 1.12 | 4, 7, 6, 5 | Poster | Poster | ✔ |
| 250 | On the Theory of Reinforcement Learning with Once-per-Episode Feedback | 5.00 | 0.71 | 4, 5, 5, 6 | Poster | Reject | ✔ |
| 251 | On the Role of Optimization in Double Descent: A Least Squares Study | 4.33 | 0.47 | 5, 4, 4 | Poster | Reject | ✔ |
| 252 | On the Role of Optimization in Double Descent: A Least Squares Study | 7.33 | 0.47 | 7, 7, 8 | Poster | Poster | ✔ |
| 253 | On learning sparse vectors from mixture of responses | 4.60 | 0.49 | 5, 5, 4, 4, 5 | Poster | Reject | ✔ |
| 254 | On learning sparse vectors from mixture of responses | 6.00 | 1.41 | 8, 7, 5, 6, 4 | Poster | Poster | ✔ |
| 255 | On Success and Simplicity: A Second Look at Transferable Targeted Attacks | 6.00 | 1.22 | 7, 6, 4, 7 | Poster | Reject | ✔ |
| 256 | On Success and Simplicity: A Second Look at Transferable Targeted Attacks | 5.75 | 1.09 | 6, 4, 7, 6 | Poster | Poster | ✔ |
| 257 | On Locality of Local Explanation Models | 7.00 | 0.00 | 7, 7, 7, 7 | Poster | Poster | ✔ |
| 258 | On Locality of Local Explanation Models | 5.50 | 1.50 | 7, 6, 6, 3 | Poster | Reject | ✔ |
| 259 | On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources | 5.75 | 1.09 | 7, 6, 4, 6 | Poster | Reject | ✔ |
| 260 | On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources | 6.00 | 0.71 | 5, 6, 6, 7 | Poster | Poster | ✔ |
| 261 | Non-asymptotic convergence bounds for Wasserstein approximation using point clouds | 7.50 | 1.12 | 7, 6, 8, 9 | Poster | Spotlight | ✔ |
| 262 | Non-asymptotic convergence bounds for Wasserstein approximation using point clouds | 5.75 | 0.83 | 5, 5, 7, 6 | Poster | Reject | ✔ |
| 263 | Neural Tangent Kernel Maximum Mean Discrepancy | 4.50 | 1.50 | 7, 4, 4, 3 | Poster | Reject | ✔ |
| 264 | Neural Tangent Kernel Maximum Mean Discrepancy | 6.50 | 0.50 | 7, 6, 6, 7 | Poster | Poster | ✔ |
| 265 | Neural Routing by Memory | 7.00 | 0.71 | 8, 6, 7, 7 | Poster | Poster | ✔ |
| 266 | Neural Routing by Memory | 4.25 | 1.09 | 4, 3, 6, 4 | Poster | Reject | ✔ |
| 267 | Neural Architecture Dilation for Adversarial Robustness | 6.00 | 1.41 | 8, 6, 4, 6 | Poster | Reject | ✔ |
| 268 | Neural Architecture Dilation for Adversarial Robustness | 6.00 | 0.71 | 6, 5, 7, 6 | Poster | Poster | ✔ |
| 269 | Nested Variational Inference | 6.50 | 0.50 | 7, 7, 6, 6 | Poster | Poster | ✔ |
| 270 | Nested Variational Inference | 5.33 | 0.47 | 6, 5, 5 | Poster | Reject | ✔ |
| 271 | Nearly-Tight and Oblivious Algorithms for Explainable Clustering | 7.00 | 0.71 | 8, 7, 6, 7 | Poster | Spotlight | ✔ |
| 272 | Nearly-Tight and Oblivious Algorithms for Explainable Clustering | 7.00 | 0.71 | 7, 6, 7, 8 | Poster | Poster | ✔ |
| 273 | NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform | 7.00 | 1.22 | 6, 9, 6, 7 | Poster | Poster | ✔ |
| 274 | NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform | 6.50 | 0.50 | 7, 6, 7, 6 | Poster | Poster | ✔ |
| 275 | Multilingual Pre-training with Universal Dependency Learning | 6.00 | 0.00 | 6, 6, 6, 6 | Poster | Poster | ✔ |
| 276 | Multilingual Pre-training with Universal Dependency Learning | 5.50 | 1.12 | 6, 7, 5, 4 | Poster | Reject | ✔ |
| 277 | Multiclass Boosting and the Cost of Weak Learning | 6.50 | 0.87 | 7, 7, 5, 7 | Poster | Poster | ✔ |
| 278 | Multiclass Boosting and the Cost of Weak Learning | 6.33 | 0.47 | 6, 7, 6 | Poster | Poster | ✔ |
| 279 | Multi-Person 3D Motion Prediction with Multi-Range Transformers | 6.00 | 1.41 | 6, 3, 7, 7, 7, 6 | Poster | Poster | ✔ |
| 280 | Multi-Person 3D Motion Prediction with Multi-Range Transformers | 5.25 | 0.83 | 6, 5, 4, 6 | Poster | Reject | ✔ |
| 281 | Multi-Objective Meta Learning | 4.00 | 0.71 | 3, 4, 4, 5 | Poster | Reject | ✔ |
| 282 | Multi-Objective Meta Learning | 6.25 | 1.30 | 5, 8, 5, 7 | Poster | Poster | ✔ |
| 283 | Model-Based Episodic Memory Induces Dynamic Hybrid Controls | 5.75 | 0.43 | 6, 5, 6, 6 | Poster | Poster | ✔ |
| 284 | Model-Based Episodic Memory Induces Dynamic Hybrid Controls | 6.50 | 0.50 | 7, 6, 6, 7 | Poster | Poster | ✔ |
| 285 | Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization | 4.75 | 1.30 | 6, 4, 6, 3 | Poster | Reject | ✔ |
| 286 | Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization | 6.67 | 0.94 | 8, 6, 6 | Poster | Poster | ✔ |
| 287 | Meta-Adaptive Nonlinear Control: Theory and Algorithms | 6.75 | 0.43 | 7, 6, 7, 7 | Poster | Poster | ✔ |
| 288 | Meta-Adaptive Nonlinear Control: Theory and Algorithms | 5.00 | 1.00 | 4, 6, 6, 4 | Poster | Reject | ✔ |
| 289 | Machine versus Human Attention in Deep Reinforcement Learning Tasks | 6.50 | 0.87 | 5, 7, 7, 7 | Poster | Poster | ✔ |
| 290 | Machine versus Human Attention in Deep Reinforcement Learning Tasks | 4.75 | 1.48 | 7, 5, 3, 4 | Poster | Reject | ✔ |
| 291 | Machine learning structure preserving brackets for forecasting irreversible processes | 4.75 | 1.09 | 5, 5, 6, 3 | Poster | Reject | ✔ |
| 292 | Machine learning structure preserving brackets for forecasting irreversible processes | 7.00 | 0.82 | 8, 6, 7 | Poster | Spotlight | ✔ |
| 293 | Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems | 5.25 | 0.43 | 5, 5, 6, 5 | Poster | Reject | ✔ |
