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2020 – today
- 2025
[j32]Amrit Singh Bedi, Dheeraj Peddireddy, Vaneet Aggarwal, Brian M. Sadler, Alec Koppel:
Regret and Belief Complexity Tradeoff in Gaussian Process Bandits via Information Thresholding. IEEE Trans. Artif. Intell. 6(3): 508-517 (2025)- [i64]Sihan Zeng, Sujay Bhatt, Alec Koppel, Sumitra Ganesh:
Regularized Proportional Fairness Mechanism for Resource Allocation Without Money. CoRR abs/2501.01111 (2025) - [i63]Boya Hou, Sina Sanjari, Nathan Dahlin, Alec Koppel, Subhonmesh Bose:
Nonparametric Sparse Online Learning of the Koopman Operator. CoRR abs/2501.16489 (2025) - [i62]Denizalp Goktas, Amy Greenwald, Sadie Zhao, Alec Koppel, Sumitra Ganesh:
Efficient Inverse Multiagent Learning. CoRR abs/2502.14160 (2025) - 2024
- [j31]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) - [j30]Wesley A. Suttle, Alec Koppel, Ji Liu
:
Occupancy Information Ratio: Infinite-Horizon, Information-Directed, Parameterized Policy Search. SIAM J. Control. Optim. 62(6): 3145-3171 (2024) - [j29]Alec Koppel, Joe Eappen, Sujay Bhatt, Cole Hawkins, Sumitra Ganesh:
Online MCMC Thinning with Kernelized Stein Discrepancy. SIAM J. Math. Data Sci. 6(1): 51-75 (2024) - [c79]Aakash Sunil Lahoti, Spandan Senapati, Ketan Rajawat, Alec Koppel:
Sharpened Lazy Incremental Quasi-Newton Method. AISTATS 2024: 4735-4743 - [c78]Xiaoqi Bi, Alec Koppel, Carolyn L. Beck:
Multi-layer Default Risk Contagion in Inter-banking Networks. CDC 2024: 5286-5291 - [c77]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 - [c76]Denizalp Goktas, Amy Greenwald, Sadie Zhao, Alec Koppel, Sumitra Ganesh:
Efficient Inverse Multiagent Learning. ICLR 2024 - [c75]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 - [c74]Alec Koppel, Sujay Bhatt, Jiacheng Guo, Joe Eappen, Mengdi Wang, Sumitra Ganesh:
Information-Directed Pessimism for Offline Reinforcement Learning. ICML 2024 - [c73]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 - [c72]Muhammad Aneeq uz Zaman, Mathieu Laurière, Alec Koppel, Tamer Basar:
Robust cooperative multi-agent reinforcement learning: A mean-field type game perspective. L4DC 2024: 770-783 - [c71]Sihan Zeng, Sujay Bhatt, Eleonora Kreacic, Parisa Hassanzadeh, Alec Koppel, Sumitra Ganesh:
Learning Payment-Free Resource Allocation Mechanisms. WSC 2024: 2667-2678 - [i61]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) - [i60]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) - [i59]Muhammad Aneeq uz Zaman, Alec Koppel, Mathieu Laurière, Tamer Basar:
Independent RL for Cooperative-Competitive Agents: A Mean-Field Perspective. CoRR abs/2403.11345 (2024) - [i58]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) - [i57]Boya Hou, Sina Sanjari, Alec Koppel, Subhonmesh Bose:
Compressed Online Learning of Conditional Mean Embedding. CoRR abs/2405.07432 (2024) - [i56]Muhammad Aneeq uz Zaman, Mathieu Laurière, Alec Koppel, Tamer Basar:
Robust Cooperative Multi-Agent Reinforcement Learning:A Mean-Field Type Game Perspective. CoRR abs/2406.13992 (2024) - [i55]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) - [i54]Sihan Zeng, Sujay Bhatt, Alec Koppel, Sumitra Ganesh:
Partially Observable Contextual Bandits with Linear Payoffs. CoRR abs/2409.11521 (2024) - [i53]Yuancheng Xu, Udari Madhushani Sehwag, Alec Koppel, Sicheng Zhu, Bang An, Furong Huang, Sumitra Ganesh:
GenARM: Reward Guided Generation with Autoregressive Reward Model for Test-time Alignment. CoRR abs/2410.08193 (2024) - [i52]Jung Yeon Park, Sujay Bhatt, Sihan Zeng, Lawson L. S. Wong, Alec Koppel, Sumitra Ganesh, Robin Walters:
Approximate Equivariance in Reinforcement Learning. CoRR abs/2411.04225 (2024) - [i51]Edwin Lock, Benjamin Patrick Evans, Eleonora Kreacic, Sujay Bhatt, Alec Koppel, Sumitra Ganesh, Paul W. Goldberg:
Decentralized Convergence to Equilibrium Prices in Trading Networks. CoRR abs/2412.13972 (2024) - 2023
- [j28]Harshat Kumar, Alec Koppel, Alejandro Ribeiro:
On the sample complexity of actor-critic method for reinforcement learning with function approximation. Mach. Learn. 112(7): 2433-2467 (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 - [c69]Muhammad Aneeq uz Zaman, Alec Koppel, Sujay Bhatt, Tamer Basar:
Oracle-free Reinforcement Learning in Mean-Field Games along a Single Sample Path. AISTATS 2023: 10178-10206 - [c68]Donghao Ying, Yuhao Ding, Alec Koppel, Javad Lavaei:
Scalable Multi-Agent Reinforcement Learning with General Utilities. ACC 2023: 3977-3982 - [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 - [c66]Wesley A. Suttle, Alec Koppel, Ji Liu:
Information-Directed Policy Search in Sparse-Reward Settings via the Occupancy Information Ratio. CISS 2023: 1-6 - [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 - [c61]Donghao Ying, Yunkai Zhang, Yuhao Ding, Alec Koppel, Javad Lavaei:
Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities. NeurIPS 2023 - [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) - [i48]Donghao Ying, Yuhao Ding, Alec Koppel, Javad Lavaei:
Scalable Multi-Agent Reinforcement Learning with General Utilities. CoRR abs/2302.07938 (2023) - [i47]Aakash Lahoti, Spandan Senapati, Ketan Rajawat, Alec Koppel:
Sharpened Lazy Incremental Quasi-Newton Method. CoRR abs/2305.17283 (2023) - [i46]Donghao Ying, Yunkai Zhang, Yuhao Ding, Alec Koppel, Javad Lavaei:
Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities. CoRR abs/2305.17568 (2023) - [i45]Yifan Yang, Alec Koppel, Zheng Zhang:
A Gradient-based Approach for Online Robust Deep Neural Network Training with Noisy Labels. CoRR abs/2306.05046 (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) - [i43]Zhan Gao, Aryan Mokhtari, Alec Koppel:
Limited-Memory Greedy Quasi-Newton Method with Non-asymptotic Superlinear Convergence Rate. CoRR abs/2306.15444 (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) - [i41]Jingxuan Zhu, Alec Koppel, Alvaro Velasquez, Ji Liu:
Byzantine-Resilient Decentralized Multi-Armed Bandits. CoRR abs/2310.07320 (2023) - [i40]Sihan Zeng, Sujay Bhatt, Eleonora Kreacic, Parisa Hassanzadeh, Alec Koppel, Sumitra Ganesh:
Near-Optimal Fair Resource Allocation for Strategic Agents without Money: A Data-Driven Approach. CoRR abs/2311.10927 (2023) - 2022
- [j27]Yagiz Savas, Erfaun Noorani, Alec Koppel, John S. Baras, Ufuk Topcu, Brian M. Sadler:
Collaborative one-shot beamforming under localization errors: A discrete optimization approach. Signal Process. 200: 108647 (2022) - [j26]Ehsan Zobeidi, Alec Koppel, Nikolay Atanasov:
Dense Incremental Metric-Semantic Mapping for Multiagent Systems via Sparse Gaussian Process Regression. IEEE Trans. Robotics 38(5): 3133-3153 (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) - [j24]Abhishek Chakraborty, Ketan Rajawat, Alec Koppel:
Sparse Representations of Positive Functions via First- and Second-Order Pseudo-Mirror Descent. IEEE Trans. Signal Process. 70: 3148-3164 (2022) - [j23]Zhan Gao, Alec Koppel, Alejandro Ribeiro:
Balancing Rates and Variance via Adaptive Batch-Size for Stochastic Optimization Problems. IEEE Trans. Signal Process. 70: 3693-3708 (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 - [c58]James Di, Ehsan Zobeidi, Alec Koppel, Nikolay Atanasov:
Distributed Gaussian Process Mapping for Robot Teams with Time-varying Communication. ACC 2022: 4458-4464 - [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 - [c56]Wesley A. Suttle, Alec Koppel, Ji Liu:
Policy Gradient for Ratio Optimization: A Case Study. CISS 2022: 281-286 - [c55]Hrusikesha Pradhan, Alec Koppel, Ketan Rajawat:
On Submodular Set Cover Problems for Near-Optimal Online Kernel Basis Selection. ICASSP 2022: 4168-4172 - [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 - [c53]Qiujiang Jin, Alec Koppel, Ketan Rajawat, Aryan Mokhtari:
Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood. ICML 2022: 10228-10250 - [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 - [i39]Cole Hawkins, Alec Koppel, Zheng Zhang:
Online, Informative MCMC Thinning with Kernelized Stein Discrepancy. CoRR abs/2201.07130 (2022) - [i38]Wesley A. Suttle, Alec Koppel, Ji Liu:
Occupancy Information Ratio: Infinite-Horizon, Information-Directed, Parameterized Policy Search. CoRR abs/2201.08832 (2022) - [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) - [i32]Muhammad Aneeq uz Zaman, Alec Koppel, Sujay Bhatt, Tamer Basar:
Oracle-free Reinforcement Learning in Mean-Field Games along a Single Sample Path. CoRR abs/2208.11639 (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) - [j21]Alec Koppel, Hrusikesha Pradhan, Ketan Rajawat:
Consistent online Gaussian process regression without the sample complexity bottleneck. Stat. Comput. 31(6): 76 (2021) - [j20]Alec Koppel, Garrett Warnell, Ethan Stump, Peter Stone, Alejandro Ribeiro:
Policy Evaluation in Continuous MDPs With Efficient Kernelized Gradient Temporal Difference. IEEE Trans. Autom. Control. 66(4): 1856-1863 (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) - [c51]Erfaun Noorani, Yagiz Savas, Alec Koppel, John S. Baras, Ufuk Topcu, Brian M. Sadler:
Collaborative Beamforming for Agents with Localization Errors. ACSCC 2021: 204-208 - [c50]Abhishek Chakraborty, Ketan Rajawat, Alec Koppel:
Projected Pseudo-Mirror Descent in Reproducing Kernel Hilbert Space. ACSCC 2021: 1008-1012 - [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 - [c45]Sujay Bhatt, Weichao Mao, Alec Koppel, Tamer Basar:
Semiparametric Information State Embedding for Policy Search under Imperfect Information. CDC 2021: 4501-4506 - [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 - [i31]Ekaterina I. Tolstaya, Ethan Stump, Alec Koppel, Alejandro Ribeiro:
Composable Learning with Sparse Kernel Representations. CoRR abs/2103.14474 (2021) - [i30]Ehsan Zobeidi, Alec Koppel, Nikolay Atanasov:
Dense Incremental Metric-Semantic Mapping for Multi-Agent Systems via Sparse Gaussian Process Regression. CoRR abs/2103.16170 (2021) - [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) - [i25]James Di, Ehsan Zobeidi, Alec Koppel, Nikolay Atanasov:
Distributed Gaussian Process Mapping for Robot Teams with Time-varying Communication. CoRR abs/2110.06401 (2021) - 2020
- [j15]Aryan Mokhtari, Alec Koppel, Martin Takác, Alejandro Ribeiro:
A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning. J. Mach. Learn. Res. 21: 120:1-120:51 (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) - [j13]Kaiqing Zhang, Alec Koppel, Hao Zhu, Tamer Basar:
Global Convergence of Policy Gradient Methods to (Almost) Locally Optimal Policies. SIAM J. Control. Optim. 58(6): 3586-3612 (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) - [j11]Aryan Mokhtari, Alec Koppel:
High-Dimensional Nonconvex Stochastic Optimization by Doubly Stochastic Successive Convex Approximation. IEEE Trans. Signal Process. 68: 6287-6302 (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 - [c40]Nagananda K. G, Rick S. Blum, Alec Koppel:
Reduced-rank Least Squares Parameter Estimation in the Presence of Byzantine Sensors. CISS 2020: 1-6 - [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 - [c38]Zhan Gao, Alec Koppel, Alejandro Ribeiro:
Balancing Rates and Variance via Adaptive Batch-Sizes in First-Order Stochastic Optimization. ICASSP 2020: 5385-5389 - [c37]Ehsan Zobeidi, Alec Koppel, Nikolay Atanasov:
Dense Incremental Metric-Semantic Mapping via Sparse Gaussian Process Regression. IROS 2020: 6180-6187 - [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) - [i21]Erfaun Noorani, Yagiz Savas, Alec Koppel, John S. Baras, Ufuk Topcu, Brian M. Sadler:
Distributed Beamforming for Agents with Localization Errors. CoRR abs/2003.12637 (2020) - [i20]Sujay Bhatt, Alec Koppel, Vikram Krishnamurthy:
Policy Gradient using Weak Derivatives for Reinforcement Learning. CoRR abs/2004.04843 (2020) - [i19]Alec Koppel, Hrusikesha Pradhan, Ketan Rajawat:
Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck. CoRR abs/2004.11094 (2020) - [i18]Zhan Gao, Alec Koppel, Alejandro Ribeiro:
Balancing Rates and Variance via Adaptive Batch-Size for Stochastic Optimization Problems. CoRR abs/2007.01219 (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) - [i16]Bingjia Wang, Alec Koppel, Vikram Krishnamurthy:
A Markov Decision Process Approach to Active Meta Learning. CoRR abs/2009.04950 (2020) - [i15]Abhishek Chakraborty, Ketan Rajawat, Alec Koppel:
Sparse Representations of Positive Functions via Projected Pseudo-Mirror Descent. CoRR abs/2011.07142 (2020)
2010 – 2019
- 2019
- [j10]Alec Koppel, Garrett Warnell, Ethan Stump, Alejandro Ribeiro:
Parsimonious Online Learning with Kernels via Sparse Projections in Function Space. J. Mach. Learn. Res. 20: 3:1-3:44 (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) - [j7]Alec Koppel, Kaiqing Zhang, Hao Zhu, Tamer Basar:
Projected Stochastic Primal-Dual Method for Constrained Online Learning With Kernels. IEEE Trans. Signal Process. 67(10): 2528-2542 (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 - [c33]Alec Koppel:
Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck. ACC 2019: 3512-3518 - [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 - [c30]Sujay Bhatt, Alec Koppel, Vikram Krishnamurthy:
Policy Gradient using Weak Derivatives for Reinforcement Learning. CDC 2019: 5531-5537 - [c29]Kaiqing Zhang, Alec Koppel, Hao Zhu, Tamer Basar:
Convergence and Iteration Complexity of Policy Gradient Method for Infinite-horizon Reinforcement Learning. CDC 2019: 7415-7422 - [c28]Sujay Bhatt, Alec Koppel, Vikram Krishnamurthy:
Policy Gradient using Weak Derivatives for Reinforcement Learning. CISS 2019: 1-3 - [c27]Kaiqing Zhang, Alec Koppel, Hao Zhu, Tamer Basar:
Policy Search in Infinite-Horizon Discounted Reinforcement Learning: Advances through Connections to Non-Convex Optimization : Invited Presentation. CISS 2019: 1-3 - [i14]Kaiqing Zhang, Alec Koppel, Hao Zhu, Tamer Basar:
Global Convergence of Policy Gradient Methods to (Almost) Locally Optimal Policies. CoRR abs/1906.08383 (2019) - [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) - [i10]Harshat Kumar, Alec Koppel, Alejandro Ribeiro:
On the Sample Complexity of Actor-Critic Method for Reinforcement Learning with Function Approximation. CoRR abs/1910.08412 (2019) - 2018
- [j6]Alec Koppel, Santiago Paternain, Cédric Richard, Alejandro Ribeiro:
Decentralized Online Learning With Kernels. IEEE Trans. Signal Process. 66(12): 3240-3255 (2018) - [c26]Brian Jalaian, Alec Koppel, Andre V. Harrison, James Michaelis, Stephen Russell:
