Task-driven dictionary learning in distributed online settings. - Alec Koppel; Garrett Warned; Ethan Stump (ACSSC, 2015)

Prediction-correction methods for time-varying convex optimization. - Andrea Simonetto; Alec Koppel; Aryan Mokhtari; Geert Leus; Alejandro Ribeiro (ACSSC, 2015)

A decentralized prediction-correction method for networked time-varying convex optimization. - Andrea Simonetto; Aryan Mokhtari; Alec Koppel; Geert Leus; Alejandro Ribeiro (CAMSAP, 2015)

Target tracking with dynamic convex optimization. - Alec Koppel; Andrea Simonetto; Aryan Mokhtari; Geert Leus; Alejandro Ribeiro (GlobalSIP, 2015)

Regret bounds of a distributed saddle point algorithm. - Alec Koppel; Felicia Y. Jakubiec; Alejandro Ribeiro (ICASSP, 2015)

A Saddle Point Algorithm for Networked Online Convex Optimization. - Alec Koppel; Felicia Y. Jakubiec; Alejandro Ribeiro (IEEE Trans. Signal Process., 2015)

Doubly stochastic algorithms for large-scale optimization. - Alec Koppel; Aryan Mokhtari; Alejandro Ribeiro (ACSSC, 2016)

Doubly random parallel stochastic methods for large scale learning. - Aryan Mokhtari; Alec Koppel; Alejandro Ribeiro (ACC, 2016)

A Quasi-newton prediction-correction method for decentralized dynamic convex optimization. - Andrea Simonetto; Alec Koppel; Aryan Mokhtari; Geert Leus; Alejandro Ribeiro (ECC, 2016)

Decentralized online optimization with heterogeneous data sources. - Alec Koppel; Brian M. Sadler; Alejandro Ribeiro (GlobalSIP, 2016)

Proximity without consensus in online multi-agent optimization. - Alec Koppel; Brian M. Sadler; Alejandro Ribeiro (ICASSP, 2016)

Online learning for characterizing unknown environments in ground robotic vehicle models. - Alec Koppel; Jonathan Fink; Garrett Warnell; Ethan Stump; Alejandro Ribeiro (IROS, 2016)

Decentralized Dynamic Discriminative Dictionary Learning. - Alec Koppel; Garrett Warnell; Ethan Stump; Alejandro Ribeiro (CoRR, 2016)

A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning. - Aryan Mokhtari; Alec Koppel; Alejandro Ribeiro (CoRR, 2016)

A Class of Prediction-Correction Methods for Time-Varying Convex Optimization. - Andrea Simonetto; Aryan Mokhtari; Alec Koppel; Geert Leus; Alejandro Ribeiro (IEEE Trans. Signal Process., 2016)

Beyond consensus and synchrony in decentralized online optimization using saddle point method. - Amrit Singh Bedi; Alec Koppel; Ketan Rajawat (ACSSC, 2017)

A variational approach to dual methods for constrained convex optimization. - Mahyar Fazlyab; Alec Koppel; Victor M. Preciado; Alejandro Ribeiro (ACC, 2017)

Decentralized efficient nonparametric stochastic optimization. - Alec Koppel; Santiago Paternain; Cédric Richard; Alejandro Ribeiro (GlobalSIP, 2017)

Large-scale nonconvex stochastic optimization by Doubly Stochastic Successive Convex approximation. - Aryan Mokhtari; Alec Koppel; Gesualdo Scutari; Alejandro Ribeiro (ICASSP, 2017)

Decentralized Prediction-Correction Methods for Networked Time-Varying Convex Optimization. - Andrea Simonetto; Alec Koppel; Aryan Mokhtari; Geert Leus; Alejandro Ribeiro (IEEE Trans. Autom. Control., 2017)

D4L: Decentralized Dynamic Discriminative Dictionary Learning. - Alec Koppel; Garrett Warnell; Ethan Stump; Alejandro Ribeiro (IEEE Trans. Signal Inf. Process. over Networks, 2017)

Proximity Without Consensus in Online Multiagent Optimization. - Alec Koppel; Brian M. Sadler; Alejandro Ribeiro (IEEE Trans. Signal Process., 2017)

On Stream-Centric Learning for Internet of Battlefield Things. - Brian Jalaian; Alec Koppel; Andre V. Harrison; James Michaelis; Stephen Russell 001 (AAAI Spring Symposia, 2018)