| 294 | Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems | 7.00 | 1.41 | 9, 6, 6 | Poster | Poster | ✔ |
| 295 | Low-Rank Constraints for Fast Inference in Structured Models | 6.00 | 0.71 | 5, 7, 6, 6 | Poster | Poster | ✔ |
| 296 | Low-Rank Constraints for Fast Inference in Structured Models | 5.50 | 1.12 | 5, 6, 4, 7 | Poster | Reject | ✔ |
| 297 | Low-Fidelity Video Encoder Optimization for Temporal Action Localization | 5.33 | 0.47 | 5, 6, 5 | Poster | Reject | ✔ |
| 298 | Low-Fidelity Video Encoder Optimization for Temporal Action Localization | 6.75 | 0.83 | 6, 7, 8, 6 | Poster | Poster | ✔ |
| 299 | Locally private online change point detection | 6.00 | 0.00 | 6, 6, 6 | Poster | Poster | ✔ |
| 300 | Locally private online change point detection | 5.25 | 1.48 | 3, 7, 5, 6 | Poster | Reject | ✔ |
| 301 | Locally differentially private estimation of functionals of discrete distributions | 6.00 | 0.71 | 6, 6, 7, 5 | Poster | Poster | ✔ |
| 302 | Locally differentially private estimation of functionals of discrete distributions | 5.60 | 0.80 | 6, 7, 5, 5, 5 | Poster | Reject | ✔ |
| 303 | Localization with Sampling-Argmax | 7.50 | 0.87 | 7, 7, 7, 9 | Poster | Poster | ✔ |
| 304 | Localization with Sampling-Argmax | 5.25 | 1.09 | 5, 7, 5, 4 | Poster | Reject | ✔ |
| 305 | Linear-Time Probabilistic Solution of Boundary Value Problems | 6.50 | 0.87 | 6, 6, 6, 8 | Poster | Poster | ✔ |
| 306 | Linear-Time Probabilistic Solution of Boundary Value Problems | 4.00 | 0.00 | 4, 4, 4 | Poster | Reject | ✔ |
| 307 | Limiting fluctuation and trajectorial stability of multilayer neural networks with mean field training | 5.75 | 0.43 | 6, 5, 6, 6 | Poster | Poster | ✔ |
| 308 | Limiting fluctuation and trajectorial stability of multilayer neural networks with mean field training | 6.00 | 0.00 | 6, 6, 6 | Poster | Poster | ✔ |
| 309 | Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces | 6.25 | 0.83 | 7, 5, 7, 6 | Poster | Poster | ✔ |
| 310 | Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces | 6.50 | 0.50 | 6, 7, 7, 6 | Poster | Poster | ✔ |
| 311 | Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds | 6.50 | 0.50 | 7, 7, 6, 6 | Poster | Poster | ✔ |
| 312 | Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds | 5.75 | 0.43 | 6, 6, 5, 6 | Poster | Poster | ✔ |
| 313 | Learning where to learn: Gradient sparsity in meta and continual learning | 6.25 | 0.83 | 5, 7, 6, 7 | Poster | Poster | ✔ |
| 314 | Learning where to learn: Gradient sparsity in meta and continual learning | 5.75 | 0.43 | 6, 5, 6, 6 | Poster | Poster | ✔ |
| 315 | Learning to Schedule Heuristics in Branch and Bound | 6.00 | 1.22 | 4, 7, 6, 7 | Poster | Poster | ✔ |
| 316 | Learning to Schedule Heuristics in Branch and Bound | 6.75 | 0.43 | 6, 7, 7, 7 | Poster | Poster | ✔ |
| 317 | Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer | 6.00 | 0.71 | 7, 5, 6, 6 | Poster | Poster | ✔ |
| 318 | Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer | 5.75 | 1.30 | 7, 7, 4, 5 | Poster | Poster | ✔ |
| 319 | Learning to Generate Visual Questions with Noisy Supervision | 6.75 | 0.43 | 7, 7, 6, 7 | Poster | Spotlight | ✔ |
| 320 | Learning to Generate Visual Questions with Noisy Supervision | 4.75 | 0.43 | 5, 5, 4, 5 | Poster | Reject | ✔ |
| 321 | Learning from Inside: Self-driven Siamese Sampling and Reasoning for Video Question Answering | 5.25 | 0.43 | 5, 5, 6, 5 | Poster | Reject | ✔ |
| 322 | Learning from Inside: Self-driven Siamese Sampling and Reasoning for Video Question Answering | 5.75 | 0.83 | 6, 5, 7, 5 | Poster | Poster | ✔ |
| 323 | Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning | 5.50 | 0.87 | 6, 6, 6, 4 | Poster | Reject | ✔ |
| 324 | Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning | 6.00 | 0.82 | 6, 5, 7 | Poster | Poster | ✔ |
| 325 | Learning Student-Friendly Teacher Networks for Knowledge Distillation | 6.00 | 0.71 | 6, 5, 7, 6 | Poster | Poster | ✔ |
| 326 | Learning Student-Friendly Teacher Networks for Knowledge Distillation | 4.50 | 0.50 | 4, 4, 5, 5 | Poster | Reject | ✔ |
| 327 | Learning Riemannian metric for disease progression modeling | 6.50 | 0.50 | 6, 6, 7, 7 | Poster | Poster | ✔ |
| 328 | Learning Riemannian metric for disease progression modeling | 6.00 | 1.00 | 5, 7, 7, 5 | Poster | Reject | ✔ |
| 329 | Learning Graph Models for Retrosynthesis Prediction | 4.33 | 0.47 | 4, 5, 4 | Poster | Reject | ✔ |
| 330 | Learning Graph Models for Retrosynthesis Prediction | 5.75 | 1.48 | 6, 8, 5, 4 | Poster | Poster | ✔ |
| 331 | Learnable Fourier Features for Multi-dimensional Spatial Positional Encoding | 5.75 | 0.43 | 6, 6, 5, 6 | Poster | Poster | ✔ |
| 332 | Learnable Fourier Features for Multi-dimensional Spatial Positional Encoding | 6.83 | 1.34 | 8, 7, 9, 6, 5, 6 | Poster | Poster | ✔ |
| 333 | Lattice partition recovery with dyadic CART | 6.33 | 0.47 | 7, 6, 6 | Poster | Poster | ✔ |
| 334 | Lattice partition recovery with dyadic CART | 6.00 | 0.00 | 6, 6, 6 | Poster | Poster | ✔ |
| 335 | Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models | 5.50 | 0.50 | 5, 6, 5, 6 | Poster | Poster | ✔ |
| 336 | Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models | 4.25 | 1.92 | 2, 5, 3, 7 | Poster | Reject | ✔ |
