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Stochastic Processes

2,667 papers

Papers per year

Papers

Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction NIPS 2021
Regularization in ResNet with Stochastic Depth NIPS 2021
A nonparametric method for gradual change problems with statistical guarantees NIPS 2021
Adaptive Machine Unlearning NIPS 2021
Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes NIPS 2021
Noisy Adaptation Generates Lévy Flights in Attractor Neural Networks NIPS 2021
NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform NIPS 2021
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels NIPS 2021
Risk-Aware Transfer in Reinforcement Learning using Successor Features NIPS 2021
Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling NIPS 2021
A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning NIPS 2021
Heavy Ball Neural Ordinary Differential Equations NIPS 2021
Efficient and Accurate Gradients for Neural SDEs NIPS 2021
Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance NIPS 2021
Understanding the Effect of Stochasticity in Policy Optimization NIPS 2021
Optimal Underdamped Langevin MCMC Method NIPS 2021
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise NIPS 2021
Predicting Molecular Conformation via Dynamic Graph Score Matching NIPS 2021
Time-independent Generalization Bounds for SGLD in Non-convex Settings NIPS 2021
Variational Inference for Continuous-Time Switching Dynamical Systems NIPS 2021
Neural Flows: Efficient Alternative to Neural ODEs NIPS 2021
Last iterate convergence of SGD for Least-Squares in the Interpolation regime. NIPS 2021
Model, sample, and epoch-wise descents: exact solution of gradient flow in the random feature model NIPS 2021
Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization NIPS 2021
On the Stochastic Stability of Deep Markov Models NIPS 2021