Decentralized Online Nonparametric Learning. - Alec Koppel; Santiago Paternain; Cédric Richard; Alejandro Ribeiro (ACSSC, 2018)

Nonparametric Stochastic Compositional Gradient Descent for Q-Learning in Continuous Markov Decision Problems. - Ekaterina I. Tolstaya; Alec Koppel; Ethan Stump; Alejandro Ribeiro (ACC, 2018)

Asynchronous Saddle Point Method: Interference Management Through Pricing. - Amrit Singh Bedi; Alec Koppel; Ketan Rajawat (CDC, 2018)

Exact Nonparametric Decentralized Online Optimization. - Hrusikesha Pradhan; Amrit Singh Bedi; Alec Koppel; Ketan Rajawat (GlobalSIP, 2018)

Parallel Stochastic Successive Convex Approximation Method for Large-Scale Dictionary Learning. - Alec Koppel; Aryan Mokhtari; Alejandro Ribeiro (ICASSP, 2018)

Composable Learning with Sparse Kernel Representations. - Ekaterina I. Tolstaya; Ethan Stump; Alec Koppel; Alejandro Ribeiro (IROS, 2018)

Decentralized Online Learning With Kernels. - Alec Koppel; Santiago Paternain; Cédric Richard; Alejandro Ribeiro (IEEE Trans. Signal Process., 2018)

Compressed Streaming Importance Sampling for Efficient Representations of Localization Distributions. - Amrit Singh Bedi; Alec Koppel; Brian M. Sadler; Víctor Elvira (ACSSC, 2019)

Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck. - Alec Koppel (ACC, 2019)

Controlling the Bias-Variance Tradeoff via Coherent Risk for Robust Learning with Kernels. - Alec Koppel; Amrit S. Bedi; Ketan Rajawat (ACC, 2019)

Policy Gradient using Weak Derivatives for Reinforcement Learning. - Sujay Bhatt; Alec Koppel; Vikram Krishnamurthy (CDC, 2019)

Distributed Online Learning over Time-varying Graphs via Proximal Gradient Descent. - Rishabh Dixit; Amrit Singh Bedi; Ketan Rajawat; Alec Koppel (CDC, 2019)

Convergence and Iteration Complexity of Policy Gradient Method for Infinite-horizon Reinforcement Learning. - Kaiqing Zhang; Alec Koppel; Hao Zhu 001; Tamer Basar (CDC, 2019)

Policy Search in Infinite-Horizon Discounted Reinforcement Learning: Advances through Connections to Non-Convex Optimization : Invited Presentation. - Kaiqing Zhang; Alec Koppel; Hao Zhu 001; Tamer Basar (CISS, 2019)

Nonstationary Nonparametric Online Learning: Balancing Dynamic Regret and Model Parsimony. - Amrit Singh Bedi; Alec Koppel; Ketan Rajawat; Brian M. Sadler (CoRR, 2019)

Approximate Shannon Sampling in Importance Sampling: Nearly Consistent Finite Particle Estimates. - Alec Koppel; Amrit Singh Bedi; Victor Elvira; Brian M. Sadler (CoRR, 2019)

Optimally Compressed Nonparametric Online Learning. - Alec Koppel; Amrit Singh Bedi; Ketan Rajawat; Brian M. Sadler (CoRR, 2019)

Parsimonious Online Learning with Kernels via Sparse Projections in Function Space. - Alec Koppel; Garrett Warnell; Ethan Stump; Alejandro Ribeiro (J. Mach. Learn. Res., 2019)

Asynchronous Online Learning in Multi-Agent Systems With Proximity Constraints. - Amrit Singh Bedi; Alec Koppel; Ketan Rajawat (IEEE Trans. Signal Inf. Process. over Networks, 2019)

Asynchronous Saddle Point Algorithm for Stochastic Optimization in Heterogeneous Networks. - Amrit Singh Bedi; Alec Koppel; Ketan Rajawat (IEEE Trans. Signal Process., 2019)

Projected Stochastic Primal-Dual Method for Constrained Online Learning With Kernels. - Alec Koppel; Kaiqing Zhang; Hao Zhu 001; Tamer Basar (IEEE Trans. Signal Process., 2019)

