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ORCIDAlec Koppel
- > Home > Persons > Alec Koppel
Publications
- 2024
[j30]Amrit Singh Bedi, Anjaly Parayil, Junyu Zhang, Mengdi Wang, Alec Koppel:
On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control. J. Mach. Learn. Res. 25: 39:1-39:58 (2024)- [c76]Souradip Chakraborty, Amrit S. Bedi, Alec Koppel, Huazheng Wang, Dinesh Manocha, Mengdi Wang, Furong Huang:
PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback. ICLR 2024 - [c74]Souradip Chakraborty, Jiahao Qiu, Hui Yuan, Alec Koppel, Dinesh Manocha, Furong Huang, Amrit S. Bedi, Mengdi Wang:
MaxMin-RLHF: Alignment with Diverse Human Preferences. ICML 2024 - [c72]Bhrij Patel, Wesley A. Suttle, Alec Koppel, Vaneet Aggarwal, Brian M. Sadler, Dinesh Manocha, Amrit S. Bedi:
Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles. ICML 2024 - [i57]Souradip Chakraborty, Jiahao Qiu, Hui Yuan, Alec Koppel, Furong Huang, Dinesh Manocha, Amrit Singh Bedi, Mengdi Wang
:
MaxMin-RLHF: Towards Equitable Alignment of Large Language Models with Diverse Human Preferences. CoRR abs/2402.08925 (2024) - [i56]Peihong Yu, Manav Mishra, Alec Koppel, Carl E. Busart, Priya Narayan, Dinesh Manocha, Amrit S. Bedi, Pratap Tokekar:
Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning. CoRR abs/2403.08936 (2024) - [i54]Bhrij Patel, Wesley A. Suttle, Alec Koppel, Vaneet Aggarwal, Brian M. Sadler, Amrit Singh Bedi, Dinesh Manocha:
Global Optimality without Mixing Time Oracles in Average-reward RL via Multi-level Actor-Critic. CoRR abs/2403.11925 (2024) - [i51]Mucong Ding, Souradip Chakraborty, Vibhu Agrawal, Zora Che, Alec Koppel, Mengdi Wang, Amrit S. Bedi, Furong Huang:
SAIL: Self-Improving Efficient Online Alignment of Large Language Models. CoRR abs/2406.15567 (2024) - 2023
- [c70]Souradip Chakraborty, Amrit Singh Bedi, Pratap Tokekar, Alec Koppel, Brian M. Sadler, Furong Huang, Dinesh Manocha:
Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning. AAAI 2023: 6980-6988 - [c67]Hans He, Alec Koppel, Amrit Singh Bedi, Mazen Farhood, Daniel J. Stilwell:
Bi-Level Nonstationary Kernels for Online Gaussian Process Regression. CASE 2023: 1-7 - [c65]Souradip Chakraborty, Amrit S. Bedi, Alec Koppel, Mengdi Wang, Furong Huang, Dinesh Manocha:
STEERING : Stein Information Directed Exploration for Model-Based Reinforcement Learning. ICML 2023: 3949-3978 - [c64]Wesley A. Suttle, Amrit S. Bedi, Bhrij Patel, Brian M. Sadler, Alec Koppel, Dinesh Manocha:
Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic. ICML 2023: 33240-33267 - [c63]Souradip Chakraborty, Amrit Singh Bedi, Kasun Weerakoon, Prithvi Poddar, Alec Koppel, Pratap Tokekar, Dinesh Manocha:
Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policy Optimization. ICRA 2023: 989-995 - [c62]Hans He, Alec Koppel, Amrit Singh Bedi, Daniel J. Stilwell, Mazen Farhood, Benjamin Biggs:
Decentralized Multi-agent Exploration with Limited Inter-agent Communications. ICRA 2023: 5530-5536 - [i50]Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Mengdi Wang, Furong Huang, Dinesh Manocha:
STEERING: Stein Information Directed Exploration for Model-Based Reinforcement Learning. CoRR abs/2301.12038 (2023) - [i49]Wesley A. Suttle, Amrit Singh Bedi, Bhrij Patel, Brian M. Sadler, Alec Koppel, Dinesh Manocha:
Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic. CoRR abs/2301.12083 (2023) - [i44]Bhrij Patel, Kasun Weerakoon, Wesley A. Suttle, Alec Koppel, Brian M. Sadler, Amrit Singh Bedi, Dinesh Manocha:
Ada-NAV: Adaptive Trajectory-Based Sample Efficient Policy Learning for Robotic Navigation. CoRR abs/2306.06192 (2023) - [i42]Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Dinesh Manocha, Huazheng Wang, Furong Huang, Mengdi Wang:
Aligning Agent Policy with Externalities: Reward Design via Bilevel RL. CoRR abs/2308.02585 (2023) - 2022
- [j25]Amrit Singh Bedi, Ketan Rajawat, Vaneet Aggarwal, Alec Koppel:
Escaping Saddle Points for Successive Convex Approximation. IEEE Trans. Signal Process. 70: 307-321 (2022) - [c60]Qinbo Bai, Amrit Singh Bedi, Mridul Agarwal, Alec Koppel, Vaneet Aggarwal:
Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach. AAAI 2022: 3682-3689 - [c59]Junyu Zhang, Amrit Singh Bedi, Mengdi Wang, Alec Koppel:
Multi-Agent Reinforcement Learning with General Utilities via Decentralized Shadow Reward Actor-Critic. AAAI 2022: 9031-9039 - [c57]Alec Koppel, Amrit Singh Bedi, Bhargav Ganguly, Vaneet Aggarwal:
Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming. CDC 2022: 4545-4552 - [c54]Amrit Singh Bedi, Souradip Chakraborty, Anjaly Parayil, Brian M. Sadler, Pratap Tokekar, Alec Koppel:
On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces. ICML 2022: 1716-1731 - [c52]Yulun Tian, Amrit Singh Bedi, Alec Koppel, Miguel Calvo-Fullana, David M. Rosen, Jonathan P. How:
Distributed Riemannian Optimization with Lazy Communication for Collaborative Geometric Estimation. IROS 2022: 4391-4398 - [i37]Amrit Singh Bedi, Souradip Chakraborty, Anjaly Parayil, Brian M. Sadler, Pratap Tokekar, Alec Koppel:
On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces. CoRR abs/2201.12332 (2022) - [i36]Yulun Tian, Amrit Singh Bedi, Alec Koppel, Miguel Calvo-Fullana, David M. Rosen, Jonathan P. How:
Distributed Riemannian Optimization with Lazy Communication for Collaborative Geometric Estimation. CoRR abs/2203.00851 (2022) - [i35]Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Brian M. Sadler, Furong Huang, Pratap Tokekar, Dinesh Manocha:
Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning. CoRR abs/2206.01162 (2022) - [i34]Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Pratap Tokekar, Dinesh Manocha:
Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policies. CoRR abs/2206.05652 (2022) - [i33]Amrit Singh Bedi, Chen Fan, Alec Koppel, Anit Kumar Sahu, Brian M. Sadler, Furong Huang, Dinesh Manocha:
FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus. CoRR abs/2206.10815 (2022) - 2021
- [j22]Junyu Zhang, Amrit Singh Bedi, Mengdi Wang, Alec Koppel:
Cautious Reinforcement Learning via Distributional Risk in the Dual Domain. IEEE J. Sel. Areas Inf. Theory 2(2): 611-626 (2021) - [j19]Hrusikesha Pradhan, Amrit Singh Bedi, Alec Koppel, Ketan Rajawat:
Adaptive Kernel Learning in Heterogeneous Networks. IEEE Trans. Signal Inf. Process. over Networks 7: 423-437 (2021) - [j18]Amrit Singh Bedi, Alec Koppel, Ketan Rajawat, Panchajanya Sanyal:
Nonparametric Compositional Stochastic Optimization for Risk-Sensitive Kernel Learning. IEEE Trans. Signal Process. 69: 428-442 (2021) - [j17]Deepak S. Kalhan, Amrit Singh Bedi, Alec Koppel, Ketan Rajawat, Hamed Hassani, Abhishek K. Gupta, Adrish Banerjee:
Dynamic Online Learning via Frank-Wolfe Algorithm. IEEE Trans. Signal Process. 69: 932-947 (2021) - [j16]Alec Koppel, Amrit Singh Bedi, Brian M. Sadler, Víctor Elvira:
Nearly Consistent Finite Particle Estimates in Streaming Importance Sampling. IEEE Trans. Signal Process. 69: 6401-6415 (2021) - [c49]Alec Koppel, Amrit Singh Bedi, Bhargav Ganguly, Vaneet Aggarwal:
Randomized Linear Programming for Tabular Average-Cost Multi-agent Reinforcement Learning. ACSCC 2021: 1023-1026 - [c48]Junyu Zhang, Amrit Singh Bedi, Mengdi Wang, Alec Koppel:
Beyond Cumulative Returns via Reinforcement Learning over State-Action Occupancy Measures. ACC 2021: 894-901 - [c47]Anjaly Parayil, Amrit Singh Bedi, Alec Koppel:
Joint Position and Beamforming Control via Alternating Nonlinear Least-Squares with a Hierarchical Gamma Prior. ACC 2021: 3513-3518 - [c46]Amrit Singh Bedi, Alec Koppel, Mengdi Wang, Junyu Zhang:
Intermittent Communications in Decentralized Shadow Reward Actor-Critic. CDC 2021: 2613-2620 - [c44]Alec Koppel, Amrit S. Bedi, Vikram Krishnamurthy:
A Dynamical Systems Perspective on Online Bayesian Nonparametric Estimators with Adaptive Hyperparameters. ICASSP 2021: 2975-2979 - [c43]Michael E. Kepler, Alec Koppel, Amrit Singh Bedi, Daniel J. Stilwell:
Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference. IROS 2021: 9833-9840 - [i29]Junyu Zhang, Amrit Singh Bedi, Mengdi Wang, Alec Koppel:
MARL with General Utilities via Decentralized Shadow Reward Actor-Critic. CoRR abs/2106.00543 (2021) - [i28]Amrit Singh Bedi, Anjaly Parayil, Junyu Zhang, Mengdi Wang, Alec Koppel:
On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control. CoRR abs/2106.08414 (2021) - [i27]Michael E. Kepler, Alec Koppel, Amrit Singh Bedi, Daniel J. Stilwell:
Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference. CoRR abs/2107.12797 (2021) - [i26]Qinbo Bai, Amrit Singh Bedi, Mridul Agarwal, Alec Koppel, Vaneet Aggarwal:
Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach. CoRR abs/2109.06332 (2021) - 2020
- [j14]Yulun Tian, Alec Koppel, Amrit Singh Bedi, Jonathan P. How:
Asynchronous and Parallel Distributed Pose Graph Optimization. IEEE Robotics Autom. Lett. 5(4): 5819-5826 (2020) - [j12]Alec Koppel, Amrit Singh Bedi, Ketan Rajawat, Brian M. Sadler:
Optimally Compressed Nonparametric Online Learning: Tradeoffs between memory and consistency. IEEE Signal Process. Mag. 37(3): 61-70 (2020) - [c42]Hrusikesha Pradhan, Amrit Singh Bedi, Alec Koppel, Ketan Rajawat:
Conservative Multi-agent Online Kernel Learning in Heterogeneous Networks. ACSSC 2020: 53-57 - [c41]Amrit Singh Bedi, Alec Koppel, Ketan Rajawat, Brian M. Sadler:
Trading Dynamic Regret for Model Complexity in Nonstationary Nonparametric Optimization. ACC 2020: 321-326 - [c39]Deepak S. Kalhan, Amrit S. Bedi, Alec Koppel, Ketan Rajawat, Abhishek K. Gupta, Adrish Banerjee:
Projection Free Dynamic Online Learning. ICASSP 2020: 3957-3961 - [c36]Amrit Singh Bedi, Dheeraj Peddireddy, Vaneet Aggarwal, Alec Koppel:
Efficient Large-Scale Gaussian Process Bandits by Believing only Informative Actions. L4DC 2020: 924-934 - [c35]Junyu Zhang, Alec Koppel, Amrit Singh Bedi, Csaba Szepesvári, Mengdi Wang:
Variational Policy Gradient Method for Reinforcement Learning with General Utilities. NeurIPS 2020 - [i24]Junyu Zhang, Amrit Singh Bedi, Mengdi Wang, Alec Koppel:
Cautious Reinforcement Learning via Distributional Risk in the Dual Domain. CoRR abs/2002.12475 (2020) - [i23]Yulun Tian, Alec Koppel, Amrit Singh Bedi, Jonathan P. How:
Asynchronous and Parallel Distributed Pose Graph Optimization. CoRR abs/2003.03281 (2020) - [i22]Amrit Singh Bedi, Dheeraj Peddireddy, Vaneet Aggarwal, Alec Koppel:
Efficient Gaussian Process Bandits by Believing only Informative Actions. CoRR abs/2003.10550 (2020) - [i17]Junyu Zhang, Alec Koppel, Amrit Singh Bedi, Csaba Szepesvári, Mengdi Wang:
Variational Policy Gradient Method for Reinforcement Learning with General Utilities. CoRR abs/2007.02151 (2020) - 2019
- [j9]Amrit Singh Bedi, Alec Koppel, Ketan Rajawat:
Asynchronous Online Learning in Multi-Agent Systems With Proximity Constraints. IEEE Trans. Signal Inf. Process. over Networks 5(3): 479-494 (2019) - [j8]Amrit Singh Bedi, Alec Koppel, Ketan Rajawat:
Asynchronous Saddle Point Algorithm for Stochastic Optimization in Heterogeneous Networks. IEEE Trans. Signal Process. 67(7): 1742-1757 (2019) - [c34]Amrit Singh Bedi, Alec Koppel, Brian M. Sadler, Víctor Elvira:
Compressed Streaming Importance Sampling for Efficient Representations of Localization Distributions. ACSSC 2019: 477-481 - [c32]Alec Koppel, Amrit S. Bedi, Ketan Rajawat:
Controlling the Bias-Variance Tradeoff via Coherent Risk for Robust Learning with Kernels. ACC 2019: 3519-3525 - [c31]Rishabh Dixit, Amrit Singh Bedi, Ketan Rajawat, Alec Koppel:
Distributed Online Learning over Time-varying Graphs via Proximal Gradient Descent. CDC 2019: 2745-2751 - [i13]Amrit Singh Bedi, Alec Koppel, Ketan Rajawat, Brian M. Sadler:
Nonstationary Nonparametric Online Learning: Balancing Dynamic Regret and Model Parsimony. CoRR abs/1909.05442 (2019) - [i12]Alec Koppel, Amrit Singh Bedi, Victor Elvira, Brian M. Sadler:
Approximate Shannon Sampling in Importance Sampling: Nearly Consistent Finite Particle Estimates. CoRR abs/1909.10279 (2019) - [i11]Alec Koppel, Amrit Singh Bedi, Ketan Rajawat, Brian M. Sadler:
Optimally Compressed Nonparametric Online Learning. CoRR abs/1909.11555 (2019) - 2018
- [c23]Amrit Singh Bedi, Alec Koppel, Ketan Rajawat:
Asynchronous Saddle Point Method: Interference Management Through Pricing. CDC 2018: 3229-3235 - [c21]Hrusikesha Pradhan, Amrit Singh Bedi, Alec Koppel, Ketan Rajawat:
Exact Nonparametric Decentralized Online Optimization. GlobalSIP 2018: 643-647 - 2017
- [c18]Amrit Singh Bedi, Alec Koppel, Ketan Rajawat:
Beyond consensus and synchrony in decentralized online optimization using saddle point method. ACSSC 2017: 293-297
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