| 337 | Integrating Tree Path in Transformer for Code Representation | 5.00 | 1.41 | 4, 4, 7 | Poster | Reject | ✔ |
| 338 | Integrating Tree Path in Transformer for Code Representation | 5.75 | 1.09 | 6, 7, 6, 4 | Poster | Poster | ✔ |
| 339 | Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression | 5.25 | 1.64 | 4, 4, 5, 8 | Poster | Reject | ✔ |
| 340 | Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression | 5.75 | 1.09 | 6, 7, 6, 4 | Poster | Poster | ✔ |
| 341 | Increasing Liquid State Machine Performance with Edge-of-Chaos Dynamics Organized by Astrocyte-modulated Plasticity | 6.00 | 1.22 | 4, 6, 7, 7 | Poster | Reject | ✔ |
| 342 | Increasing Liquid State Machine Performance with Edge-of-Chaos Dynamics Organized by Astrocyte-modulated Plasticity | 6.33 | 0.94 | 7, 5, 7 | Poster | Poster | ✔ |
| 343 | Improving Compositionality of Neural Networks by Decoding Representations to Inputs | 6.00 | 1.58 | 8, 7, 4, 5 | Poster | Poster | ✔ |
| 344 | Improving Compositionality of Neural Networks by Decoding Representations to Inputs | 7.33 | 0.47 | 7, 8, 7 | Poster | Reject | ✔ |
| 345 | Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning | 7.00 | 1.22 | 7, 6, 9, 6 | Poster | Spotlight | ✔ |
| 346 | Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning | 4.25 | 0.83 | 5, 3, 4, 5 | Poster | Reject | ✔ |
| 347 | Improved Regularization and Robustness for Fine-tuning in Neural Networks | 4.50 | 0.50 | 5, 4, 4, 5 | Poster | Reject | ✔ |
| 348 | Improved Regularization and Robustness for Fine-tuning in Neural Networks | 6.25 | 0.43 | 6, 7, 6, 6 | Poster | Poster | ✔ |
| 349 | Improved Regret Bounds for Tracking Experts with Memory | 6.67 | 0.47 | 7, 7, 6 | Poster | Poster | ✔ |
| 350 | Improved Regret Bounds for Tracking Experts with Memory | 6.40 | 0.49 | 7, 7, 6, 6, 6 | Poster | Poster | ✔ |
| 351 | Hyperbolic Procrustes Analysis Using Riemannian Geometry | 6.25 | 0.43 | 6, 6, 6, 7 | Poster | Poster | ✔ |
| 352 | Hyperbolic Procrustes Analysis Using Riemannian Geometry | 6.00 | 0.71 | 7, 6, 6, 5 | Poster | Poster | ✔ |
| 353 | How does a Neural Network's Architecture Impact its Robustness to Noisy Labels? | 5.33 | 1.70 | 6, 7, 3 | Poster | Reject | ✔ |
| 354 | How does a Neural Network's Architecture Impact its Robustness to Noisy Labels? | 6.00 | 1.10 | 6, 5, 6, 8, 5 | Poster | Poster | ✔ |
| 355 | How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness? | 6.33 | 0.47 | 7, 6, 6 | Poster | Poster | ✔ |
| 356 | How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness? | 6.25 | 0.83 | 7, 6, 5, 7 | Poster | Poster | ✔ |
| 357 | How Fine-Tuning Allows for Effective Meta-Learning | 6.33 | 0.47 | 7, 6, 6 | Poster | Poster | ✔ |
| 358 | How Fine-Tuning Allows for Effective Meta-Learning | 6.00 | 0.71 | 6, 5, 6, 7 | Poster | Poster | ✔ |
| 359 | Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation | 6.00 | 0.71 | 6, 6, 5, 7 | Poster | Poster | ✔ |
| 360 | Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation | 4.40 | 0.49 | 5, 4, 5, 4, 4 | Poster | Reject | ✔ |
| 361 | Handling Long-tailed Feature Distribution in AdderNets | 5.00 | 0.71 | 5, 5, 6, 4 | Poster | Reject | ✔ |
| 362 | Handling Long-tailed Feature Distribution in AdderNets | 6.25 | 0.43 | 7, 6, 6, 6 | Poster | Poster | ✔ |
| 363 | Grounding Spatio-Temporal Language with Transformers | 5.50 | 1.12 | 4, 6, 7, 5 | Poster | Reject | ✔ |
| 364 | Grounding Spatio-Temporal Language with Transformers | 5.75 | 1.09 | 4, 7, 6, 6 | Poster | Poster | ✔ |
| 365 | Grammar-Based Grounded Lexicon Learning | 5.75 | 1.64 | 7, 3, 7, 6 | Poster | Poster | ✔ |
| 366 | Grammar-Based Grounded Lexicon Learning | 5.75 | 0.83 | 7, 5, 6, 5 | Poster | Poster | ✔ |
| 367 | Gradient-based Hyperparameter Optimization Over Long Horizons | 6.33 | 0.47 | 6, 6, 7 | Poster | Poster | ✔ |
| 368 | Gradient-based Hyperparameter Optimization Over Long Horizons | 6.00 | 0.00 | 6, 6, 6, 6 | Poster | Poster | ✔ |
| 369 | Gradient Starvation: A Learning Proclivity in Neural Networks | 6.50 | 0.50 | 7, 6, 6, 7 | Poster | Poster | ✔ |
| 370 | Gradient Starvation: A Learning Proclivity in Neural Networks | 5.20 | 1.60 | 4, 5, 7, 7, 3 | Poster | Reject | ✔ |
| 371 | GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training | 6.50 | 0.50 | 7, 6, 6, 7 | Poster | Poster | ✔ |
| 372 | GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training | 6.25 | 0.43 | 6, 6, 7, 6 | Poster | Poster | ✔ |
| 373 | Generalized Linear Bandits with Local Differential Privacy | 6.75 | 0.43 | 7, 6, 7, 7 | Poster | Poster | ✔ |
| 374 | Generalized Linear Bandits with Local Differential Privacy | 6.33 | 0.47 | 6, 7, 6 | Poster | Poster | ✔ |
| 375 | Generalized DataWeighting via Class-Level Gradient Manipulation | 5.00 | 0.00 | 5, 5, 5, 5 | Poster | Reject | ✔ |
| 376 | Generalized DataWeighting via Class-Level Gradient Manipulation | 6.00 | 1.58 | 4, 7, 8, 5 | Poster | Poster | ✔ |
| 377 | Gaussian Kernel Mixture Network for Single Image Defocus Deblurring | 5.25 | 1.48 | 3, 6, 7, 5 | Poster | Poster | ✔ |
| 378 | Gaussian Kernel Mixture Network for Single Image Defocus Deblurring | 5.67 | 1.25 | 7, 4, 6 | Poster | Reject | ✔ |
| 379 | GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement | 5.50 | 1.12 | 7, 6, 5, 4 | Poster | Reject | ✔ |
