Theory

Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks ICLR 2023 Kernelized Reinforcement Learning with Order Optimal Regret Bounds NeurIPS 2023 Kernel Quadrature with Randomly Pivoted Cholesky NeurIPS 2023 Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors NeurIPS 2023 Large data limit of the MBO scheme for data clustering: convergence of the dynamics JMLR 2023 Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback NeurIPS 2023 Learning better with Dale’s Law: A Spectral Perspective NeurIPS 2023 Learning in Multi-Memory Games Triggers Complex Dynamics Diverging from Nash Equilibrium IJCAI 2023 Learning k-qubit Quantum Operators via Pauli Decomposition AISTATS 2023 Learning Logic Programs by Discovering Where Not to Search AAAI 2023 Learning on Manifolds: Universal Approximations Properties using Geometric Controllability Conditions for Neural ODEs L4DC 2023 Learning Small Decision Trees with Large Domain IJCAI 2023 Learning to Estimate Shapley Values with Vision Transformers ICLR 2023 Learning to Extrapolate: A Transductive Approach ICLR 2023 Learning with Stochastic Orders ICLR 2023 Leveraging the two-timescale regime to demonstrate convergence of neural networks NeurIPS 2023 Likelihood Ratio Confidence Sets for Sequential Decision Making NeurIPS 2023 Limitations on approximation by deep and shallow neural networks JMLR 2023 Limits, approximation and size transferability for GNNs on sparse graphs via graphops NeurIPS 2023 Limits for learning with language models ACL 2023 Linear CNNs Discover the Statistical Structure of the Dataset Using Only the Most Dominant Frequencies ICML 2023 Linear Connectivity Reveals Generalization Strategies ICLR 2023 Linearly Constrained Bilevel Optimization: A Smoothed Implicit Gradient Approach ICML 2023 Linear Regularizers Enforce the Strict Saddle Property AAAI 2023 List and Certificate Complexities in Replicable Learning NeurIPS 2023