Learning Theory

Understanding Generalization in Recurrent Neural Networks ICLR 2020 Understanding the Effects of Batching in Online Active Learning AISTATS 2020 Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle ICML 2020 Understanding Why Neural Networks Generalize Well Through GSNR of Parameters ICLR 2020 Uniform Convergence of Rank-weighted Learning ICML 2020 Unique Properties of Flat Minima in Deep Networks ICML 2020 Universal Approximation with Deep Narrow Networks COLT 2020 Universal guarantees for decision tree induction via a higher-order splitting criterion NeurIPS 2020 Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs CVPR 2020 What Can Neural Networks Reason About? ICLR 2020 What Do Position Embeddings Learn? An Empirical Study of Pre-Trained Language Model Positional Encoding EMNLP 2020 What Happens To BERT Embeddings During Fine-tuning? EMNLP 2020 What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation NeurIPS 2020 What shapes feature representations? Exploring datasets, architectures, and training NeurIPS 2020 When Demands Evolve Larger and Noisier: Learning and Earning in a Growing Environment ICML 2020 When Do Neural Networks Outperform Kernel Methods? NeurIPS 2020 When to Use Convolutional Neural Networks for Inverse Problems CVPR 2020 Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? --- A Neural Tangent Kernel Perspective NeurIPS 2020 With Little Power Comes Great Responsibility EMNLP 2020 Achieving Optimal Dynamic Regret for Non-stationary Bandits without Prior Information COLT 2019 A Continuous-Time View of Early Stopping for Least Squares Regression AISTATS 2019 A Convergence Theory for Deep Learning via Over-Parameterization ICML 2019 Active Ranking with Subset-wise Preferences AISTATS 2019 AdaLinUCB: Opportunistic Learning for Contextual Bandits IJCAI 2019 Adaptive Exact Learning of Decision Trees from Membership Queries ALT 2019