Learning Theory

Efficient active learning of sparse halfspaces with arbitrary bounded noise NeurIPS 2020 Efficient Algorithms for Learning Revenue-Maximizing Two-Part Tariffs IJCAI 2020 Efficient and Robust High-Dimensional Linear Contextual Bandits IJCAI 2020 Efficient improper learning for online logistic regression COLT 2020 Efficiently Learning Adversarially Robust Halfspaces with Noise ICML 2020 Efficient, Noise-Tolerant, and Private Learning via Boosting COLT 2020 Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation ICML 2020 Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning AISTATS 2020 Empirical Bounds on Linear Regions of Deep Rectifier Networks AAAI 2020 Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models ICML 2020 Error-Bounded Correction of Noisy Labels ICML 2020 Estimating decision tree learnability with polylogarithmic sample complexity NeurIPS 2020 Estimation Rates for Sparse Linear Cyclic Causal Models UAI 2020 Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions NeurIPS 2020 Evaluating Compositionality of Sentence Representation Models ACL 2020 Evaluating Models’ Local Decision Boundaries via Contrast Sets EMNLP 2020 Evaluating The Search Phase of Neural Architecture Search ICLR 2020 Exact expressions for double descent and implicit regularization via surrogate random design NeurIPS 2020 Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions ACL 2020 Exploration by Optimisation in Partial Monitoring COLT 2020 Exploring Weaknesses of VQA Models through Attribution Driven Insights ACL 2020 Extending Learnability to Auxiliary-Input Cryptographic Primitives and Meta-PAC Learning COLT 2020 Extrapolation Towards Imaginary 0-Nearest Neighbour and Its Improved Convergence Rate NeurIPS 2020 Fantastic Generalization Measures and Where to Find Them ICLR 2020 Fast and Bayes-consistent nearest neighbors AISTATS 2020