Stochastic Methods

Parameter Symmetry and Noise Equilibrium of Stochastic Gradient Descent NeurIPS 2024 Pareto Smoothed Importance Sampling JMLR 2024 Penalized Overdamped and Underdamped Langevin Monte Carlo Algorithms for Constrained Sampling JMLR 2024 PILOT: An O(1/K)-Convergent Approach for Policy Evaluation with Nonlinear Function Approximation ICLR 2024 Private Edge Density Estimation for Random Graphs: Optimal, Efficient and Robust NeurIPS 2024 Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions NeurIPS 2024 PrivSGP-VR: Differentially Private Variance-Reduced Stochastic Gradient Push with Tight Utility Bounds IJCAI 2024 Probability-Polarized Optimal Transport for Unsupervised Domain Adaptation AAAI 2024 Provably Faster Algorithms for Bilevel Optimization via Without-Replacement Sampling NeurIPS 2024 Quasi-Monte Carlo for 3D Sliced Wasserstein ICLR 2024 QUEST: Quality-Aware Metropolis-Hastings Sampling for Machine Translation NeurIPS 2024 Reciprocal Learning NeurIPS 2024 Relightable and Animatable Neural Avatar from Sparse-View Video CVPR 2024 RepAn: Enhanced Annealing through Re-parameterization CVPR 2024 Repelling Random Walks ICLR 2024 Reverse Transition Kernel: A Flexible Framework to Accelerate Diffusion Inference NeurIPS 2024 Revisiting the Last-Iterate Convergence of Stochastic Gradient Methods ICLR 2024 Risk-Conditioned Reinforcement Learning: A Generalized Approach for Adapting to Varying Risk Measures AAAI 2024 Robust Approximate Sampling via Stochastic Gradient Barker Dynamics AISTATS 2024 Robustly Train Normalizing Flows via KL Divergence Regularization AAAI 2024 Sample Efficient Reinforcement Learning with Partial Dynamics Knowledge AAAI 2024 Scalable DP-SGD: Shuffling vs. Poisson Subsampling NeurIPS 2024 Scaling Laws in Linear Regression: Compute, Parameters, and Data NeurIPS 2024 SDEs for Minimax Optimization AISTATS 2024 Second-Order Graph ODEs for Multi-Agent Trajectory Forecasting WACV 2024