Theory

Computably Continuous Reinforcement-Learning Objectives Are PAC-Learnable AAAI 2023 Computational Asymmetries in Robust Classification ICML 2023 Concentration analysis of multivariate elliptic diffusions JMLR 2023 Conformalized Unconditional Quantile Regression AISTATS 2023 Conformal Nucleus Sampling ACL 2023 Connected Superlevel Set in (Deep) Reinforcement Learning and its Application to Minimax Theorems NeurIPS 2023 Continuous-in-time Limit for Bayesian Bandits JMLR 2023 Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions ICLR 2023 Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network ICML 2023 Convergence and Diversity in the Control Hierarchy ACL 2023 Convergence of Actor-Critic with Multi-Layer Neural Networks NeurIPS 2023 Convergence of Alternating Gradient Descent for Matrix Factorization NeurIPS 2023 Convergence of score-based generative modeling for general data distributions ALT 2023 Convergence of Stein Variational Gradient Descent under a Weaker Smoothness Condition AISTATS 2023 Convolutional State Space Models for Long-Range Spatiotemporal Modeling NeurIPS 2023 Critical Initialization of Wide and Deep Neural Networks using Partial Jacobians: General Theory and Applications NeurIPS 2023 Cross-Entropy Loss Functions: Theoretical Analysis and Applications ICML 2023 Curvature-Balanced Feature Manifold Learning for Long-Tailed Classification CVPR 2023 Data-Copying in Generative Models: A Formal Framework ICML 2023 Data Feedback Loops: Model-driven Amplification of Dataset Biases ICML 2023 Data Level Lottery Ticket Hypothesis for Vision Transformers IJCAI 2023 Data Representations’ Study of Latent Image Manifolds ICML 2023 Data Sampling and (In)stability in Machine Translation Evaluation ACL 2023 Data Selection for Fine-tuning Large Language Models Using Transferred Shapley Values ACL 2023 Data Valuation Without Training of a Model ICLR 2023