Learning Theory

On learning sparse vectors from mixture of responses NeurIPS 2021 Online Convex Optimization with Continuous Switching Constraint NeurIPS 2021 Online Knapsack with Frequency Predictions NeurIPS 2021 Online Learning from Optimal Actions COLT 2021 Online Learning in Periodic Zero-Sum Games NeurIPS 2021 Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games COLT 2021 Online Model Selection for Reinforcement Learning with Function Approximation AISTATS 2021 Online Optimization in Games via Control Theory: Connecting Regret, Passivity and Poincaré Recurrence ICML 2021 Online Sparse Reinforcement Learning AISTATS 2021 On Lower Bounds for Standard and Robust Gaussian Process Bandit Optimization ICML 2021 On Projection Robust Optimal Transport: Sample Complexity and Model Misspecification AISTATS 2021 On Provable Benefits of Depth in Training Graph Convolutional Networks NeurIPS 2021 On the Adequacy of Untuned Warmup for Adaptive Optimization AAAI 2021 On the Algorithmic Stability of Adversarial Training NeurIPS 2021 On the Cryptographic Hardness of Learning Single Periodic Neurons NeurIPS 2021 On the Estimation of Network Complexity: Dimension of Graphons JMLR 2021 On the Expected Complexity of Maxout Networks NeurIPS 2021 On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models ICML 2021 On the geometry of generalization and memorization in deep neural networks ICLR 2021 On the Hardness of Robust Classification JMLR 2021 On the High Accuracy Limitation of Adaptive Property Estimation AISTATS 2021 On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent ICML 2021 On the Inherent Regularization Effects of Noise Injection During Training ICML 2021 On the Learnability of Knowledge in Multi-Agent Logics IJCAI 2021 On the Limits of Pseudo Ground Truth in Visual Camera Re-Localisation ICCV 2021