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Stochastic Processes

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Papers

Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance NIPS 2021
Optimal Underdamped Langevin MCMC Method NIPS 2021
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise NIPS 2021
Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization NIPS 2021
Fast Doubly-Adaptive MCMC to Estimate the Gibbs Partition Function with Weak Mixing Time Bounds NIPS 2021
Generalized Linear Bandits with Local Differential Privacy NIPS 2021
Stochastic Multi-Armed Bandits with Control Variates NIPS 2021
Permuton-induced Chinese Restaurant Process NIPS 2021
Robustness between the worst and average case NIPS 2021
Batched Thompson Sampling NIPS 2021
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize NIPS 2021
MCMC Variational Inference via Uncorrected Hamiltonian Annealing NIPS 2021
Dangers of Bayesian Model Averaging under Covariate Shift NIPS 2021
Self-Adaptable Point Processes with Nonparametric Time Decays NIPS 2021
Noisy Recurrent Neural Networks NIPS 2021
Analysis of one-hidden-layer neural networks via the resolvent method NIPS 2021
Equilibrium and non-Equilibrium regimes in the learning of Restricted Boltzmann Machines NIPS 2021
Scaling Gaussian Processes with Derivative Information Using Variational Inference NIPS 2021
The future is log-Gaussian: ResNets and their infinite-depth-and-width limit at initialization NIPS 2021
A Biased Graph Neural Network Sampler with Near-Optimal Regret NIPS 2021
Latent Matters: Learning Deep State-Space Models NIPS 2021
Scalable Bayesian GPFA with automatic relevance determination and discrete noise models NIPS 2021
Regularization in ResNet with Stochastic Depth NIPS 2021
Noisy Adaptation Generates Lévy Flights in Attractor Neural Networks NIPS 2021
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels NIPS 2021