Learning Theory

Quantity doesn’t buy quality syntax with neural language models EMNLP 2019 Rademacher Complexity for Adversarially Robust Generalization ICML 2019 Random deep neural networks are biased towards simple functions NeurIPS 2019 Randomized Wagering Mechanisms AAAI 2019 Random Shuffling Beats SGD after Finite Epochs ICML 2019 Reconstructing Trees from Traces COLT 2019 RecurJac: An Efficient Recursive Algorithm for Bounding Jacobian Matrix of Neural Networks and Its Applications AAAI 2019 Region-Based Active Learning AISTATS 2019 Regret Bounds for Learning State Representations in Reinforcement Learning NeurIPS 2019 Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems NeurIPS 2019 Regret Minimization for Reinforcement Learning by Evaluating the Optimal Bias Function NeurIPS 2019 Relating RNN Layers with the Spectral WFA Ranks in Sequence Modelling ACL 2019 ResNet and Batch-normalization Improve Data Separability ACML 2019 Robust Metric Learning on Grassmann Manifolds with Generalization Guarantees AAAI 2019 Robustness May Be at Odds with Accuracy ICLR 2019 Rotting bandits are no harder than stochastic ones AISTATS 2019 Sample Complexity of Learning Mixture of Sparse Linear Regressions NeurIPS 2019 Sample complexity of partition identification using multi-armed bandits COLT 2019 Sample Compression for Real-Valued Learners ALT 2019 Sample-Optimal Parametric Q-Learning Using Linearly Additive Features ICML 2019 Semantic Expressive Capacity with Bounded Memory ACL 2019 Separating Wheat from Chaff: Joining Biomedical Knowledge and Patient Data for Repurposing Medications AAAI 2019 SGD Converges to Global Minimum in Deep Learning via Star-convex Path ICLR 2019 Sharp Analysis of Learning with Discrete Losses AISTATS 2019 Simultaneous Private Learning of Multiple Concepts JMLR 2019