Optimization Research Papers in JMLR Volume 25

Nam Le
Sep 29, 2024

Table of Contents

Optimization Research Papers in JMLR Volume 25 (2024) #

This document lists papers from JMLR Volume 25 (2024) that focus on optimization research, categorized by their primary themes. Each paper is numbered starting from 1 within its subsection, with a brief description of its key contributions to optimization theory, algorithms, or applications.

Convex Optimization #

Papers addressing convex optimization problems, including sparse NMF, differential privacy, and sparse regression.

Lower Complexity Bounds of Finite-Sum Optimization Problems: The Results and Construction
Authors: Yuze Han, Guangzeng Xie, Zhihua Zhang
Description: Investigates lower complexity bounds for finite-sum optimization problems in convex settings.
Sparse NMF with Archetypal Regularization: Computational and Robustness Properties
Authors: Kayhan Behdin, Rahul Mazumder
Description: Proposes sparse non-negative matrix factorization with archetypal regularization using convex optimization.
Scaling the Convex Barrier with Sparse Dual Algorithms
Authors: Alessandro De Palma, Harkirat Singh Behl, Rudy Bunel, Philip H.S. Torr, M. Pawan Kumar
Description: Develops sparse dual algorithms for scaling convex optimization problems.
Faster Rates in Differentially Private Stochastic Convex Optimization
Authors: Jinyan Su, Lijie Hu, Di Wang
Description: Analyzes faster convergence rates for differentially private stochastic convex optimization.
Estimation of Sparse Gaussian Graphical Models with Hidden Clustering Structure
Authors: Meixia Lin, Defeng Sun, Kim-Chuan Toh, Chengjing Wang
Description: Develops convex optimization methods for sparse Gaussian graphical models with hidden clustering.
A Minimax Optimal Approach to High-Dimensional Double Sparse Linear Regression
Authors: Yanhang Zhang, Zhifan Li, Shixiang Liu, Jianxin Yin
Description: Proposes a minimax optimal approach for high-dimensional double sparse linear regression using convex optimization.
An Inexact Projected Regularized Newton Method for Fused Zero-Norms Regularization Problems
Authors: Yuqia Wu, Shaohua Pan, Xiaoqi Yang
Description: Introduces an inexact projected regularized Newton method for fused zero-norms regularization in convex optimization.

Nonconvex Optimization #

Papers tackling nonconvex optimization, focusing on ADMM, Adam-family methods, and stochastic minimax optimization.

Convergence for Nonconvex ADMM, with Applications to CT Imaging
Authors: Rina Foygel Barber, Emil Y. Sidky
Description: Studies convergence properties of nonconvex ADMM with applications to CT imaging.
Adam-Family Methods for Nonsmooth Optimization with Convergence Guarantees
Authors: Nachuan Xiao, Xiaoyin Hu, Xin Liu, Kim-Chuan Toh
Description: Develops Adam-family methods for nonsmooth nonconvex optimization with convergence guarantees.
Nonasymptotic Analysis of Stochastic Gradient Hamiltonian Monte Carlo under Local Conditions for Nonconvex Optimization
Authors: O. Deniz Akyildiz, Sotirios Sabanis
Description: Provides a nonasymptotic analysis of stochastic gradient Hamiltonian Monte Carlo for nonconvex optimization.
High Probability Convergence Bounds for Non-Convex Stochastic Gradient Descent with Sub-Weibull Noise
Authors: Liam Madden, Emiliano Dall’Anese, Stephen Becker
Description: Derives high-probability convergence bounds for nonconvex stochastic gradient descent with sub-Weibull noise.
Stochastic Regularized Majorization-Minimization with Weakly Convex and Multi-Convex Surrogates
Authors: Hanbaek Lyu
Description: Proposes stochastic regularized majorization-minimization for weakly convex and multi-convex problems.
Near-Optimal Algorithms for Stochastic Minimax Optimization
Authors: Lesi Chen, Luo Luo
Description: Develops near-optimal algorithms for stochastic minimax optimization in nonconvex settings.
Scaled Conjugate Gradient Method for Nonconvex Optimization in Deep Neural Networks
Authors: Naoki Sato, Koshiro Izumi, Hideaki Iiduka
Description: Introduces a scaled conjugate gradient method for nonconvex optimization in deep neural networks.

Stochastic Optimization #

Papers focusing on stochastic optimization methods, including continuous-time approximations, momentum, and curvature estimates.

