| 2025 | AAAI | Decentralized Convergence to Equilibrium Prices in Trading Networks. | Edwin Lock, Benjamin Patrick Evans, Eleonora Kreacic, Sujay Bhatt, Alec Koppel, Sumitra Ganesh, Paul W. Goldberg |
| 2025 | AISTATS | Approximate Equivariance in Reinforcement Learning. | Jung Yeon Park, Sujay Bhatt, Sihan Zeng, Lawson L. S. Wong, Alec Koppel, Sumitra Ganesh, Robin Walters |
| 2025 | AISTATS | Learning in Herding Mean Field Games: Single-Loop Algorithm with Finite-Time Convergence Analysis. | Sihan Zeng, Sujay Bhatt, Alec Koppel, Sumitra Ganesh |
| 2025 | ICASSP | Partially Observable Contextual Bandits With Linear Payoffs. | Sihan Zeng, Sujay Bhatt, Alec Koppel, Sumitra Ganesh |
| 2025 | ICLR | 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 |
| 2025 | ICLR | GenARM: Reward Guided Generation with Autoregressive Reward Model for Test-Time Alignment. | Yuancheng Xu, Udari Madhushani Sehwag, Alec Koppel, Sicheng Zhu, Bang An, Furong Huang, Sumitra Ganesh |
| 2025 | IROS | Confidence-Controlled Exploration: Efficient Sparse-Reward Policy Learning for Robot Navigation. | Bhrij Patel, Kasun Weerakoon, Wesley A. Suttle, Alec Koppel, Brian M. Sadler, Tianyi Zhou, Dinesh Manocha, Amrit Singh Bedi |
| 2024 | ACSSC | Bimodal Bandits: Max-Mean Regret Minimization. | Adit Jain, Sujay Bhatt, Vikram Krishnamurthy, Alec Koppel |
| 2024 | AISTATS | Sharpened Lazy Incremental Quasi-Newton Method. | Aakash Sunil Lahoti, Spandan Senapati, Ketan Rajawat, Alec Koppel |
| 2024 | ICLR | 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 |
| 2024 | ICLR | Efficient Inverse Multiagent Learning. | Denizalp Goktas, Amy Greenwald, Sadie Zhao, Alec Koppel, Sumitra Ganesh |
| 2024 | ICML | MaxMin-RLHF: Alignment with Diverse Human Preferences. | Souradip Chakraborty, Jiahao Qiu, Hui Yuan, Alec Koppel, Dinesh Manocha, Furong Huang, Amrit S. Bedi, Mengdi Wang |
| 2024 | ICML | Information-Directed Pessimism for Offline Reinforcement Learning. | Alec Koppel, Sujay Bhatt, Jiacheng Guo, Joe Eappen, Mengdi Wang, Sumitra Ganesh |
| 2024 | ICML | 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 |
| 2024 | WSC | Learning Payment-Free Resource Allocation Mechanisms. | Sihan Zeng, Sujay Bhatt, Eleonora Kreacic, Parisa Hassanzadeh, Alec Koppel, Sumitra Ganesh |
| 2023 | AAAI | 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 |
| 2023 | AISTATS | Oracle-free Reinforcement Learning in Mean-Field Games along a Single Sample Path. | Muhammad Aneeq uz Zaman, Alec Koppel, Sujay Bhatt, Tamer Basar |
| 2023 | CISS | Information-Directed Policy Search in Sparse-Reward Settings via the Occupancy Information Ratio. | Wesley A. Suttle, Alec Koppel, Ji Liu |
| 2023 | ICML | STEERING : Stein Information Directed Exploration for Model-Based Reinforcement Learning. | Souradip Chakraborty, Amrit S. Bedi, Alec Koppel, Mengdi Wang, Furong Huang, Dinesh Manocha |
| 2023 | ICML | 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 |
| 2023 | ICRA | 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 |
| 2023 | ICRA | Decentralized Multi-agent Exploration with Limited Inter-agent Communications. | Hans He, Alec Koppel, Amrit Singh Bedi, Daniel J. Stilwell, Mazen Farhood, Benjamin Biggs |
| 2022 | AAAI | Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach. | Qinbo Bai, Amrit Singh Bedi, Mridul Agarwal, Alec Koppel, Vaneet Aggarwal |
| 2022 | AAAI | Multi-Agent Reinforcement Learning with General Utilities via Decentralized Shadow Reward Actor-Critic. | Junyu Zhang, Amrit Singh Bedi, Mengdi Wang, Alec Koppel |
| 2022 | CISS | Policy Gradient for Ratio Optimization: A Case Study. | Wesley A. Suttle, Alec Koppel, Ji Liu |
| 2022 | ICASSP | On Submodular Set Cover Problems for Near-Optimal Online Kernel Basis Selection. | Hrusikesha Pradhan, Alec Koppel, Ketan Rajawat |
| 2022 | ICML | 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 |
| 2022 | ICML | Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood. | Qiujiang Jin, Alec Koppel, Ketan Rajawat, Aryan Mokhtari |
| 2022 | IROS | 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 |
| 2021 | ACSSC | Projected Pseudo-Mirror Descent in Reproducing Kernel Hilbert Space. | Abhishek Chakraborty, Ketan Rajawat, Alec Koppel |