| 380 | GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement | 6.25 | 0.43 | 7, 6, 6, 6 | Poster | Poster | ✔ |
| 381 | Finite Sample Analysis of Average-Reward TD Learning and
Q
-Learning | 6.25 | 0.43 | 6, 6, 6, 7 | Poster | Poster | ✔ |
| 382 | Finite Sample Analysis of Average-Reward TD Learning and
Q
-Learning | 5.67 | 0.94 | 5, 5, 7 | Poster | Reject | ✔ |
| 383 | Faster Non-asymptotic Convergence for Double Q-learning | 6.00 | 0.00 | 6, 6, 6, 6 | Poster | Poster | ✔ |
| 384 | Faster Non-asymptotic Convergence for Double Q-learning | 5.50 | 1.12 | 6, 7, 5, 4 | Poster | Reject | ✔ |
| 385 | Fast Pure Exploration via Frank-Wolfe | 6.50 | 0.50 | 7, 7, 6, 6 | Poster | Poster | ✔ |
| 386 | Fast Pure Exploration via Frank-Wolfe | 7.33 | 0.47 | 7, 8, 7 | Poster | Poster | ✔ |
| 387 | Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization | 6.25 | 0.83 | 6, 5, 7, 7 | Poster | Poster | ✔ |
| 388 | Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization | 5.50 | 1.12 | 7, 4, 5, 6 | Poster | Reject | ✔ |
| 389 | Fast Extra Gradient Methods for Smooth Structured Nonconvex-Nonconcave Minimax Problems | 6.75 | 0.83 | 7, 6, 6, 8 | Poster | Poster | ✔ |
| 390 | Fast Extra Gradient Methods for Smooth Structured Nonconvex-Nonconcave Minimax Problems | 6.50 | 0.87 | 7, 7, 5, 7 | Poster | Poster | ✔ |
| 391 | Fast Certified Robust Training with Short Warmup | 5.67 | 0.47 | 6, 5, 6 | Poster | Poster | ✔ |
| 392 | Fast Certified Robust Training with Short Warmup | 6.50 | 0.50 | 7, 6, 7, 6 | Poster | Poster | ✔ |
| 393 | Fast Algorithms for
L∞
-constrained S-rectangular Robust MDPs | 6.50 | 1.12 | 6, 7, 5, 8 | Poster | Poster | ✔ |
| 394 | Fast Algorithms for
L∞
-constrained S-rectangular Robust MDPs | 6.00 | 0.71 | 5, 7, 6, 6 | Poster | Poster | ✔ |
| 395 | Fairness via Representation Neutralization | 6.75 | 0.43 | 7, 7, 7, 6 | Poster | Poster | ✔ |
| 396 | Fairness via Representation Neutralization | 6.00 | 0.71 | 7, 5, 6, 6 | Poster | Reject | ✔ |
| 397 | FACMAC: Factored Multi-Agent Centralised Policy Gradients | 6.00 | 1.10 | 7, 4, 7, 6, 6 | Poster | Poster | ✔ |
| 398 | FACMAC: Factored Multi-Agent Centralised Policy Gradients | 5.00 | 2.83 | 7, 1, 7 | Poster | Reject | ✔ |
| 399 | Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models | 4.80 | 0.98 | 4, 4, 6, 4, 6 | Poster | Reject | ✔ |
| 400 | Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models | 7.00 | 0.63 | 7, 8, 7, 6, 7 | Poster | Poster | ✔ |
| 401 | Exponential Separation between Two Learning Models and Adversarial Robustness | 5.33 | 0.47 | 5, 6, 5 | Poster | Reject | ✔ |
| 402 | Exponential Separation between Two Learning Models and Adversarial Robustness | 7.00 | 1.41 | 6, 9, 6 | Poster | Poster | ✔ |
| 403 | Exploring Cross-Video and Cross-Modality Signals for Weakly-Supervised Audio-Visual Video Parsing | 6.25 | 0.83 | 7, 5, 7, 6 | Poster | Poster | ✔ |
| 404 | Exploring Cross-Video and Cross-Modality Signals for Weakly-Supervised Audio-Visual Video Parsing | 4.75 | 0.83 | 6, 5, 4, 4 | Poster | Reject | ✔ |
| 405 | Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation | 6.00 | 1.22 | 7, 6, 4, 7 | Poster | Poster | ✔ |
| 406 | Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation | 5.50 | 0.87 | 6, 4, 6, 6 | Poster | Poster | ✔ |
| 407 | Exploiting Domain-Specific Features to Enhance Domain Generalization | 6.00 | 1.22 | 4, 7, 7, 6 | Poster | Poster | ✔ |
| 408 | Exploiting Domain-Specific Features to Enhance Domain Generalization | 5.50 | 1.12 | 5, 7, 6, 4 | Poster | Reject | ✔ |
| 409 | Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots | 6.00 | 1.58 | 5, 8, 4, 7 | Poster | Poster | ✔ |
| 410 | Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots | 6.33 | 1.25 | 6, 8, 5 | Poster | Reject | ✔ |
| 411 | Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi | 5.25 | 0.43 | 5, 5, 6, 5 | Poster | Reject | ✔ |
| 412 | Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi | 6.50 | 0.50 | 7, 6, 6, 7 | Poster | Poster | ✔ |
| 413 | Evaluating State-of-the-Art Classification Models Against Bayes Optimality | 5.50 | 0.50 | 5, 6, 6, 5 | Poster | Reject | ✔ |
| 414 | Evaluating State-of-the-Art Classification Models Against Bayes Optimality | 6.50 | 0.87 | 7, 7, 5, 7 | Poster | Poster | ✔ |
| 415 | ErrorCompensatedX: error compensation for variance reduced algorithms | 6.50 | 0.50 | 7, 7, 6, 6 | Poster | Poster | ✔ |
| 416 | ErrorCompensatedX: error compensation for variance reduced algorithms | 5.25 | 0.43 | 5, 6, 5, 5 | Poster | Reject | ✔ |
| 417 | Episodic Multi-agent Reinforcement Learning with Curiosity-driven Exploration | 6.25 | 0.83 | 7, 5, 6, 7 | Poster | Poster | ✔ |
| 418 | Episodic Multi-agent Reinforcement Learning with Curiosity-driven Exploration | 6.00 | 0.71 | 5, 6, 6, 7 | Poster | Poster | ✔ |
| 419 | Entropic Desired Dynamics for Intrinsic Control | 5.25 | 0.43 | 5, 5, 6, 5 | Poster | Reject | ✔ |
| 420 | Entropic Desired Dynamics for Intrinsic Control | 6.60 | 0.49 | 7, 6, 6, 7, 7 | Poster | Poster | ✔ |
| 421 | Emergent Discrete Communication in Semantic Spaces | 7.50 | 0.50 | 8, 7, 8, 7 | Poster | Poster | ✔ |
