Theory

Last-iterate Convergence in Extensive-Form Games NeurIPS 2021 Latent Convergent Cross Mapping ICLR 2021 Learned Extragradient ISTA with Interpretable Residual Structures for Sparse Coding AAAI 2021 Learning and certification under instance-targeted poisoning UAI 2021 Learning CNF Theories Using MDL and Predicate Invention IJCAI 2021 Learning curves of generic features maps for realistic datasets with a teacher-student model NeurIPS 2021 Learning Finite-Dimensional Representations For Koopman Operators L4DC 2021 Learning Groupwise Explanations for Black-Box Models IJCAI 2021 Learning Hyperbolic Representations of Topological Features ICLR 2021 Learning Implicitly with Noisy Data in Linear Arithmetic IJCAI 2021 Learning in Matrix Games can be Arbitrarily Complex COLT 2021 Learning Interaction Kernels for Agent Systems on Riemannian Manifolds ICML 2021 Learning rule influences recurrent network representations but not attractor structure in decision-making tasks NeurIPS 2021 Learning Set Functions that are Sparse in Non-Orthogonal Fourier Bases AAAI 2021 Learning to Select Exogenous Events for Marked Temporal Point Process NeurIPS 2021 Learning with Hyperspherical Uniformity AISTATS 2021 Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings JMLR 2021 Living Without Beth and Craig: Definitions and Interpolants in Description Logics with Nominals and Role Inclusions AAAI 2021 Localization, Convexity, and Star Aggregation NeurIPS 2021 Localizing Changes in High-Dimensional Regression Models AISTATS 2021 Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis NeurIPS 2021 Lower and Upper Bounds on the Pseudo-Dimension of Tensor Network Models NeurIPS 2021 Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions NeurIPS 2021 Manifold Topology Divergence: a Framework for Comparing Data Manifolds. NeurIPS 2021 Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data ICML 2021