Learning Theory

Scaling Law for Document Neural Machine Translation EMNLP 2023 Scaling Laws for BERT in Low-Resource Settings ACL 2023 Scaling Laws For Deep Learning Based Image Reconstruction ICLR 2023 Scaling Laws for Discriminative Speech Recognition Rescoring Models Interspeech 2023 Scaling Laws for Generative Mixed-Modal Language Models ICML 2023 Scan and Snap: Understanding Training Dynamics and Token Composition in 1-layer Transformer NeurIPS 2023 Second Order Path Variationals in Non-Stationary Online Learning AISTATS 2023 Self-Directed Linear Classification COLT 2023 Self-Stabilization: The Implicit Bias of Gradient Descent at the Edge of Stability ICLR 2023 Semi-supervised Learning with Support Isolation by Small-Paced Self-Training AAAI 2023 Semi-Verified PAC Learning from the Crowd AISTATS 2023 Sequential Changepoint Detection via Backward Confidence Sequences ICML 2023 SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics COLT 2023 Sharper Bounds for Uniformly Stable Algorithms with Stationary Mixing Process ICLR 2023 Sharper Model-free Reinforcement Learning for Average-reward Markov Decision Processes COLT 2023 Sharpness Minimization Algorithms Do Not Only Minimize Sharpness To Achieve Better Generalization NeurIPS 2023 Sharp thresholds in inference of planted subgraphs COLT 2023 Sharp Variance-Dependent Bounds in Reinforcement Learning: Best of Both Worlds in Stochastic and Deterministic Environments ICML 2023 Shifted PAUQ: Distribution shift in text-to-SQL EMNLP 2023 Shortest Program Interpolation Learning COLT 2023 Simpler neural networks prefer subregular languages EMNLP 2023 Simplicity Bias in 1-Hidden Layer Neural Networks NeurIPS 2023 Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions ACL 2023 Sketched Ridgeless Linear Regression: The Role of Downsampling ICML 2023 SLaM: Student-Label Mixing for Distillation with Unlabeled Examples NeurIPS 2023