| 422 | Emergent Discrete Communication in Semantic Spaces | 4.75 | 0.83 | 4, 6, 4, 5 | Poster | Reject | ✔ |
| 423 | Efficient Training of Visual Transformers with Small Datasets | 5.75 | 0.43 | 6, 5, 6, 6 | Poster | Spotlight | ✔ |
| 424 | Efficient Training of Visual Transformers with Small Datasets | 5.40 | 1.02 | 5, 4, 7, 6, 5 | Poster | Reject | ✔ |
| 425 | Efficient Statistical Assessment of Neural Network Corruption Robustness | 4.33 | 0.94 | 5, 3, 5 | Poster | Reject | ✔ |
| 426 | Efficient Statistical Assessment of Neural Network Corruption Robustness | 6.33 | 0.94 | 5, 7, 7 | Poster | Poster | ✔ |
| 427 | Efficient Learning of Discrete-Continuous Computation Graphs | 5.25 | 1.09 | 4, 5, 7, 5 | Poster | Reject | ✔ |
| 428 | Efficient Learning of Discrete-Continuous Computation Graphs | 5.75 | 1.09 | 6, 4, 7, 6 | Poster | Poster | ✔ |
| 429 | Efficient Bayesian network structure learning via local Markov boundary search | 5.80 | 0.75 | 5, 7, 5, 6, 6 | Poster | Reject | ✔ |
| 430 | Efficient Bayesian network structure learning via local Markov boundary search | 6.00 | 0.71 | 7, 6, 5, 6 | Poster | Poster | ✔ |
| 431 | Efficient Active Learning for Gaussian Process Classification by Error Reduction | 5.00 | 0.00 | 5, 5, 5 | Poster | Reject | ✔ |
| 432 | Efficient Active Learning for Gaussian Process Classification by Error Reduction | 6.00 | 1.22 | 6, 5, 5, 8 | Poster | Poster | ✔ |
| 433 | EditGAN: High-Precision Semantic Image Editing | 4.25 | 0.83 | 5, 5, 3, 4 | Poster | Reject | ✔ |
| 434 | EditGAN: High-Precision Semantic Image Editing | 7.33 | 0.94 | 8, 6, 8 | Poster | Poster | ✔ |
| 435 | Edge Representation Learning with Hypergraphs | 6.00 | 0.71 | 7, 6, 5, 6 | Poster | Poster | ✔ |
| 436 | Edge Representation Learning with Hypergraphs | 4.00 | 0.00 | 4, 4, 4, 4 | Poster | Reject | ✔ |
| 437 | DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification | 6.00 | 0.71 | 6, 7, 5, 6 | Poster | Poster | ✔ |
| 438 | DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification | 6.33 | 0.47 | 7, 6, 6, 6, 7, 6 | Poster | Poster | ✔ |
| 439 | Dual Progressive Prototype Network for Generalized Zero-Shot Learning | 5.50 | 0.50 | 6, 6, 5, 5 | Poster | Reject | ✔ |
| 440 | Dual Progressive Prototype Network for Generalized Zero-Shot Learning | 6.00 | 0.00 | 6, 6, 6, 6 | Poster | Poster | ✔ |
| 441 | Does Knowledge Distillation Really Work? | 6.25 | 0.83 | 6, 7, 7, 5 | Poster | Poster | ✔ |
| 442 | Does Knowledge Distillation Really Work? | 6.40 | 0.80 | 5, 7, 7, 7, 6 | Poster | Poster | ✔ |
| 443 | Do Vision Transformers See Like Convolutional Neural Networks? | 5.25 | 0.83 | 5, 6, 6, 4 | Poster | Reject | ✔ |
| 444 | Do Vision Transformers See Like Convolutional Neural Networks? | 6.50 | 0.50 | 6, 7, 6, 7 | Poster | Spotlight | ✔ |
| 445 | Distributionally Robust Imitation Learning | 5.00 | 0.71 | 6, 5, 5, 4 | Poster | Reject | ✔ |
| 446 | Distributionally Robust Imitation Learning | 6.00 | 0.71 | 5, 6, 7, 6 | Poster | Poster | ✔ |
| 447 | Distributional Reinforcement Learning for Multi-Dimensional Reward Functions | 4.75 | 1.09 | 5, 3, 6, 5 | Poster | Reject | ✔ |
| 448 | Distributional Reinforcement Learning for Multi-Dimensional Reward Functions | 6.25 | 0.43 | 6, 6, 7, 6 | Poster | Poster | ✔ |
| 449 | Distribution-free inference for regression: discrete, continuous, and in between | 6.00 | 1.58 | 8, 4, 7, 5 | Poster | Reject | ✔ |
| 450 | Distribution-free inference for regression: discrete, continuous, and in between | 6.80 | 0.98 | 7, 7, 7, 5, 8 | Poster | Poster | ✔ |
| 451 | Distributed Saddle-Point Problems Under Data Similarity | 5.75 | 0.43 | 6, 5, 6, 6 | Poster | Reject | ✔ |
| 452 | Distributed Saddle-Point Problems Under Data Similarity | 6.00 | 0.71 | 6, 5, 6, 7 | Poster | Poster | ✔ |
| 453 | Disrupting Deep Uncertainty Estimation Without Harming Accuracy | 6.67 | 0.47 | 7, 6, 7 | Poster | Spotlight | ✔ |
| 454 | Disrupting Deep Uncertainty Estimation Without Harming Accuracy | 5.67 | 0.94 | 7, 5, 5 | Poster | Reject | ✔ |
| 455 | Disentangled Contrastive Learning on Graphs | 6.33 | 1.25 | 8, 6, 5 | Poster | Poster | ✔ |
| 456 | Disentangled Contrastive Learning on Graphs | 6.00 | 0.71 | 5, 6, 7, 6 | Poster | Poster | ✔ |
| 457 | Directed Graph Contrastive Learning | 6.25 | 0.43 | 6, 6, 6, 7 | Poster | Poster | ✔ |
| 458 | Directed Graph Contrastive Learning | 5.00 | 0.71 | 5, 4, 6, 5 | Poster | Reject | ✔ |
| 459 | Dimensionality Reduction for Wasserstein Barycenter | 5.33 | 1.70 | 3, 6, 7 | Poster | Reject | ✔ |
| 460 | Dimensionality Reduction for Wasserstein Barycenter | 6.75 | 0.43 | 6, 7, 7, 7 | Poster | Poster | ✔ |
| 461 | Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings | 6.25 | 1.48 | 6, 8, 4, 7 | Poster | Poster | ✔ |
| 462 | Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings | 6.67 | 0.47 | 7, 6, 7 | Poster | Spotlight | ✔ |
| 463 | Differentially Private Multi-Armed Bandits in the Shuffle Model | 6.00 | 0.00 | 6, 6, 6, 6 | Poster | Poster | ✔ |
| 464 | Differentially Private Multi-Armed Bandits in the Shuffle Model | 6.75 | 0.43 | 7, 7, 7, 6 | Poster | Poster | ✔ |
| 465 | Differentially Private Federated Bayesian Optimization with Distributed Exploration | 6.75 | 0.43 | 6, 7, 7, 7 | Poster | Poster | ✔ |