A Comparison of Continuous-Time Approximations to Stochastic Gradient Descent
Authors: Stefan Ankirchner, Stefan Perko
Description: Compares continuous-time approximations to stochastic gradient descent for optimization.
On the Generalization of Stochastic Gradient Descent with Momentum
Authors: Ali Ramezani-Kebrya, Kimon Antonakopoulos, Volkan Cevher, Ashish Khisti, Ben Liang
Description: Analyzes the generalization properties of stochastic gradient descent with momentum.
Stochastic Modified Flows, Mean-Field Limits and Dynamics of Stochastic Gradient Descent
Authors: Benjamin Gess, Sebastian Kassing, Vitalii Konarovskyi
Description: Studies stochastic modified flows and mean-field limits for stochastic gradient descent dynamics.
Stochastic Approximation with Decision-Dependent Distributions: Asymptotic Normality and Optimality
Authors: Joshua Cutler, Mateo Díaz, Dmitriy Drusvyatskiy
Description: Investigates stochastic approximation with decision-dependent distributions, focusing on asymptotic normality and optimality.
An Algorithm with Optimal Dimension-Dependence for Zero-Order Nonsmooth Nonconvex Stochastic Optimization
Authors: Guy Kornowski, Ohad Shamir
Description: Proposes an algorithm with optimal dimension-dependence for zero-order nonsmooth nonconvex stochastic optimization.
On the Hyperparameters in Stochastic Gradient Descent with Momentum
Authors: Bin Shi
Description: Examines the impact of hyperparameters in stochastic gradient descent with momentum.
Almost Sure Convergence Rates Analysis and Saddle Avoidance of Stochastic Gradient Methods
Authors: Jun Liu, Ye Yuan
Description: Analyzes almost sure convergence rates and saddle avoidance in stochastic gradient methods.
PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates
Authors: Zachary Frangella, Pratik Rathore, Shipu Zhao, Madeleine Udell
Description: Introduces preconditioned stochastic optimization methods with scalable curvature estimates.
Zeroth-Order Stochastic Approximation Algorithms for DR-Submodular Optimization
Authors: Yuefang Lian, Xiao Wang, Dachuan Xu, Zhongrui Zhao
Description: Develops zeroth-order stochastic approximation algorithms for DR-submodular optimization.
Stochastic-Constrained Stochastic Optimization with Markovian Data
Authors: Yeongjong Kim, Dabeen Lee
Description: Studies stochastic-constrained optimization with Markovian data.
High Probability and Risk-Averse Guarantees for a Stochastic Accelerated Primal-Dual Method
Authors: Yassine Laguel, Necdet Serhat Aybat, Mert Gürbüzbalaban
Description: Provides high-probability and risk-averse guarantees for a stochastic accelerated primal-dual method.

Distributed/Decentralized Optimization #

Papers addressing distributed or decentralized optimization algorithms, focusing on communication efficiency and federated learning.

Distributed Gaussian Mean Estimation under Communication Constraints: Optimal Rates and Communication-Efficient Algorithms
Authors: T. Tony Cai, Hongji Wei
Description: Develops optimal rates and communication-efficient algorithms for distributed Gaussian mean estimation.
Accelerated Gradient Tracking over Time-Varying Graphs for Decentralized Optimization
Authors: Huan Li, Zhouchen Lin
Description: Proposes accelerated gradient tracking for decentralized optimization over time-varying graphs.
Compressed and Distributed Least-Squares Regression: Convergence Rates with Applications to Federated Learning
Authors: Constantin Philippenko, Aymeric Dieuleveut
Description: Analyzes convergence rates for compressed and distributed least-squares regression in federated learning.
Federated Automatic Differentiation
Authors: Keith Rush, Zachary Charles, Zachary Garrett
Description: Introduces federated automatic differentiation for distributed optimization.
A Random Projection Approach to Personalized Federated Learning: Enhancing Communication Efficiency, Robustness, and Fairness
Authors: Yuze Han, Xiang Li, Shiyun Lin, Zhihua Zhang
Description: Proposes a random projection approach to enhance communication efficiency in personalized federated learning.
Countering the Communication Bottleneck in Federated Learning: A Highly Efficient Zero-Order Optimization Technique
Authors: Elissa Mhanna, Mohamad Assaad
Description: Develops a zero-order optimization technique to address communication bottlenecks in federated learning.

Bandits and Online Learning #

Papers addressing multi-armed bandits, online optimization, and regret minimization.

