renyi divergence

Generalization of noisy SGD in unbounded non-convex settings ICML 2025 Rényi Neural Processes ICML 2025 Lower Bounds on the Bayesian Risk via Information Measures JMLR 2024 PAC-Bayesian Error Bound, via Rényi Divergence, for a Class of Linear Time-Invariant State-Space Models ICML 2024 Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration via Shift Reduction Lemmas ICML 2024 Risk-sensitive control as inference with Rényi divergence NeurIPS 2024 Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano ICML 2023 Function-space regularized Rényi divergences ICLR 2023 Improved Discretization Analysis for Underdamped Langevin Monte Carlo COLT 2023 Individual Privacy Accounting with Gaussian Differential Privacy ICLR 2023 On the Theoretical Equivalence of Several Trade-Off Curves Assessing Statistical Proximity JMLR 2023 Private Prediction Strikes Back! Private Kernelized Nearest Neighbors with Individual Rényi Filter UAI 2023 Resolving the Mixing Time of the Langevin Algorithm to its Stationary Distribution for Log-Concave Sampling COLT 2023 Convergence of Langevin Monte Carlo in Chi-Squared and Rényi Divergence AISTATS 2022 Fast Relative Entropy Coding with A* coding ICML 2022 A Discriminative Technique for Multiple-Source Adaptation ICML 2021 A Novel Estimator of Mutual Information for Learning to Disentangle Textual Representations ACL 2021 Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent NeurIPS 2021 Individual Privacy Accounting via a Rényi Filter NeurIPS 2021 Location Trace Privacy Under Conditional Priors AISTATS 2021 Privacy Budget Scheduling OSDI 2021 Consistent recovery threshold of hidden nearest neighbor graphs COLT 2020 Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC NeurIPS 2020 Hypothesis Testing Interpretations and Renyi Differential Privacy AISTATS 2020 PAC-Bayes Learning Bounds for Sample-Dependent Priors NeurIPS 2020