| 466 | Differentially Private Federated Bayesian Optimization with Distributed Exploration | 5.50 | 0.50 | 5, 5, 6, 6 | Poster | Reject | ✔ |
| 467 | Deformable Butterfly: A Highly Structured and Sparse Linear Transform | 6.25 | 0.83 | 7, 5, 6, 7 | Poster | Poster | ✔ |
| 468 | Deformable Butterfly: A Highly Structured and Sparse Linear Transform | 5.75 | 1.09 | 6, 6, 7, 4 | Poster | Poster | ✔ |
| 469 | Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time | 7.00 | 0.82 | 6, 8, 7 | Poster | Poster | ✔ |
| 470 | Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time | 4.50 | 0.50 | 5, 4, 4, 5 | Poster | Reject | ✔ |
| 471 | Deep Networks Provably Classify Data on Curves | 6.50 | 1.66 | 8, 6, 4, 8 | Poster | Poster | ✔ |
| 472 | Deep Networks Provably Classify Data on Curves | 6.50 | 0.50 | 6, 7, 6, 7 | Poster | Poster | ✔ |
| 473 | Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis | 6.00 | 0.71 | 7, 5, 6, 6 | Poster | Poster | ✔ |
| 474 | Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis | 6.00 | 1.41 | 6, 6, 4, 8 | Poster | Poster | ✔ |
| 475 | Deep Learning Through the Lens of Example Difficulty | 6.00 | 1.22 | 6, 8, 5, 5 | Poster | Reject | ✔ |
| 476 | Deep Learning Through the Lens of Example Difficulty | 6.00 | 0.82 | 7, 5, 6 | Poster | Poster | ✔ |
| 477 | Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings | 6.75 | 0.83 | 6, 8, 6, 7 | Poster | Poster | ✔ |
| 478 | Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings | 6.50 | 0.50 | 6, 7, 6, 7 | Poster | Poster | ✔ |
| 479 | Deep Conditional Gaussian Mixture Model for Constrained Clustering | 5.75 | 1.30 | 7, 4, 7, 5 | Poster | Poster | ✔ |
| 480 | Deep Conditional Gaussian Mixture Model for Constrained Clustering | 5.75 | 1.09 | 7, 4, 6, 6 | Poster | Reject | ✔ |
| 481 | Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks | 6.67 | 0.47 | 7, 6, 7 | Poster | Poster | ✔ |
| 482 | Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks | 5.75 | 0.83 | 7, 5, 5, 6 | Poster | Reject | ✔ |
| 483 | Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLP | 6.75 | 1.09 | 7, 8, 5, 7 | Poster | Poster | ✔ |
| 484 | Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLP | 7.25 | 0.83 | 7, 8, 8, 6 | Poster | Poster | ✔ |
| 485 | Decoupling the Depth and Scope of Graph Neural Networks | 5.50 | 1.12 | 6, 5, 7, 4 | Poster | Reject | ✔ |
| 486 | Decoupling the Depth and Scope of Graph Neural Networks | 6.67 | 1.70 | 5, 9, 6 | Poster | Poster | ✔ |
| 487 | Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification | 5.33 | 0.94 | 6, 6, 4 | Poster | Reject | ✔ |
| 488 | Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification | 6.67 | 0.47 | 7, 7, 6 | Poster | Poster | ✔ |
| 489 | Dataset Distillation with Infinitely Wide Convolutional Networks | 5.50 | 0.50 | 6, 5, 6, 5 | Poster | Poster | ✔ |
| 490 | Dataset Distillation with Infinitely Wide Convolutional Networks | 5.67 | 0.47 | 5, 6, 6 | Poster | Reject | ✔ |
| 491 | Dangers of Bayesian Model Averaging under Covariate Shift | 6.75 | 0.43 | 6, 7, 7, 7 | Poster | Poster | ✔ |
| 492 | Dangers of Bayesian Model Averaging under Covariate Shift | 6.75 | 0.43 | 7, 7, 7, 6 | Poster | Poster | ✔ |
| 493 | DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples | 4.50 | 0.50 | 4, 4, 5, 5 | Poster | Reject | ✔ |
| 494 | DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples | 6.00 | 0.00 | 6, 6, 6, 6 | Poster | Poster | ✔ |
| 495 | DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer | 5.25 | 0.43 | 5, 5, 6, 5 | Poster | Reject | ✔ |
| 496 | DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer | 5.75 | 0.43 | 5, 6, 6, 6 | Poster | Poster | ✔ |
| 497 | Coupled Gradient Estimators for Discrete Latent Variables | 5.40 | 0.80 | 6, 5, 6, 4, 6 | Poster | Poster | ✔ |
| 498 | Coupled Gradient Estimators for Discrete Latent Variables | 6.50 | 0.50 | 7, 6, 7, 6 | Poster | Poster | ✔ |
| 499 | CorticalFlow: A Diffeomorphic Mesh Transformer Network for Cortical Surface Reconstruction | 4.25 | 0.83 | 5, 5, 3, 4 | Poster | Reject | ✔ |
| 500 | CorticalFlow: A Diffeomorphic Mesh Transformer Network for Cortical Surface Reconstruction | 6.75 | 0.43 | 6, 7, 7, 7 | Poster | Spotlight | ✔ |
| 501 | Continuous Mean-Covariance Bandits | 6.00 | 1.22 | 6, 4, 7, 7 | Poster | Reject | ✔ |
| 502 | Continuous Mean-Covariance Bandits | 6.67 | 0.47 | 7, 7, 6 | Poster | Poster | ✔ |
| 503 | Continuous Doubly Constrained Batch Reinforcement Learning | 5.50 | 0.50 | 6, 6, 5, 5 | Poster | Reject | ✔ |
| 504 | Continuous Doubly Constrained Batch Reinforcement Learning | 6.25 | 0.43 | 6, 6, 7, 6 | Poster | Poster | ✔ |
| 505 | Contextual Recommendations and Low-Regret Cutting-Plane Algorithms | 5.67 | 2.05 | 6, 3, 8 | Poster | Reject | ✔ |
| 506 | Contextual Recommendations and Low-Regret Cutting-Plane Algorithms | 7.00 | 0.00 | 7, 7, 7, 7 | Poster | Poster | ✔ |
| 507 | Constrained Two-step Look-Ahead Bayesian Optimization | 5.25 | 0.83 | 6, 4, 5, 6 | Poster | Reject | ✔ |
| 508 | Constrained Two-step Look-Ahead Bayesian Optimization | 6.25 | 0.83 | 5, 6, 7, 7 | Poster | Poster | ✔ |
| 509 | Conditional Generation Using Polynomial Expansions | 5.00 | 1.22 | 4, 5, 7, 4 | Poster | Reject | ✔ |