Exploration, Exploitation, and Engagement in Multi-Armed Bandits with Abandonment
Authors: Zixian Yang, Xin Liu, Lei Ying
Description: Studies exploration, exploitation, and engagement in multi-armed bandits with abandonment.
Adaptivity and Non-Stationarity: Problem-Dependent Dynamic Regret for Online Convex Optimization
Authors: Peng Zhao, Yu-Jie Zhang, Lijun Zhang, Zhi-Hua Zhou
Description: Analyzes problem-dependent dynamic regret for online convex optimization under non-stationarity.
Materials Discovery Using Max K-Armed Bandit
Authors: Nobuaki Kikkawa, Hiroshi Ohno
Description: Applies max k-armed bandit algorithms to materials discovery, focusing on regret minimization.
Finite-Time Analysis of Globally Nonstationary Multi-Armed Bandits
Authors: Junpei Komiyama, Edouard Fouché, Junya Honda
Description: Provides finite-time analysis for globally nonstationary multi-armed bandits.
Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization
Authors: Sijia Chen, Yu-Jie Zhang, Wei-Wei Tu, Peng Zhao, Lijun Zhang
Description: Develops optimistic online mirror descent for bridging stochastic and adversarial online convex optimization.
Continuous Prediction with Experts’ Advice
Authors: Nicholas J. A. Harvey, Christopher Liaw, Victor S. Portella
Description: Investigates continuous prediction with experts’ advice in online learning settings.
Regret Analysis of Bilateral Trade with a Smoothed Adversary
Authors: Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Federico Fusco, Stefano Leonardi
Description: Analyzes regret in bilateral trade with a smoothed adversary in online optimization.
Optimal Learning Policies for Differential Privacy in Multi-Armed Bandits
Authors: Siwei Wang, Jun Zhu
Description: Develops optimal learning policies for differential privacy in multi-armed bandits.
Information Capacity Regret Bounds for Bandits with Mediator Feedback
Authors: Khaled Eldowa, Nicolò Cesa-Bianchi, Alberto Maria Metelli, Marcello Restelli
Description: Derives regret bounds for bandits with mediator feedback, focusing on information capacity.
Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression
Authors: Aleksandrs Slivkins, Xingyu Zhou, Karthik Abinav Sankararaman, Dylan J. Foster
Description: Proposes a modular Lagrangian approach for contextual bandits with packing and covering constraints.

Optimization in Reinforcement Learning #

Papers focusing on optimization techniques for reinforcement learning, including policy gradient, actor-critic, and safe RL.

Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization
Authors: Shicong Cen, Yuting Wei, Yuejie Chi
Description: Develops fast policy extragradient methods for competitive games with entropy regularization in RL.
Sample-Efficient Adversarial Imitation Learning
Authors: Dahuin Jung, Hyungyu Lee, Sungroh Yoon
Description: Proposes sample-efficient adversarial imitation learning methods for RL optimization.
On the Sample Complexity and Metastability of Heavy-Tailed Policy Search in Continuous Control
Authors: Amrit Singh Bedi, Anjaly Parayil, Junyu Zhang, Mengdi Wang, Alec Koppel
Description: Analyzes sample complexity and metastability for heavy-tailed policy search in continuous control.
Off-Policy Action Anticipation in Multi-Agent Reinforcement Learning
Authors: Ariyan Bighashdel, Daan de Geus, Pavol Jancura, Gijs Dubbelman
Description: Develops off-policy action anticipation methods for multi-agent RL optimization.
Policy Gradient Methods in the Presence of Symmetries and State Abstractions
Authors: Prakash Panangaden, Sahand Rezaei-Shoshtari, Rosie Zhao, David Meger, Doina Precup
Description: Investigates policy gradient methods with symmetries and state abstractions for RL optimization.
Log Barriers for Safe Black-Box Optimization with Application to Safe Reinforcement Learning
Authors: Ilnura Usmanova, Yarden As, Maryam Kamgarpour, Andreas Krause
Description: Proposes log barriers for safe black-box optimization with applications to safe RL.
Decentralized Natural Policy Gradient with Variance Reduction for Collaborative Multi-Agent Reinforcement Learning
Authors: Jinchi Chen, Jie Feng, Weiguo Gao, Ke Wei
Description: Develops decentralized natural policy gradient with variance reduction for multi-agent RL.
Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity
Authors: Laixi Shi, Yuejie Chi
Description: Studies distributionally robust model-based offline RL with near-optimal sample complexity.
Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds
Authors: Zhenghao Xu, Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao
Description: Analyzes sample complexity of neural policy mirror descent for policy optimization on low-dimensional manifolds.
Mean-Field Approximation of Cooperative Constrained Multi-Agent Reinforcement Learning (CMARL)
Authors: Washim Uddin Mondal, Vaneet Aggarwal, Satish V. Ukkusuri
Description: Proposes mean-field approximations for cooperative constrained multi-agent RL optimization.
Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning
Authors: Luofeng Liao, Zuyue Fu, Zhuoran Yang, Yixin Wang, Dingli Ma, Mladen Kolar, Zhaoran Wang
Description: Develops instrumental variable value iteration for causal offline RL optimization.
Matryoshka Policy Gradient for Entropy-Regularized RL: Convergence and Global Optimality
Authors: François G. Ged, Maria Han Veiga
Description: Introduces a Matryoshka policy gradient method for entropy-regularized RL with convergence guarantees.
Data-Efficient Policy Evaluation Through Behavior Policy Search
Authors: Josiah P. Hanna, Yash Chandak, Philip S. Thomas, Martha White, Peter Stone, Scott Niekum
Description: Proposes data-efficient policy evaluation methods for RL through behavior policy search.
Empirical Design in Reinforcement Learning
Authors: Andrew Patterson, Samuel Neumann, Martha White, Adam White
Description: Investigates empirical design strategies for optimization in reinforcement learning.
A New, Physics-Informed Continuous-Time Reinforcement Learning Algorithm with Performance Guarantees
Authors: Brent A. Wallace, Jennie Si
Description: Develops a physics-informed continuous-time RL algorithm with performance guarantees.