Conservative Multi-agent Online Kernel Learning in Heterogeneous Networks. - Hrusikesha Pradhan; Amrit Singh Bedi; Alec Koppel; Ketan Rajawat (ACSSC, 2020)

Trading Dynamic Regret for Model Complexity in Nonstationary Nonparametric Optimization. - Amrit Singh Bedi; Alec Koppel; Ketan Rajawat; Brian M. Sadler (ACC, 2020)

Reduced-rank Least Squares Parameter Estimation in the Presence of Byzantine Sensors. - Nagananda K. G; Rick S. Blum; Alec Koppel (CISS, 2020)

Balancing Rates and Variance via Adaptive Batch-Sizes in First-Order Stochastic Optimization. - Zhan Gao; Alec Koppel; Alejandro Ribeiro (ICASSP, 2020)

Projection Free Dynamic Online Learning. - Deepak S. Kalhan; Amrit S. Bedi; Alec Koppel; Ketan Rajawat; Abhishek K. Gupta; Adrish Banerjee (ICASSP, 2020)

Dense Incremental Metric-Semantic Mapping via Sparse Gaussian Process Regression. - Ehsan Zobeidi; Alec Koppel; Nikolay Atanasov 001 (IROS, 2020)

Efficient Large-Scale Gaussian Process Bandits by Believing only Informative Actions. - Amrit Singh Bedi; Dheeraj Peddireddy; Vaneet Aggarwal; Alec Koppel (L4DC, 2020)

Variational Policy Gradient Method for Reinforcement Learning with General Utilities. - Junyu Zhang; Alec Koppel; Amrit Singh Bedi; Csaba Szepesvári; Mengdi Wang (NeurIPS, 2020)

Efficient Gaussian Process Bandits by Believing only Informative Actions. - Amrit Singh Bedi; Dheeraj Peddireddy; Vaneet Aggarwal; Alec Koppel (CoRR, 2020)

Distributed Beamforming for Agents with Localization Errors. - Erfaun Noorani; Yagiz Savas; Alec Koppel; John S. Baras; Ufuk Topcu; Brian M. Sadler (CoRR, 2020)

A Markov Decision Process Approach to Active Meta Learning. - Bingjia Wang; Alec Koppel; Vikram Krishnamurthy (CoRR, 2020)

Sparse Representations of Positive Functions via Projected Pseudo-Mirror Descent. - Abhishek Chakraborty; Ketan Rajawat; Alec Koppel (CoRR, 2020)

A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning. - Aryan Mokhtari; Alec Koppel; Martin Takác 001; Alejandro Ribeiro (J. Mach. Learn. Res., 2020)

Asynchronous and Parallel Distributed Pose Graph Optimization. - Yulun Tian; Alec Koppel; Amrit Singh Bedi; Jonathan P. How (IEEE Robotics Autom. Lett., 2020)

Global Convergence of Policy Gradient Methods to (Almost) Locally Optimal Policies. - Kaiqing Zhang; Alec Koppel; Hao Zhu 001; Tamer Basar (SIAM J. Control. Optim., 2020)

Optimally Compressed Nonparametric Online Learning: Tradeoffs between memory and consistency. - Alec Koppel; Amrit Singh Bedi; Ketan Rajawat; Brian M. Sadler (IEEE Signal Process. Mag., 2020)

High-Dimensional Nonconvex Stochastic Optimization by Doubly Stochastic Successive Convex Approximation. - Aryan Mokhtari; Alec Koppel (IEEE Trans. Signal Process., 2020)

Projected Pseudo-Mirror Descent in Reproducing Kernel Hilbert Space. - Abhishek Chakraborty; Ketan Rajawat; Alec Koppel (ACSCC, 2021)

Randomized Linear Programming for Tabular Average-Cost Multi-agent Reinforcement Learning. - Alec Koppel; Amrit Singh Bedi; Bhargav Ganguly; Vaneet Aggarwal (ACSCC, 2021)

Collaborative Beamforming for Agents with Localization Errors. - Erfaun Noorani; Yagiz Savas; Alec Koppel; John S. Baras; Ufuk Topcu; Brian M. Sadler (ACSCC, 2021)

Joint Position and Beamforming Control via Alternating Nonlinear Least-Squares with a Hierarchical Gamma Prior. - Anjaly Parayil; Amrit Singh Bedi; Alec Koppel (ACC, 2021)