On Stream-Centric Learning for Internet of Battlefield Things. AAAI Spring Symposia 2018 - [c25]Alec Koppel, Santiago Paternain, Cédric Richard, Alejandro Ribeiro:
Decentralized Online Nonparametric Learning. ACSSC 2018: 2139-2143 - [c24]Ekaterina I. Tolstaya, Alec Koppel, Ethan Stump, Alejandro Ribeiro:
Nonparametric Stochastic Compositional Gradient Descent for Q-Learning in Continuous Markov Decision Problems. ACC 2018: 6608-6615 - [c23]Amrit Singh Bedi, Alec Koppel, Ketan Rajawat:
Asynchronous Saddle Point Method: Interference Management Through Pricing. CDC 2018: 3229-3235 - [c22]Kaiqing Zhang, Hao Zhu, Tamer Basar, Alec Koppel:
Projected Stochastic Primal-Dual Method for Constrained Online Learning with Kernels. CDC 2018: 4224-4231 - [c21]Hrusikesha Pradhan, Amrit Singh Bedi, Alec Koppel, Ketan Rajawat:
Exact Nonparametric Decentralized Online Optimization. GlobalSIP 2018: 643-647 - [c20]Alec Koppel, Aryan Mokhtari, Alejandro Ribeiro:
Parallel Stochastic Successive Convex Approximation Method for Large-Scale Dictionary Learning. ICASSP 2018: 2771-2775 - [c19]Ekaterina I. Tolstaya, Ethan Stump, Alec Koppel, Alejandro Ribeiro:
Composable Learning with Sparse Kernel Representations. IROS 2018: 4622-4628 - [i9]Alec Koppel, Ekaterina I. Tolstaya, Ethan Stump, Alejandro Ribeiro:
Nonparametric Stochastic Compositional Gradient Descent for Q-Learning in Continuous Markov Decision Problems. CoRR abs/1804.07323 (2018) - 2017
- [j5]Andrea Simonetto, Alec Koppel, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro:
Decentralized Prediction-Correction Methods for Networked Time-Varying Convex Optimization. IEEE Trans. Autom. Control. 62(11): 5724-5738 (2017) - [j4]Alec Koppel, Garrett Warnell, Ethan Stump, Alejandro Ribeiro:
D4L: Decentralized Dynamic Discriminative Dictionary Learning. IEEE Trans. Signal Inf. Process. over Networks 3(4): 728-743 (2017) - [j3]Alec Koppel, Brian M. Sadler, Alejandro Ribeiro:
Proximity Without Consensus in Online Multiagent Optimization. IEEE Trans. Signal Process. 65(12): 3062-3077 (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 - [c17]Mahyar Fazlyab, Alec Koppel, Victor M. Preciado, Alejandro Ribeiro:
A variational approach to dual methods for constrained convex optimization. ACC 2017: 5269-5275 - [c16]Alec Koppel, Santiago Paternain, Cédric Richard, Alejandro Ribeiro:
Decentralized efficient nonparametric stochastic optimization. GlobalSIP 2017: 533-537 - [c15]Alec Koppel, Garrett Warnell, Ethan Stump, Alejandro Ribeiro:
Parsimonious Online Learning with Kernels via sparse projections in function space. ICASSP 2017: 4671-4675 - [c14]Aryan Mokhtari, Alec Koppel, Gesualdo Scutari, Alejandro Ribeiro:
Large-scale nonconvex stochastic optimization by Doubly Stochastic Successive Convex approximation. ICASSP 2017: 4701-4705 - [i8]Alec Koppel, Santiago Paternain, Cédric Richard, Alejandro Ribeiro:
Decentralized Online Learning with Kernels. CoRR abs/1710.04062 (2017) - 2016
- [j2]Andrea Simonetto, Aryan Mokhtari, Alec Koppel, Geert Leus, Alejandro Ribeiro:
A Class of Prediction-Correction Methods for Time-Varying Convex Optimization. IEEE Trans. Signal Process. 64(17): 4576-4591 (2016) - [c13]Alec Koppel, Aryan Mokhtari, Alejandro Ribeiro:
Doubly stochastic algorithms for large-scale optimization. ACSSC 2016: 1705-1709 - [c12]Aryan Mokhtari, Alec Koppel, Alejandro Ribeiro:
Doubly random parallel stochastic methods for large scale learning. ACC 2016: 4847-4852 - [c11]Andrea Simonetto, Alec Koppel, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro:
