Theory

On the Convergence of Black-Box Variational Inference NeurIPS 2023 On the Convergence of Gradient Flow on Multi-layer Linear Models ICML 2023 On The Convergence Of Policy Iteration-Based Reinforcement Learning With Monte Carlo Policy Evaluation AISTATS 2023 On the Convergence of SARSA with Linear Function Approximation ICML 2023 On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters ICML 2023 On the duality between contrastive and non-contrastive self-supervised learning ICLR 2023 On the Effects of Structural Modeling for Neural Semantic Parsing CoNLL 2023 On the Expressive Flexibility of Self-Attention Matrices AAAI 2023 On the Expressive Power of Geometric Graph Neural Networks ICML 2023 On the Identifiability and Estimation of Causal Location-Scale Noise Models ICML 2023 On the Implicit Bias of Linear Equivariant Steerable Networks NeurIPS 2023 On the inconsistency of separable losses for structured prediction EACL 2023 On the Interventional Kullback-Leibler Divergence CLeaR 2023 On the limitations of Markovian rewards to express multi-objective, risk-sensitive, and modal tasks UAI 2023 On the Limitations of Simulating Active Learning ACL 2023 On the Occupancy Measure of Non-Markovian Policies in Continuous MDPs ICML 2023 On the Optimality of Misspecified Kernel Ridge Regression ICML 2023 On the Optimality of Nuclear-norm-based Matrix Completion for Problems with Smooth Non-linear Structure JMLR 2023 On the Power of Foundation Models ICML 2023 On the Relation between Sensitivity and Accuracy in In-Context Learning EMNLP 2023 On the Relationship Between Relevance and Conflict in Online Social Link Recommendations NeurIPS 2023 On The Relative Error of Random Fourier Features for Preserving Kernel Distance ICLR 2023 On the Robustness of Removal-Based Feature Attributions NeurIPS 2023 On the Robustness of Text Vectorizers ICML 2023 On the role of model uncertainties in Bayesian optimisation UAI 2023