Beyond Cumulative Returns via Reinforcement Learning over State-Action Occupancy Measures. - Junyu Zhang; Amrit Singh Bedi; Mengdi Wang; Alec Koppel (ACC, 2021)

Intermittent Communications in Decentralized Shadow Reward Actor-Critic. - Amrit Singh Bedi; Alec Koppel; Mengdi Wang; Junyu Zhang (CDC, 2021)

Semiparametric Information State Embedding for Policy Search under Imperfect Information. - Sujay Bhatt; Weichao Mao; Alec Koppel; Tamer Basar (CDC, 2021)

A Dynamical Systems Perspective on Online Bayesian Nonparametric Estimators with Adaptive Hyperparameters. - Alec Koppel; Amrit S. Bedi; Vikram Krishnamurthy (ICASSP, 2021)

Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference. - Michael E. Kepler; Alec Koppel; Amrit Singh Bedi; Daniel J. Stilwell (IROS, 2021)

Dense Incremental Metric-Semantic Mapping for Multi-Agent Systems via Sparse Gaussian Process Regression. - Ehsan Zobeidi; Alec Koppel; Nikolay Atanasov 001 (CoRR, 2021)

MARL with General Utilities via Decentralized Shadow Reward Actor-Critic. - Junyu Zhang; Amrit Singh Bedi; Mengdi Wang; Alec Koppel (CoRR, 2021)

Cautious Reinforcement Learning via Distributional Risk in the Dual Domain. - Junyu Zhang; Amrit Singh Bedi; Mengdi Wang; Alec Koppel (IEEE J. Sel. Areas Inf. Theory, 2021)

Consistent online Gaussian process regression without the sample complexity bottleneck. - Alec Koppel; Hrusikesha Pradhan; Ketan Rajawat (Stat. Comput., 2021)

Policy Evaluation in Continuous MDPs With Efficient Kernelized Gradient Temporal Difference. - Alec Koppel; Garrett Warnell; Ethan Stump; Peter Stone 001; Alejandro Ribeiro (IEEE Trans. Autom. Control., 2021)

Adaptive Kernel Learning in Heterogeneous Networks. - Hrusikesha Pradhan; Amrit Singh Bedi; Alec Koppel; Ketan Rajawat (IEEE Trans. Signal Inf. Process. over Networks, 2021)

Nonparametric Compositional Stochastic Optimization for Risk-Sensitive Kernel Learning. - Amrit Singh Bedi; Alec Koppel; Ketan Rajawat; Panchajanya Sanyal (IEEE Trans. Signal Process., 2021)

Dynamic Online Learning via Frank-Wolfe Algorithm. - Deepak S. Kalhan; Amrit Singh Bedi; Alec Koppel; Ketan Rajawat; Hamed Hassani; Abhishek K. Gupta; Adrish Banerjee (IEEE Trans. Signal Process., 2021)

Nearly Consistent Finite Particle Estimates in Streaming Importance Sampling. - Alec Koppel; Amrit Singh Bedi; Brian M. Sadler; Víctor Elvira (IEEE Trans. Signal Process., 2021)

Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach. - Qinbo Bai; Amrit Singh Bedi; Mridul Agarwal; Alec Koppel; Vaneet Aggarwal (AAAI, 2022)

Multi-Agent Reinforcement Learning with General Utilities via Decentralized Shadow Reward Actor-Critic. - Junyu Zhang; Amrit Singh Bedi; Mengdi Wang; Alec Koppel (AAAI, 2022)

Distributed Gaussian Process Mapping for Robot Teams with Time-varying Communication. - James Di; Ehsan Zobeidi; Alec Koppel; Nikolay Atanasov 001 (ACC, 2022)

Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming. - Alec Koppel; Amrit Singh Bedi; Bhargav Ganguly; Vaneet Aggarwal (CDC, 2022)

Policy Gradient for Ratio Optimization: A Case Study. - Wesley A. Suttle; Alec Koppel; Ji Liu 001 (CISS, 2022)

On Submodular Set Cover Problems for Near-Optimal Online Kernel Basis Selection. - Hrusikesha Pradhan; Alec Koppel; Ketan Rajawat (ICASSP, 2022)

