ICML 2024 "stochastic optimization" Papers
22 papers found
Accelerated Policy Gradient for s-rectangular Robust MDPs with Large State Spaces
Ziyi Chen, Heng Huang
A Federated Stochastic Multi-level Compositional Minimax Algorithm for Deep AUC Maximization
Xinwen Zhang, Ali Payani, Myungjin Lee et al.
Dealing With Unbounded Gradients in Stochastic Saddle-point Optimization
Gergely Neu, Nneka Okolo
High-Probability Bound for Non-Smooth Non-Convex Stochastic Optimization with Heavy Tails
Langqi Liu, Yibo Wang, Lijun Zhang
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise
Eduard Gorbunov, Abdurakhmon Sadiev, Marina Danilova et al.
How Free is Parameter-Free Stochastic Optimization?
Amit Attia, Tomer Koren
Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds
Daniel Dodd, Louis Sharrock, Chris Nemeth
Locally Differentially Private Decentralized Stochastic Bilevel Optimization with Guaranteed Convergence Accuracy
Ziqin Chen, Yongqiang Wang
Nonlinear Filtering with Brenier Optimal Transport Maps
Mohammad Al-Jarrah, Niyizhen Jin, Bamdad Hosseini et al.
No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths
Charles Guille-Escuret, Hiroki Naganuma, Kilian Fatras et al.
On The Complexity of First-Order Methods in Stochastic Bilevel Optimization
Jeongyeol Kwon, Dohyun Kwon, Hanbaek Lyu
PASOA- PArticle baSed Bayesian Optimal Adaptive design
Jacopo Iollo, Christophe Heinkelé, Pierre Alliez et al.
Position: Future Directions in the Theory of Graph Machine Learning
Christopher Morris, Fabrizio Frasca, Nadav Dym et al.
Revisiting Inexact Fixed-Point Iterations for Min-Max Problems: Stochasticity and Structured Nonconvexity
Ahmet Alacaoglu, Donghwan Kim, Stephen Wright
Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features
Aleksandr Beznosikov, David Dobre, Gauthier Gidel
Stability and Generalization for Stochastic Recursive Momentum-based Algorithms for (Strongly-)Convex One to $K$-Level Stochastic Optimizations
Xiaokang Pan, Xingyu Li, Jin Liu et al.
Stochastic Optimization with Arbitrary Recurrent Data Sampling
William Powell, Hanbaek Lyu
Stochastic Weakly Convex Optimization beyond Lipschitz Continuity
Wenzhi Gao, Qi Deng
Tuning-Free Stochastic Optimization
Ahmed Khaled, Chi Jin
Understanding Stochastic Natural Gradient Variational Inference
Kaiwen Wu, Jacob Gardner
Variational Linearized Laplace Approximation for Bayesian Deep Learning
Luis A. Ortega, Simon Rodriguez Santana, Daniel Hernández-Lobato
Zeroth-Order Methods for Constrained Nonconvex Nonsmooth Stochastic Optimization
Zhuanghua Liu, Cheng Chen, Luo Luo et al.