Stochastic Processes

On the Inherent Regularization Effects of Noise Injection During Training ICML 2021 On the Intrinsic Differential Privacy of Bagging IJCAI 2021 On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning COLT 2021 On the Stochastic Stability of Deep Markov Models NeurIPS 2021 On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs) NeurIPS 2021 Oops I Took A Gradient: Scalable Sampling for Discrete Distributions ICML 2021 Optimal Algorithms for Stochastic Contextual Preference Bandits NeurIPS 2021 Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm COLT 2021 Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime ICLR 2021 Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search ICML 2021 Optimal Underdamped Langevin MCMC Method NeurIPS 2021 Optimizing Information-theoretical Generalization Bound via Anisotropic Noise of SGLD NeurIPS 2021 Orientation Probabilistic Movement Primitives on Riemannian Manifolds CoRL 2021 Outlier-Robust Learning of Ising Models Under Dobrushin’s Condition COLT 2021 Overcoming Catastrophic Forgetting by Bayesian Generative Regularization ICML 2021 Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics ICML 2021 Parallelised Diffeomorphic Sampling-based Motion Planning CoRL 2021 Particle-Gibbs Sampling for Bayesian Feature Allocation Models JMLR 2021 Pathwise Conditioning of Gaussian Processes JMLR 2021 Perception Beyond Sensors Under Uncertainty AAAI 2021 Permuton-induced Chinese Restaurant Process NeurIPS 2021 Posterior Promoted GAN With Distribution Discriminator for Unsupervised Image Synthesis CVPR 2021 Predicting Molecular Conformation via Dynamic Graph Score Matching NeurIPS 2021 Preferential Temporal Difference Learning ICML 2021 Probabilistic selection of inducing points in sparse Gaussian processes UAI 2021