On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces. - Amrit Singh Bedi; Souradip Chakraborty; Anjaly Parayil; Brian M. Sadler; Pratap Tokekar; Alec Koppel (ICML, 2022)

Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood. - Qiujiang Jin; Alec Koppel; Ketan Rajawat; Aryan Mokhtari (ICML, 2022)

Distributed Riemannian Optimization with Lazy Communication for Collaborative Geometric Estimation. - Yulun Tian; Amrit Singh Bedi; Alec Koppel; Miguel Calvo-Fullana; David M. Rosen; Jonathan P. How (IROS, 2022)

Online, Informative MCMC Thinning with Kernelized Stein Discrepancy. - Cole Hawkins; Alec Koppel; Zheng Zhang 005 (CoRR, 2022)

Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policies. - Souradip Chakraborty; Amrit Singh Bedi; Alec Koppel; Pratap Tokekar; Dinesh Manocha (CoRR, 2022)

FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus. - Amrit Singh Bedi; Chen Fan; Alec Koppel; Anit Kumar Sahu; Brian M. Sadler; Furong Huang; Dinesh Manocha (CoRR, 2022)

Collaborative one-shot beamforming under localization errors: A discrete optimization approach. - Yagiz Savas; Erfaun Noorani; Alec Koppel; John S. Baras; Ufuk Topcu; Brian M. Sadler (Signal Process., 2022)

Dense Incremental Metric-Semantic Mapping for Multiagent Systems via Sparse Gaussian Process Regression. - Ehsan Zobeidi; Alec Koppel; Nikolay Atanasov 001 (IEEE Trans. Robotics, 2022)

Escaping Saddle Points for Successive Convex Approximation. - Amrit Singh Bedi; Ketan Rajawat; Vaneet Aggarwal; Alec Koppel (IEEE Trans. Signal Process., 2022)

Sparse Representations of Positive Functions via First- and Second-Order Pseudo-Mirror Descent. - Abhishek Chakraborty; Ketan Rajawat; Alec Koppel (IEEE Trans. Signal Process., 2022)

Balancing Rates and Variance via Adaptive Batch-Size for Stochastic Optimization Problems. - Zhan Gao; Alec Koppel; Alejandro Ribeiro (IEEE Trans. Signal Process., 2022)

Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning. - Souradip Chakraborty; Amrit Singh Bedi; Pratap Tokekar; Alec Koppel; Brian M. Sadler; Furong Huang; Dinesh Manocha (AAAI, 2023)

Oracle-free Reinforcement Learning in Mean-Field Games along a Single Sample Path. - Muhammad Aneeq uz Zaman; Alec Koppel; Sujay Bhatt; Tamer Basar (AISTATS, 2023)

Scalable Multi-Agent Reinforcement Learning with General Utilities. - Donghao Ying; Yuhao Ding; Alec Koppel; Javad Lavaei (ACC, 2023)

Bi-Level Nonstationary Kernels for Online Gaussian Process Regression. - Hans He; Alec Koppel; Amrit Singh Bedi; Mazen Farhood; Daniel J. Stilwell (CASE, 2023)

Information-Directed Policy Search in Sparse-Reward Settings via the Occupancy Information Ratio. - Wesley A. Suttle; Alec Koppel; Ji Liu 001 (CISS, 2023)

STEERING : Stein Information Directed Exploration for Model-Based Reinforcement Learning. - Souradip Chakraborty; Amrit S. Bedi; Alec Koppel; Mengdi Wang; Furong Huang; Dinesh Manocha (ICML, 2023)

Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic. - Wesley A. Suttle; Amrit S. Bedi; Bhrij Patel; Brian M. Sadler; Alec Koppel; Dinesh Manocha (ICML, 2023)

Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policy Optimization. - Souradip Chakraborty; Amrit Singh Bedi; Kasun Weerakoon; Prithvi Poddar; Alec Koppel; Pratap Tokekar; Dinesh Manocha (ICRA, 2023)

Decentralized Multi-agent Exploration with Limited Inter-agent Communications. - Hans He; Alec Koppel; Amrit Singh Bedi; Daniel J. Stilwell; Mazen Farhood; Benjamin Biggs (ICRA, 2023)

Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities. - Donghao Ying; Yunkai Zhang 002; Yuhao Ding; Alec Koppel; Javad Lavaei (NeurIPS, 2023)