| 2021 | ACSSC | Randomized Linear Programming for Tabular Average-Cost Multi-agent Reinforcement Learning. | Alec Koppel, Amrit Singh Bedi, Bhargav Ganguly, Vaneet Aggarwal |
| 2021 | ACSSC | Collaborative Beamforming for Agents with Localization Errors. | Erfaun Noorani, Yagiz Savas, Alec Koppel, John S. Baras, Ufuk Topcu, Brian M. Sadler |
| 2021 | ICASSP | A Dynamical Systems Perspective on Online Bayesian Nonparametric Estimators with Adaptive Hyperparameters. | Alec Koppel, Amrit S. Bedi, Vikram Krishnamurthy |
| 2021 | IROS | Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference. | Michael E. Kepler, Alec Koppel, Amrit Singh Bedi, Daniel J. Stilwell |
| 2020 | ACSSC | Conservative Multi-agent Online Kernel Learning in Heterogeneous Networks. | Hrusikesha Pradhan, Amrit Singh Bedi, Alec Koppel, Ketan Rajawat |
| 2020 | CISS | Reduced-rank Least Squares Parameter Estimation in the Presence of Byzantine Sensors. | Nagananda K. G, Rick S. Blum, Alec Koppel |
| 2020 | ICASSP | Balancing Rates and Variance via Adaptive Batch-Sizes in First-Order Stochastic Optimization. | Zhan Gao, Alec Koppel, Alejandro Ribeiro |
| 2020 | ICASSP | Projection Free Dynamic Online Learning. | Deepak S. Kalhan, Amrit S. Bedi, Alec Koppel, Ketan Rajawat, Abhishek K. Gupta, Adrish Banerjee |
| 2020 | IROS | Dense Incremental Metric-Semantic Mapping via Sparse Gaussian Process Regression. | Ehsan Zobeidi, Alec Koppel, Nikolay Atanasov |
| 2019 | ACSSC | Compressed Streaming Importance Sampling for Efficient Representations of Localization Distributions. | Amrit Singh Bedi, Alec Koppel, Brian M. Sadler, Vctor Elvira |
| 2019 | CISS | Policy Gradient using Weak Derivatives for Reinforcement Learning. | Sujay Bhatt, Alec Koppel, Vikram Krishnamurthy |
| 2019 | CISS | Policy Search in Infinite-Horizon Discounted Reinforcement Learning: Advances through Connections to Non-Convex Optimization : Invited Presentation. | Kaiqing Zhang, Alec Koppel, Hao Zhu, Tamer Basar |
| 2018 | ACSSC | Decentralized Online Nonparametric Learning. | Alec Koppel, Santiago Paternain, Cdric Richard, Alejandro Ribeiro |
| 2018 | ICASSP | Parallel Stochastic Successive Convex Approximation Method for Large-Scale Dictionary Learning. | Alec Koppel, Aryan Mokhtari, Alejandro Ribeiro |
| 2018 | IROS | Composable Learning with Sparse Kernel Representations. | Ekaterina I. Tolstaya, Ethan Stump, Alec Koppel, Alejandro Ribeiro |
| 2017 | ACSSC | Beyond consensus and synchrony in decentralized online optimization using saddle point method. | Amrit Singh Bedi, Alec Koppel, Ketan Rajawat |
| 2017 | ICASSP | Parsimonious Online Learning with Kernels via sparse projections in function space. | Alec Koppel, Garrett Warnell, Ethan Stump, Alejandro Ribeiro |
| 2017 | ICASSP | Large-scale nonconvex stochastic optimization by Doubly Stochastic Successive Convex approximation. | Aryan Mokhtari, Alec Koppel, Gesualdo Scutari, Alejandro Ribeiro |
| 2016 | ACSSC | Doubly stochastic algorithms for large-scale optimization. | Alec Koppel, Aryan Mokhtari, Alejandro Ribeiro |
| 2016 | ICASSP | Proximity without consensus in online multi-agent optimization. | Alec Koppel, Brian M. Sadler, Alejandro Ribeiro |
| 2016 | IROS | Online learning for characterizing unknown environments in ground robotic vehicle models. | Alec Koppel, Jonathan Fink, Garrett Warnell, Ethan Stump, Alejandro Ribeiro |
| 2015 | ACSSC | Task-driven dictionary learning in distributed online settings. | Alec Koppel, Garrett Warned, Ethan Stump |
| 2015 | ACSSC | Prediction-correction methods for time-varying convex optimization. | Andrea Simonetto, Alec Koppel, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro |
| 2015 | ICASSP | Regret bounds of a distributed saddle point algorithm. | Alec Koppel, Felicia Y. Jakubiec, Alejandro Ribeiro |
| 2015 | IROS | D4L: Decentralized dynamic discriminative dictionary learning. | Alec Koppel, Garrett Warnell, Ethan Stump, Alejandro Ribeiro |
| 2014 | ICASSP | A saddle point algorithm for networked online convex optimization. | Alec Koppel, Felicia Y. Jakubiec, Alejandro Ribeiro |