stochastic gradient

Robust Approximate Sampling via Stochastic Gradient Barker Dynamics AISTATS 2024 Single-Loop Stochastic Algorithms for Difference of Max-Structured Weakly Convex Functions NeurIPS 2024 Stochastic Taylor Derivative Estimator: Efficient amortization for arbitrary differential operators NeurIPS 2024 Towards Sharper Risk Bounds for Minimax Problems IJCAI 2024 Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets JMLR 2024 Universality of AdaGrad Stepsizes for Stochastic Optimization: Inexact Oracle, Acceleration and Variance Reduction NeurIPS 2024 ZO-AdaMU Optimizer: Adapting Perturbation by the Momentum and Uncertainty in Zeroth-Order Optimization AAAI 2024 Accelerated Zeroth-order Method for Non-Smooth Stochastic Convex Optimization Problem with Infinite Variance NeurIPS 2023 A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting NeurIPS 2023 A Mini-Block Fisher Method for Deep Neural Networks AISTATS 2023 A Variance-Reduced and Stabilized Proximal Stochastic Gradient Method with Support Identification Guarantees for Structured Optimization AISTATS 2023 Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization ICML 2023 Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data ICML 2023 Convergence of mean-field Langevin dynamics: time-space discretization, stochastic gradient, and variance reduction NeurIPS 2023 Cyclic Block Coordinate Descent With Variance Reduction for Composite Nonconvex Optimization ICML 2023 Exploiting Inferential Structure in Neural Processes UAI 2023 ISAAC Newton: Input-based Approximate Curvature for Newton's Method ICLR 2023 Knowledge Distillation Performs Partial Variance Reduction NeurIPS 2023 Linearly Constrained Bilevel Optimization: A Smoothed Implicit Gradient Approach ICML 2023 Lower Bounds and Accelerated Algorithms for Bilevel Optimization JMLR 2023 Mini-batching error and adaptive Langevin dynamics JMLR 2023 Mitigating Gradient Bias in Multi-objective Learning: A Provably Convergent Approach ICLR 2023 Online Learning for Non-monotone DR-Submodular Maximization: From Full Information to Bandit Feedback AISTATS 2023 On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network AISTATS 2023 On the Overlooked Structure of Stochastic Gradients NeurIPS 2023