A Gradient-based Approach for Online Robust Deep Neural Network Training with Noisy Labels. - Yifan Yang; Alec Koppel; Zheng Zhang 005 (CoRR, 2023)

Ada-NAV: Adaptive Trajectory-Based Sample Efficient Policy Learning for Robotic Navigation. - Bhrij Patel; Kasun Weerakoon; Wesley A. Suttle; Alec Koppel; Brian M. Sadler; Amrit Singh Bedi; Dinesh Manocha (CoRR, 2023)

Limited-Memory Greedy Quasi-Newton Method with Non-asymptotic Superlinear Convergence Rate. - Zhan Gao; Aryan Mokhtari; Alec Koppel (CoRR, 2023)

Aligning Agent Policy with Externalities: Reward Design via Bilevel RL. - Souradip Chakraborty; Amrit Singh Bedi; Alec Koppel; Dinesh Manocha; Huazheng Wang; Furong Huang; Mengdi Wang (CoRR, 2023)

Near-Optimal Fair Resource Allocation for Strategic Agents without Money: A Data-Driven Approach. - Sihan Zeng; Sujay Bhatt; Eleonora Kreacic; Parisa Hassanzadeh; Alec Koppel; Sumitra Ganesh (CoRR, 2023)

On the sample complexity of actor-critic method for reinforcement learning with function approximation. - Harshat Kumar; Alec Koppel; Alejandro Ribeiro (Mach. Learn., 2023)

Bimodal Bandits: Max-Mean Regret Minimization. - Adit Jain; Sujay Bhatt; Vikram Krishnamurthy; Alec Koppel (IEEECONF, 2024)

Sharpened Lazy Incremental Quasi-Newton Method. - Aakash Sunil Lahoti; Spandan Senapati; Ketan Rajawat; Alec Koppel (AISTATS, 2024)

Multi-layer Default Risk Contagion in Inter-banking Networks. - Xiaoqi Bi; Alec Koppel; Carolyn L. Beck (CDC, 2024)

PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback. - Souradip Chakraborty; Amrit Singh Bedi; Alec Koppel; Huazheng Wang; Dinesh Manocha; Mengdi Wang; Furong Huang (ICLR, 2024)

Efficient Inverse Multiagent Learning. - Denizalp Goktas; Amy Greenwald; Sadie Zhao; Alec Koppel; Sumitra Ganesh (ICLR, 2024)

MaxMin-RLHF: Alignment with Diverse Human Preferences. - Souradip Chakraborty; Jiahao Qiu; Hui Yuan 002; Alec Koppel; Dinesh Manocha; Furong Huang; Amrit S. Bedi; Mengdi Wang (ICML, 2024)

Information-Directed Pessimism for Offline Reinforcement Learning. - Alec Koppel; Sujay Bhatt; Jiacheng Guo; Joe Eappen; Mengdi Wang; Sumitra Ganesh (ICML, 2024)

Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles. - Bhrij Patel; Wesley A. Suttle; Alec Koppel; Vaneet Aggarwal; Brian M. Sadler; Dinesh Manocha; Amrit S. Bedi (ICML, 2024)

Robust cooperative multi-agent reinforcement learning: A mean-field type game perspective. - Muhammad Aneeq uz Zaman; Mathieu Laurière; Alec Koppel; Tamer Basar (L4DC, 2024)

Learning Payment-Free Resource Allocation Mechanisms. - Sihan Zeng; Sujay Bhatt; Eleonora Kreacic; Parisa Hassanzadeh; Alec Koppel; Sumitra Ganesh (WSC, 2024)

MaxMin-RLHF: Towards Equitable Alignment of Large Language Models with Diverse Human Preferences. - Souradip Chakraborty; Jiahao Qiu; Hui Yuan 002; Alec Koppel; Furong Huang; Dinesh Manocha; Amrit Singh Bedi; Mengdi Wang (CoRR, 2024)

Independent RL for Cooperative-Competitive Agents: A Mean-Field Perspective. - Muhammad Aneeq uz Zaman; Alec Koppel; Mathieu Laurière; Tamer Basar (CoRR, 2024)

Global Optimality without Mixing Time Oracles in Average-reward RL via Multi-level Actor-Critic. - Bhrij Patel; Wesley A. Suttle; Alec Koppel; Vaneet Aggarwal; Brian M. Sadler; Amrit Singh Bedi; Dinesh Manocha (CoRR, 2024)

