Theory

Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension NeurIPS 2023 Mini-batch k-means terminates within O(d/ε) iterations ICLR 2023 Minimal Width for Universal Property of Deep RNN JMLR 2023 Minimum-Entropy Coupling Approximation Guarantees Beyond the Majorization Barrier AISTATS 2023 Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation ICML 2023 Mining Effective Features Using Quantum Entropy for Humor Recognition EACL 2023 Mitigating Transformer Overconfidence via Lipschitz Regularization UAI 2023 Mixture Proportion Estimation Beyond Irreducibility ICML 2023 MMD Aggregated Two-Sample Test JMLR 2023 Model-Bellman Inconsistency for Model-based Offline Reinforcement Learning ICML 2023 ModelDiff: A Framework for Comparing Learning Algorithms ICML 2023 Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization ICLR 2023 Model Shapley: Equitable Model Valuation with Black-box Access NeurIPS 2023 Model-X Sequential Testing for Conditional Independence via Testing by Betting AISTATS 2023 Mode-Seeking Divergences: Theory and Applications to GANs AISTATS 2023 Modified Policy Iteration for Exponential Cost Risk Sensitive MDPs L4DC 2023 Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes ICML 2023 Moral Responsibility for AI Systems NeurIPS 2023 Multiclass Online Learning and Uniform Convergence COLT 2023 Multi-Fidelity Bayesian Optimization with Unreliable Information Sources AISTATS 2023 Multi-Layer Neural Networks as Trainable Ladders of Hilbert Spaces ICML 2023 Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes ICML 2023 Nearly-tight Bounds for Deep Kernel Learning ICML 2023 Near-optimal Conservative Exploration in Reinforcement Learning under Episode-wise Constraints ICML 2023 Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions NeurIPS 2023