total variation distance

Certified Adversarial Robustness Under the Bounded Support Set ICML 2022 Imitation Learning by Reinforcement Learning ICLR 2022 Independence Testing for Bounded Degree Bayesian Networks NeurIPS 2022 Learning and Covering Sums of Independent Random Variables with Unbounded Support NeurIPS 2022 Learning GMMs with Nearly Optimal Robustness Guarantees COLT 2022 Lower Bounds on the Total Variation Distance Between Mixtures of Two Gaussians ALT 2022 Nearly-Tight Bounds for Testing Histogram Distributions NeurIPS 2022 On the expressivity of bi-Lipschitz normalizing flows ACML 2022 On The Memory Complexity of Uniformity Testing COLT 2022 Optimal SQ Lower Bounds for Robustly Learning Discrete Product Distributions and Ising Models COLT 2022 Sharp Constants in Uniformity Testing via the Huber Statistic COLT 2022 The Price of Tolerance in Distribution Testing COLT 2022 Differentially Private Sampling from Distributions NeurIPS 2021 Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling UAI 2021 Privately Learning Mixtures of Axis-Aligned Gaussians NeurIPS 2021 Testing Probabilistic Circuits NeurIPS 2021 Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning NeurIPS 2020 Locally Private Hypothesis Selection COLT 2020 Minimax Testing of Identity to a Reference Ergodic Markov Chain AISTATS 2020 Optimal Bounds between f-Divergences and Integral Probability Metrics ICML 2020 Estimating Convergence of Markov chains with L-Lag Couplings NeurIPS 2019 Maximum Entropy Distributions: Bit Complexity and Stability COLT 2019 Privately Learning High-Dimensional Distributions COLT 2019 The Optimal Approximation Factor in Density Estimation COLT 2019 Probability–Revealing Samples AISTATS 2018