conftrace_

variance reduction

523 papers

Explore in graph

Also known as

VR

Co-occurring keywords

stochastic optimization (1060) stochastic gradient descent (1091) stochastic gradient (296) reinforcement learning (4352) policy gradient (520) nonconvex optimization (316) gradient estimation (150) non-convex optimization (547) importance sampling (318) control variate (54)

Papers

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML NIPS 2024
Jointly Improving the Sample and Communication Complexities in Decentralized Stochastic Minimax Optimization AAAI 2024
UNEX-RL: Reinforcing Long-Term Rewards in Multi-Stage Recommender Systems with UNidirectional EXecution AAAI 2024
Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems AISTATS 2024
Diagonalisation SGD: Fast & Convergent SGD for Non-Differentiable Models via Reparameterisation and Smoothing AISTATS 2024
Universality of AdaGrad Stepsizes for Stochastic Optimization: Inexact Oracle, Acceleration and Variance Reduction NIPS 2024
Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity NIPS 2024
Faster Stochastic Variance Reduction Methods for Compositional MiniMax Optimization AAAI 2024
Efficient Sign-Based Optimization: Accelerating Convergence via Variance Reduction NIPS 2024
Efficient Training of Language Models with Compact and Consistent Next Token Distributions ACL 2024
Decentralized Gradient-Free Methods for Stochastic Non-smooth Non-convex Optimization AAAI 2024
PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization JMLR 2024
Decentralized Sum-of-Nonconvex Optimization AAAI 2024
PrivSGP-VR: Differentially Private Variance-Reduced Stochastic Gradient Push with Tight Utility Bounds IJCAI 2024
DISCount: Counting in Large Image Collections with Detector-Based Importance Sampling AAAI 2024
Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraints JMLR 2024
Sample Complexity of Variance-Reduced Distributionally Robust Q-Learning JMLR 2024
On Differentially Private U Statistics NIPS 2024
On Divergence Measures for Training GFlowNets NIPS 2024
Trust Region Methods for Nonconvex Stochastic Optimization beyond Lipschitz Smoothness AAAI 2024
Adaptive Exploration for Data-Efficient General Value Function Evaluations NIPS 2024
A Bregman Proximal Stochastic Gradient Method with Extrapolation for Nonconvex Nonsmooth Problems AAAI 2024
Prior-dependent analysis of posterior sampling reinforcement learning with function approximation AISTATS 2024
Stochastic Frank-Wolfe: Unified Analysis and Zoo of Special Cases AISTATS 2024
GGD: Grafting Gradient Descent JMLR 2024