| 510 | Conditional Generation Using Polynomial Expansions | 7.00 | 0.71 | 6, 7, 8, 7 | Poster | Spotlight | ✔ |
| 511 | Compressed Video Contrastive Learning | 5.75 | 1.09 | 7, 6, 4, 6 | Poster | Poster | ✔ |
| 512 | Compressed Video Contrastive Learning | 4.50 | 0.50 | 5, 5, 4, 4 | Poster | Reject | ✔ |
| 513 | Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces | 5.25 | 1.30 | 6, 4, 7, 4 | Poster | Reject | ✔ |
| 514 | Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces | 6.00 | 0.71 | 6, 7, 5, 6 | Poster | Poster | ✔ |
| 515 | Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable Approach | 5.60 | 1.50 | 3, 6, 7, 7, 5 | Poster | Reject | ✔ |
| 516 | Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable Approach | 6.25 | 0.43 | 7, 6, 6, 6 | Poster | Poster | ✔ |
| 517 | CoAtNet: Marrying Convolution and Attention for All Data Sizes | 5.00 | 0.71 | 5, 6, 5, 4 | Poster | Reject | ✔ |
| 518 | CoAtNet: Marrying Convolution and Attention for All Data Sizes | 6.75 | 0.43 | 7, 7, 6, 7 | Poster | Poster | ✔ |
| 519 | Class-agnostic Reconstruction of Dynamic Objects from Videos | 3.75 | 0.43 | 4, 3, 4, 4 | Poster | Reject | ✔ |
| 520 | Class-agnostic Reconstruction of Dynamic Objects from Videos | 7.00 | 0.71 | 6, 7, 8, 7 | Poster | Poster | ✔ |
| 521 | Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote | 5.00 | 0.00 | 5, 5, 5, 5 | Poster | Reject | ✔ |
| 522 | Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote | 7.00 | 1.26 | 5, 7, 7, 7, 9 | Poster | Poster | ✔ |
| 523 | Charting and Navigating the Space of Solutions for Recurrent Neural Networks | 5.75 | 0.43 | 5, 6, 6, 6 | Poster | Poster | ✔ |
| 524 | Charting and Navigating the Space of Solutions for Recurrent Neural Networks | 4.33 | 0.47 | 5, 4, 4 | Poster | Reject | ✔ |
| 525 | CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum | 6.00 | 0.00 | 6, 6, 6, 6 | Poster | Poster | ✔ |
| 526 | CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum | 3.75 | 0.43 | 4, 4, 3, 4 | Poster | Reject | ✔ |
| 527 | CBP: backpropagation with constraint on weight precision using a pseudo-Lagrange multiplier method | 6.25 | 0.43 | 6, 6, 7, 6 | Poster | Poster | ✔ |
| 528 | CBP: backpropagation with constraint on weight precision using a pseudo-Lagrange multiplier method | 4.67 | 0.94 | 6, 4, 4 | Poster | Reject | ✔ |
| 529 | Biological key-value memory networks | 6.67 | 0.47 | 7, 7, 6 | Poster | Poster | ✔ |
| 530 | Biological key-value memory networks | 5.50 | 0.50 | 6, 5, 5, 6 | Poster | Reject | ✔ |
| 531 | Beyond Bandit Feedback in Online Multiclass Classification | 6.00 | 0.71 | 5, 6, 6, 7 | Poster | Reject | ✔ |
| 532 | Beyond Bandit Feedback in Online Multiclass Classification | 6.50 | 0.50 | 6, 7, 7, 6 | Poster | Poster | ✔ |
| 533 | Bandit Quickest Changepoint Detection | 5.75 | 0.43 | 5, 6, 6, 6 | Poster | Reject | ✔ |
| 534 | Bandit Quickest Changepoint Detection | 5.67 | 0.94 | 5, 7, 5 | Poster | Poster | ✔ |
| 535 | Bandit Learning with Delayed Impact of Actions | 6.50 | 0.50 | 7, 7, 6, 6 | Poster | Poster | ✔ |
| 536 | Bandit Learning with Delayed Impact of Actions | 6.80 | 0.40 | 7, 7, 7, 6, 7 | Poster | Spotlight | ✔ |
| 537 | Balanced Chamfer Distance as a Comprehensive Metric for Point Cloud Completion | 5.67 | 0.47 | 5, 6, 6 | Poster | Poster | ✔ |
| 538 | Balanced Chamfer Distance as a Comprehensive Metric for Point Cloud Completion | 5.00 | 0.00 | 5, 5, 5, 5 | Poster | Reject | ✔ |
| 539 | BCORLE(
λ
): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce Market | 6.67 | 0.94 | 6, 6, 8 | Poster | Poster | ✔ |
| 540 | BCORLE(
λ
): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce Market | 5.50 | 1.12 | 4, 6, 7, 5 | Poster | Reject | ✔ |
| 541 | Average-Reward Learning and Planning with Options | 6.50 | 0.87 | 6, 6, 8, 6 | Poster | Poster | ✔ |
| 542 | Average-Reward Learning and Planning with Options | 6.25 | 0.83 | 7, 7, 5, 6 | Poster | Reject | ✔ |
| 543 | Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting | 6.00 | 0.00 | 6, 6, 6 | Poster | Poster | ✔ |
| 544 | Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting | 6.50 | 0.50 | 6, 7, 7, 6 | Poster | Poster | ✔ |
| 545 | Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection | 6.25 | 1.92 | 3, 8, 7, 7 | Poster | Poster | ✔ |
| 546 | Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection | 5.67 | 0.94 | 5, 5, 7 | Poster | Reject | ✔ |
| 547 | Asymptotically Best Causal Effect Identification with Multi-Armed Bandits | 4.25 | 0.83 | 5, 5, 4, 3 | Poster | Reject | ✔ |
| 548 | Asymptotically Best Causal Effect Identification with Multi-Armed Bandits | 5.33 | 1.25 | 7, 4, 5 | Poster | Poster | ✔ |
| 549 | Arbitrary Conditional Distributions with Energy | 6.00 | 0.00 | 6, 6, 6, 6 | Poster | Reject | ✔ |
| 550 | Arbitrary Conditional Distributions with Energy | 6.25 | 0.83 | 5, 7, 6, 7 | Poster | Poster | ✔ |
| 551 | An Uncertainty Principle is a Price of Privacy-Preserving Microdata | 5.75 | 0.83 | 5, 6, 7, 5 | Poster | Poster | ✔ |
| 552 | An Uncertainty Principle is a Price of Privacy-Preserving Microdata | 6.75 | 0.83 | 6, 6, 7, 8 | Poster | Spotlight | ✔ |
| 553 | An Empirical Study of Adder Neural Networks for Object Detection | 5.00 | 1.63 | 7, 5, 3 | Poster | Reject | ✔ |
