total variation distance

Hamiltonian Monte Carlo for efficient Gaussian sampling: long and random steps JMLR 2024 Mean-field Underdamped Langevin Dynamics and its Spacetime Discretization ICML 2024 Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples ALT 2024 Not All Learnable Distribution Classes are Privately Learnable ALT 2024 Piecewise deterministic generative models NeurIPS 2024 Sample-Optimal Locally Private Hypothesis Selection and the Provable Benefits of Interactivity COLT 2024 Simplifying Complex Observation Models in Continuous POMDP Planning with Probabilistic Guarantees and Practice AAAI 2024 Total Variation Distance Meets Probabilistic Inference ICML 2024 Towards Theoretical Understandings of Self-Consuming Generative Models ICML 2024 Uncertainty Quantification via Stable Distribution Propagation ICLR 2024 Algorithmic Gaussianization through Sketching: Converting Data into Sub-gaussian Random Designs COLT 2023 Contrastive Moments: Unsupervised Halfspace Learning in Polynomial Time NeurIPS 2023 Hypothesis Selection with Memory Constraints NeurIPS 2023 Learning and Testing Latent-Tree Ising Models Efficiently COLT 2023 Learning Distributions over Quantum Measurement Outcomes ICML 2023 Learning Hidden Markov Models Using Conditional Samples COLT 2023 Nearest-Neighbor Sampling Based Conditional Independence Testing AAAI 2023 On Approximating Total Variation Distance IJCAI 2023 On Differentially Private Sampling from Gaussian and Product Distributions NeurIPS 2023 On the Robustness of Mechanism Design under Total Variation Distance NeurIPS 2023 SQ Lower Bounds for Learning Mixtures of Linear Classifiers NeurIPS 2023 Statistical Indistinguishability of Learning Algorithms ICML 2023 Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms ICLR 2023 Tailoring Language Generation Models under Total Variation Distance ICLR 2023 The Sample Complexity of Approximate Rejection Sampling With Applications to Smoothed Online Learning COLT 2023