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Theory

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Papers

Incorporating Unlabeled Data into Distributionally Robust Learning JMLR 2021
Path Length Bounds for Gradient Descent and Flow JMLR 2021
Prediction against a limited adversary JMLR 2021
Attention is Turing-Complete JMLR 2021
Analyzing the discrepancy principle for kernelized spectral filter learning algorithms JMLR 2021
On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift JMLR 2021
Adaptive estimation of nonparametric functionals JMLR 2021
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks JMLR 2021
Some Theoretical Insights into Wasserstein GANs JMLR 2021
Optimal Bounds between f-Divergences and Integral Probability Metrics JMLR 2021
Hoeffding's Inequality for General Markov Chains and Its Applications to Statistical Learning JMLR 2021
Non-attracting Regions of Local Minima in Deep and Wide Neural Networks JMLR 2021
A Sharp Blockwise Tensor Perturbation Bound for Orthogonal Iteration JMLR 2021
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings JMLR 2021
Representer Theorems in Banach Spaces: Minimum Norm Interpolation, Regularized Learning and Semi-Discrete Inverse Problems JMLR 2021
How Well Generative Adversarial Networks Learn Distributions JMLR 2021
Consistency of Gaussian Process Regression in Metric Spaces JMLR 2021
On the Stability Properties and the Optimization Landscape of Training Problems with Squared Loss for Neural Networks and General Nonlinear Conic Approximation Schemes JMLR 2021
Exact Asymptotics for Linear Quadratic Adaptive Control JMLR 2021
On the Hardness of Robust Classification JMLR 2021
V-statistics and Variance Estimation JMLR 2021
Training deep residual networks for uniform approximation guarantees L4DC 2021
Learning Finite-Dimensional Representations For Koopman Operators L4DC 2021
CheXbreak: Misclassification Identification for Deep Learning Models Interpreting Chest X-rays MLHC 2021
Power Constrained Bandits MLHC 2021