A Quasi-newton prediction-correction method for decentralized dynamic convex optimization. ECC 2016: 1934-1939 - [c10]Alec Koppel, Brian M. Sadler, Alejandro Ribeiro:
Decentralized online optimization with heterogeneous data sources. GlobalSIP 2016: 515-519 - [c9]Alec Koppel, Brian M. Sadler, Alejandro Ribeiro:
Proximity without consensus in online multi-agent optimization. ICASSP 2016: 3726-3730 - [c8]Alec Koppel, Jonathan Fink, Garrett Warnell, Ethan Stump, Alejandro Ribeiro:
Online learning for characterizing unknown environments in ground robotic vehicle models. IROS 2016: 626-633 - [i7]Andrea Simonetto, Alec Koppel, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro:
Decentralized Prediction-Correction Methods for Networked Time-Varying Convex Optimization. CoRR abs/1602.01716 (2016) - [i6]Aryan Mokhtari, Alec Koppel, Alejandro Ribeiro:
Doubly Random Parallel Stochastic Methods for Large Scale Learning. CoRR abs/1603.06782 (2016) - [i5]Alec Koppel, Garrett Warnell, Ethan Stump, Alejandro Ribeiro:
Decentralized Dynamic Discriminative Dictionary Learning. CoRR abs/1605.01107 (2016) - [i4]Aryan Mokhtari, Alec Koppel, Alejandro Ribeiro:
A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning. CoRR abs/1606.04991 (2016) - [i3]Alec Koppel, Brian M. Sadler, Alejandro Ribeiro:
Proximity Without Consensus in Online Multi-Agent Optimization. CoRR abs/1606.05578 (2016) - [i2]Alec Koppel, Garrett Warnell, Ethan Stump, Alejandro Ribeiro:
Parsimonious Online Learning with Kernels via Sparse Projections in Function Space. CoRR abs/1612.04111 (2016) - 2015
- [j1]Alec Koppel, Felicia Y. Jakubiec, Alejandro Ribeiro:
A Saddle Point Algorithm for Networked Online Convex Optimization. IEEE Trans. Signal Process. 63(19): 5149-5164 (2015) - [c7]Andrea Simonetto, Alec Koppel, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro:
Prediction-correction methods for time-varying convex optimization. ACSSC 2015: 666-670 - [c6]Alec Koppel, Garrett Warned, Ethan Stump:
Task-driven dictionary learning in distributed online settings. ACSSC 2015: 1114-1118 - [c5]Andrea Simonetto, Aryan Mokhtari, Alec Koppel, Geert Leus, Alejandro Ribeiro:
A decentralized prediction-correction method for networked time-varying convex optimization. CAMSAP 2015: 509-512 - [c4]Alec Koppel, Andrea Simonetto, Aryan Mokhtari, Geert Leus, Alejandro Ribeiro:
Target tracking with dynamic convex optimization. GlobalSIP 2015: 1210-1214 - [c3]Alec Koppel, Felicia Y. Jakubiec, Alejandro Ribeiro:
Regret bounds of a distributed saddle point algorithm. ICASSP 2015: 2969-2973 - [c2]Alec Koppel, Garrett Warnell, Ethan Stump, Alejandro Ribeiro:
D4L: Decentralized dynamic discriminative dictionary learning. IROS 2015: 2966-2973 - [i1]Andrea Simonetto, Aryan Mokhtari, Alec Koppel, Geert Leus, Alejandro Ribeiro:
A Class of Prediction-Correction Methods for Time-Varying Convex Optimization. CoRR abs/1509.05196 (2015) - 2014
- [c1]Alec Koppel, Felicia Y. Jakubiec, Alejandro Ribeiro:
A saddle point algorithm for networked online convex optimization. ICASSP 2014: 8292-8296
Coauthor Index
Amrit S. Bedi
aka: Amrit Singh Bedi
aka: Amrit Singh Bedi
[j32] [j31] [c77] [c75] [c73] [i61] [i60] [i58] [i55] [c70] [c67] [c65] [c64] [c63] [c62] [i50] [i49] [i44] [i42] [j25] [c60] [c59] [c57] [c54] [c52] [i37] [i36] [i35] [i34] [i33] [j22] [j19] [j18] [j17] [j16] [c49] [c48] [c47] [c46] [c44] [c43] [i29] [i28] [i27] [i26] [j14] [j12] [c42] [c41] [c39] [c36] [c35] [i24] [i23] [i22] [i17] [j9] [j8] [c34] [c32] [c31] [i13] [i12] [i11] [c23] [c21] [c18]
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