integral probability metric

Quantification of Credal Uncertainty: A Distance-Based Approach UAI 2026 Integral Probability Metrics Meet Neural Networks: The Radon-Kolmogorov-Smirnov Test JMLR 2025 Hierarchical Integral Probability Metrics: A distance on random probability measures with low sample complexity ICML 2024 Covariate balancing using the integral probability metric for causal inference ICML 2023 Function-space regularized Rényi divergences ICLR 2023 Minimax Nonparametric Two-Sample Test under Adversarial Losses AISTATS 2023 Optimizing DDPM Sampling with Shortcut Fine-Tuning ICML 2023 STEERING : Stein Information Directed Exploration for Model-Based Reinforcement Learning ICML 2023 Unified Perspective on Probability Divergence via the Density-Ratio Likelihood: Bridging KL-Divergence and Integral Probability Metrics AISTATS 2023 (f,Gamma)-Divergences: Interpolating between f-Divergences and Integral Probability Metrics JMLR 2022 Integral Probability Metrics PAC-Bayes Bounds NeurIPS 2022 Learning fair representation with a parametric integral probability metric ICML 2022 Equitable and Optimal Transport with Multiple Agents AISTATS 2021 Non-asymptotic Error Bounds for Bidirectional GANs NeurIPS 2021 Optimal Bounds between f-Divergences and Integral Probability Metrics JMLR 2021 Risk Bounds for Unsupervised Cross-Domain Mapping with IPMs JMLR 2021 Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics NeurIPS 2021 Statistical guarantees for generative models without domination ALT 2021 Distributional Robustness with IPMs and links to Regularization and GANs NeurIPS 2020 Optimal Bounds between f-Divergences and Integral Probability Metrics ICML 2020 Reciprocal Adversarial Learning via Characteristic Functions NeurIPS 2020 Statistical and Topological Properties of Sliced Probability Divergences NeurIPS 2020 A Higher-Order Kolmogorov-Smirnov Test AISTATS 2019 Provably Efficient Imitation Learning from Observation Alone ICML 2019 Sobolev Independence Criterion NeurIPS 2019