| 554 | An Empirical Study of Adder Neural Networks for Object Detection | 5.00 | 0.71 | 6, 4, 5, 5 | Poster | Poster | ✔ |
| 555 | Adversarially Robust Change Point Detection | 6.50 | 1.50 | 7, 7, 4, 8 | Poster | Poster | ✔ |
| 556 | Adversarially Robust Change Point Detection | 6.67 | 0.94 | 8, 6, 6 | Poster | Poster | ✔ |
| 557 | Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions | 6.00 | 0.71 | 6, 7, 6, 5 | Poster | Poster | ✔ |
| 558 | Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions | 7.00 | 0.00 | 7, 7, 7, 7 | Poster | Poster | ✔ |
| 559 | Adversarial Robustness with Non-uniform Perturbations | 5.20 | 0.40 | 5, 5, 5, 6, 5 | Poster | Reject | ✔ |
| 560 | Adversarial Robustness with Non-uniform Perturbations | 6.50 | 0.50 | 7, 6, 6, 6, 7, 7 | Poster | Poster | ✔ |
| 561 | Adversarial Reweighting for Partial Domain Adaptation | 5.00 | 0.00 | 5, 5, 5 | Poster | Reject | ✔ |
| 562 | Adversarial Reweighting for Partial Domain Adaptation | 6.00 | 0.82 | 7, 5, 6 | Poster | Poster | ✔ |
| 563 | Adversarial Regression with Doubly Non-negative Weighting Matrices | 7.00 | 0.63 | 7, 8, 7, 6, 7 | Poster | Poster | ✔ |
| 564 | Adversarial Regression with Doubly Non-negative Weighting Matrices | 6.00 | 0.71 | 7, 6, 5, 6 | Poster | Reject | ✔ |
| 565 | Adversarial Feature Desensitization | 6.00 | 0.71 | 5, 6, 7, 6 | Poster | Poster | ✔ |
| 566 | Adversarial Feature Desensitization | 4.50 | 1.12 | 6, 3, 4, 5 | Poster | Reject | ✔ |
| 567 | Adversarial Attacks on Graph Classifiers via Bayesian Optimisation | 4.50 | 1.50 | 6, 5, 2, 5 | Poster | Reject | ✔ |
| 568 | Adversarial Attacks on Graph Classifiers via Bayesian Optimisation | 6.25 | 0.43 | 6, 6, 6, 7 | Poster | Poster | ✔ |
| 569 | Adaptive Machine Unlearning | 6.33 | 0.47 | 6, 7, 6 | Poster | Poster | ✔ |
| 570 | Adaptive Machine Unlearning | 6.50 | 0.50 | 6, 6, 7, 7 | Poster | Poster | ✔ |
| 571 | Adaptive Denoising via GainTuning | 4.50 | 0.50 | 5, 4, 4, 5 | Poster | Reject | ✔ |
| 572 | Adaptive Denoising via GainTuning | 5.75 | 0.43 | 5, 6, 6, 6 | Poster | Poster | ✔ |
| 573 | Active Learning of Convex Halfspaces on Graphs | 6.25 | 0.83 | 7, 7, 6, 5 | Poster | Poster | ✔ |
| 574 | Active Learning of Convex Halfspaces on Graphs | 6.75 | 0.43 | 7, 7, 6, 7 | Poster | Poster | ✔ |
| 575 | Achieving Rotational Invariance with Bessel-Convolutional Neural Networks | 5.25 | 1.64 | 5, 4, 8, 4 | Poster | Reject | ✔ |
| 576 | Achieving Rotational Invariance with Bessel-Convolutional Neural Networks | 6.75 | 0.43 | 7, 7, 7, 6 | Poster | Poster | ✔ |
| 577 | Accelerating Quadratic Optimization with Reinforcement Learning | 5.00 | 2.45 | 5, 1, 7, 7 | Poster | Poster | ✔ |
| 578 | Accelerating Quadratic Optimization with Reinforcement Learning | 6.67 | 0.47 | 7, 7, 6 | Poster | Poster | ✔ |
| 579 | A nonparametric method for gradual change problems with statistical guarantees | 5.75 | 0.43 | 6, 5, 6, 6 | Poster | Reject | ✔ |
| 580 | A nonparametric method for gradual change problems with statistical guarantees | 6.80 | 0.98 | 7, 7, 7, 5, 8 | Poster | Spotlight | ✔ |
| 581 | A mechanistic multi-area recurrent network model of decision-making | 6.33 | 0.47 | 7, 6, 6 | Poster | Poster | ✔ |
| 582 | A mechanistic multi-area recurrent network model of decision-making | 5.75 | 1.79 | 7, 8, 4, 4 | Poster | Reject | ✔ |
| 583 | A Stochastic Newton Algorithm for Distributed Convex Optimization | 6.25 | 0.43 | 6, 6, 7, 6 | Poster | Poster | ✔ |
| 584 | A Stochastic Newton Algorithm for Distributed Convex Optimization | 4.00 | 1.22 | 3, 4, 6, 3 | Poster | Reject | ✔ |
| 585 | A PAC-Bayes Analysis of Adversarial Robustness | 5.50 | 0.87 | 6, 6, 6, 4 | Poster | Reject | ✔ |
| 586 | A PAC-Bayes Analysis of Adversarial Robustness | 6.33 | 0.47 | 7, 6, 6 | Poster | Poster | ✔ |
| 587 | A Non-commutative Extension of Lee-Seung's Algorithm for Positive Semidefinite Factorizations | 7.00 | 0.00 | 7, 7, 7, 7 | Poster | Poster | ✔ |
| 588 | A Non-commutative Extension of Lee-Seung's Algorithm for Positive Semidefinite Factorizations | 4.33 | 0.47 | 4, 5, 4 | Poster | Reject | ✔ |
| 589 | A Mathematical Framework for Quantifying Transferability in Multi-source Transfer Learning | 6.00 | 0.71 | 6, 6, 5, 7 | Poster | Poster | ✔ |
| 590 | A Mathematical Framework for Quantifying Transferability in Multi-source Transfer Learning | 5.50 | 0.50 | 6, 5, 6, 5 | Poster | Reject | ✔ |
| 591 | A Highly-Efficient Group Elastic Net Algorithm with an Application to Function-On-Scalar Regression | 6.33 | 0.47 | 7, 6, 6 | Poster | Poster | ✔ |
| 592 | A Highly-Efficient Group Elastic Net Algorithm with an Application to Function-On-Scalar Regression | 5.75 | 1.09 | 7, 6, 6, 4 | Poster | Poster | ✔ |
| 593 | A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning | 5.67 | 0.47 | 6, 6, 5 | Poster | Reject | ✔ |
| 594 | A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning | 6.25 | 0.83 | 7, 7, 6, 5 | Poster | Poster | ✔ |
| 595 | A Computationally Efficient Method for Learning Exponential Family Distributions | 6.00 | 0.71 | 7, 6, 6, 5 | Poster | Poster | ✔ |
| 596 | A Computationally Efficient Method for Learning Exponential Family Distributions | 6.25 | 0.83 | 5, 6, 7, 7 | Poster | Poster | ✔ |