Stochastic Processes

Noisy Adaptation Generates Lévy Flights in Attractor Neural Networks NeurIPS 2021 Noisy Recurrent Neural Networks NeurIPS 2021 No MCMC for me: Amortized sampling for fast and stable training of energy-based models ICLR 2021 Non-Asymptotic Analysis for Two Time-scale TDC with General Smooth Function Approximation NeurIPS 2021 Non-asymptotic approximations of neural networks by Gaussian processes COLT 2021 Non-convex Distributionally Robust Optimization: Non-asymptotic Analysis NeurIPS 2021 Nonlinear Two-Time-Scale Stochastic Approximation: Convergence and Finite-Time Performance L4DC 2021 Nonparametric Hamiltonian Monte Carlo ICML 2021 Non-Parametric Stochastic Sequential Assignment With Random Arrival Times IJCAI 2021 Non-Volume Preserving Hamiltonian Monte Carlo and No-U-TurnSamplers AISTATS 2021 No-Regret Algorithms for Private Gaussian Process Bandit Optimization AISTATS 2021 Numerical Composition of Differential Privacy NeurIPS 2021 Off-Policy Confidence Sequences ICML 2021 On Empirical Risk Minimization with Dependent and Heavy-Tailed Data NeurIPS 2021 One-Sketch-for-All: Non-linear Random Features from Compressed Linear Measurements AISTATS 2021 Online Learning of a Probabilistic and Adaptive Scene Representation CVPR 2021 Online Multi-Armed Bandits with Adaptive Inference NeurIPS 2021 Online stochastic gradient descent on non-convex losses from high-dimensional inference JMLR 2021 On Multilevel Monte Carlo Unbiased Gradient Estimation for Deep Latent Variable Models AISTATS 2021 On Riemannian Stochastic Approximation Schemes with Fixed Step-Size AISTATS 2021 On Smoother Attributions using Neural Stochastic Differential Equations IJCAI 2021 On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method NeurIPS 2021 On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients ICML 2021 On the distributional properties of adaptive gradients UAI 2021 On the Impact of Random Seeds on the Fairness of Clinical Classifiers NAACL 2021