Compressed Online Learning of Conditional Mean Embedding. - Boya Hou; Sina Sanjari; Alec Koppel; Subhonmesh Bose (CoRR, 2024)

Robust Cooperative Multi-Agent Reinforcement Learning:A Mean-Field Type Game Perspective. - Muhammad Aneeq uz Zaman; Mathieu Laurière; Alec Koppel; Tamer Basar (CoRR, 2024)

SAIL: Self-Improving Efficient Online Alignment of Large Language Models. - Mucong Ding; Souradip Chakraborty; Vibhu Agrawal; Zora Che; Alec Koppel; Mengdi Wang; Amrit S. Bedi; Furong Huang (CoRR, 2024)

On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control. - Amrit Singh Bedi; Anjaly Parayil; Junyu Zhang; Mengdi Wang; Alec Koppel (J. Mach. Learn. Res., 2024)

Occupancy Information Ratio: Infinite-Horizon, Information-Directed, Parameterized Policy Search. - Wesley A. Suttle; Alec Koppel; Ji Liu 001 (SIAM J. Control. Optim., 2024)

Regularized Proportional Fairness Mechanism for Resource Allocation Without Money. - Sihan Zeng; Sujay Bhatt; Alec Koppel; Sumitra Ganesh (Trans. Mach. Learn. Res., 2024)

Byzantine-Resilient Decentralized Multi-Armed Bandits. - Jingxuan Zhu; Alec Koppel; Alvaro Velasquez; Ji Liu 001 (Trans. Mach. Learn. Res., 2024)

Online MCMC Thinning with Kernelized Stein Discrepancy. - Alec Koppel; Joe Eappen; Sujay Bhatt; Cole Hawkins; Sumitra Ganesh (SIAM J. Math. Data Sci., 2024)

Decentralized Convergence to Equilibrium Prices in Trading Networks. - Edwin Lock; Benjamin Patrick Evans; Eleonora Kreacic; Sujay Bhatt; Alec Koppel; Sumitra Ganesh; Paul W. Goldberg (AAAI, 2025)

Approximate Equivariance in Reinforcement Learning. - Jung Yeon Park; Sujay Bhatt; Sihan Zeng; Lawson L. S. Wong; Alec Koppel; Sumitra Ganesh; Robin Walters 001 (AISTATS, 2025)

Learning in Herding Mean Field Games: Single-Loop Algorithm with Finite-Time Convergence Analysis. - Sihan Zeng; Sujay Bhatt; Alec Koppel; Sumitra Ganesh (AISTATS, 2025)

Partially Observable Contextual Bandits With Linear Payoffs. - Sihan Zeng; Sujay Bhatt; Alec Koppel; Sumitra Ganesh (ICASSP, 2025)

Collab: Controlled Decoding using Mixture of Agents for LLM Alignment. - Souradip Chakraborty; Sujay Bhatt; Udari Madhushani Sehwag; Soumya Suvra Ghosal; Jiahao Qiu; Mengdi Wang; Dinesh Manocha; Furong Huang; Alec Koppel; Sumitra Ganesh (ICLR, 2025)

GenARM: Reward Guided Generation with Autoregressive Reward Model for Test-Time Alignment. - Yuancheng Xu; Udari Madhushani Sehwag; Alec Koppel; Sicheng Zhu; Bang An 001; Furong Huang; Sumitra Ganesh (ICLR, 2025)

Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning. - Peihong Yu; Manav Mishra; Alec Koppel; Carl E. Busart; Priya Narayan; Dinesh Manocha; Amrit Singh Bedi; Pratap Tokekar (Trans. Mach. Learn. Res., 2025)

Regret and Belief Complexity Tradeoff in Gaussian Process Bandits via Information Thresholding. - Amrit Singh Bedi; Dheeraj Peddireddy; Vaneet Aggarwal; Brian M. Sadler; Alec Koppel (IEEE Trans. Artif. Intell., 2025)

Decentralized Upper Confidence Bound Algorithms for Homogeneous Multi-agent Multi-armed Bandits. - Jingxuan Zhu; Ethan Mulle; Christopher Salomon Smith; Alec Koppel; Ji Liu 001 (IEEE Trans. Autom. Control., 2025)