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Theory

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Papers

On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis AISTATS 2020
Assessing Local Generalization Capability in Deep Models AISTATS 2020
Domain-Liftability of Relational Marginal Polytopes AISTATS 2020
Beyond exploding and vanishing gradients: analysing RNN training using attractors and smoothness AISTATS 2020
GAIT: A Geometric Approach to Information Theory AISTATS 2020
Finite-Time Error Bounds for Biased Stochastic Approximation with Applications to Q-Learning AISTATS 2020
Flexible distribution-free conditional predictive bands using density estimators AISTATS 2020
Quantitative stability of optimal transport maps and linearization of the 2-Wasserstein space AISTATS 2020
Gaussian-Smoothed Optimal Transport: Metric Structure and Statistical Efficiency AISTATS 2020
Causal inference in degenerate systems: An impossibility result AISTATS 2020
A Three Sample Hypothesis Test for Evaluating Generative Models AISTATS 2020
The Expressive Power of a Class of Normalizing Flow Models AISTATS 2020
Gain with no Pain: Efficiency of Kernel-PCA by Nyström Sampling AISTATS 2020
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning AISTATS 2020
Error bounds in estimating the out-of-sample prediction error using leave-one-out cross validation in high-dimensions AISTATS 2020
Budget Learning via Bracketing AISTATS 2020
Explicit Mean-Square Error Bounds for Monte-Carlo and Linear Stochastic Approximation AISTATS 2020
Invertible Generative Modeling using Linear Rational Splines AISTATS 2020
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks AISTATS 2020
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms AISTATS 2020
Approximate Cross-validation: Guarantees for Model Assessment and Selection AISTATS 2020
On Learnability wih Computable Learners ALT 2020
Distribution Free Learning with Local Queries ALT 2020
Interactive Learning of a Dynamic Structure ALT 2020
Adversarially Robust Learning Could Leverage Computational Hardness. ALT 2020