Theory

From Shapley Values to Generalized Additive Models and back AISTATS 2023 From Understanding the Population Dynamics of the NSGA-II to the First Proven Lower Bounds AAAI 2023 Fully-Dynamic Decision Trees AAAI 2023 Functional Equivalence and Path Connectivity of Reducible Hyperbolic Tangent Networks NeurIPS 2023 Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods ICML 2023 GANs as Gradient Flows that Converge JMLR 2023 Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses ICLR 2023 Generalization Bounds for Inductive Matrix Completion in Low-Noise Settings AAAI 2023 Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels JMLR 2023 Generalization Bounds using Data-Dependent Fractal Dimensions ICML 2023 Generalization error bounds for multiclass sparse linear classifiers JMLR 2023 Generalization Guarantees via Algorithm-dependent Rademacher Complexity COLT 2023 Generalization in Graph Neural Networks: Improved PAC-Bayesian Bounds on Graph Diffusion AISTATS 2023 Generalization on the Unseen, Logic Reasoning and Degree Curriculum ICML 2023 Generalizing and Decoupling Neural Collapse via Hyperspherical Uniformity Gap ICLR 2023 Generalizing Nonlinear ICA Beyond Structural Sparsity NeurIPS 2023 Geometrically regularized autoencoders for non-Euclidean data ICLR 2023 Geometric Barriers for Stable and Online Algorithms for Discrepancy Minimization COLT 2023 GLIME: General, Stable and Local LIME Explanation NeurIPS 2023 Globally injective and bijective neural operators NeurIPS 2023 Globally Optimal Training of Neural Networks with Threshold Activation Functions ICLR 2023 Global optimality of Elman-type RNNs in the mean-field regime ICML 2023 Going beyond persistent homology using persistent homology NeurIPS 2023 Graph Neural Tangent Kernel: Convergence on Large Graphs ICML 2023 Great Models Think Alike: Improving Model Reliability via Inter-Model Latent Agreement ICML 2023