Learning Theory

Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimization NeurIPS 2024 Rethinking Centered Kernel Alignment in Knowledge Distillation IJCAI 2024 Rethinking CNN’s Generalization to Backdoor Attack from Frequency Domain ICLR 2024 Rethinking Evaluation Methods for Machine Unlearning EMNLP 2024 Rethinking Guidance Information to Utilize Unlabeled Samples: A Label Encoding Perspective ICML 2024 Rethinking Independent Cross-Entropy Loss For Graph-Structured Data ICML 2024 Rethinking Information-theoretic Generalization: Loss Entropy Induced PAC Bounds ICLR 2024 Rethinking LLM Memorization through the Lens of Adversarial Compression NeurIPS 2024 Rethinking Propagation for Unsupervised Graph Domain Adaptation AAAI 2024 Rethinking the Capacity of Graph Neural Networks for Branching Strategy NeurIPS 2024 Rethinking the Effectiveness of Graph Classification Datasets in Benchmarks for Assessing GNNs IJCAI 2024 Rethinking Transformers in Solving POMDPs ICML 2024 Revisiting Causal Discovery from a Complexity-Theoretic Perspective IJCAI 2024 Revisiting the Information Capacity of Neural Network Watermarks: Upper Bound Estimation and Beyond AAAI 2024 Revisit the Essence of Distilling Knowledge through Calibration ICML 2024 Reward-Free Kernel-Based Reinforcement Learning ICML 2024 Robust agents learn causal world models ICLR 2024 Robust Learning-Augmented Dictionaries ICML 2024 Robustly Improving Bandit Algorithms with Confounded and Selection Biased Offline Data: A Causal Approach AAAI 2024 Robustly Learning Single-Index Models via Alignment Sharpness ICML 2024 Robust NAS under adversarial training: benchmark, theory, and beyond ICLR 2024 Robustness of Nonlinear Representation Learning ICML 2024 Robust Offline Active Learning on Graphs NeurIPS 2024 RoDLA: Benchmarking the Robustness of Document Layout Analysis Models CVPR 2024 Role of Locality and Weight Sharing in Image-Based Tasks: A Sample Complexity Separation between CNNs, LCNs, and FCNs ICLR 2024