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Publications

Journal Papers


    Submissions Under Review

  1. B. Patel, K. Mudiyanselage, W. A. Suttle, A. Koppel, B. Sadler, T. Zhou, A. S. Bedi, D. Manocha, `` Ada-NAV: Adaptive Trajectory Length-Based Sample Efficient Policy Learning for Robotic Navigation ," Submitted to IEEE Robotics and Automation Letters (RA-L) , preprint as arXiv2306.06192v5.


  2. J. Zhu, E. Mulle, C. Smith, A. Koppel, and J. Liu, ``Decentralized Upper Confidence Bound Algorithms for Homogeneous Multi-Agent Multi-Armed Bandits," Submitted to IEEE Trans. Automatic Control, (Conditional Acceptance) (2024).


  3. S. Zeng, S. Bhatt, A. Koppel, and S. Ganesh, ``Design Without Money: Trading Social Welfare for Incentive Compatibility ," Transactions on Machine Learning Research (TMLR, under review). Preprint as arXiv 2311.10927v1. (2024). Short version appeared in EC 2024 Workshop: Computational Methods for Economic Dynamics.


  4. P. Yu, M. Mishra, A. Koppel, C. Busart, P. Narayan, D. Manocha, A. S. Bedi, P. Tokekar, ``Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning ," in Transactions on Machine Learning Research (TMLR, under review - Major Revision) Preprint as arXiv:2206.10815 (2024).


  5. A. S. Bedi, C. Fan, A. Koppel, A. K. Sahu, B. M. Sadler, F. Huang, D. Manocha, ``FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus ," arXiv preprint arXiv:2206.10815 (2022).


  6. A. S. Bedi, A. Koppel, K. Rajawat, and B.M. Sadler. ``Nonstationary Nonparametric Online Learning: Balancing Dynamic Regret and Model Parsimony,” in IEEE Trans. Signal Processing (under review), Sept. 2019


    7. 2024

  7. W. A. Suttle, A. Koppel, J. Liu, ``Occupancy Information Ratio: Infinite-Horizon, Information-Directed, Parameterized Policy Search ," arXiv preprint arXiv:2201.08832 (2024). SIAM Journal on Control and Optimization (to appear), Nov. 2024


  8. A. Koppel, J. Eappen, S. Bhatt, C. Hawkins, S. Ganesh, ``Online MCMC Thinning with Kernelized Stein Discrepancy" in SIAM Journal on Mathematics of Data Science (SIMODS). Available as ArXiv 2201.07130


  9. J. Zhu, A. Koppel, A. Velasquez, and J. Liu, ``Byzantine-Resilient Decentralized Multi-Armed Bandits," under review at Transactions on Machine Learning Research (TMLR),(to appear), (2024).


  10. A. S. Bedi, A. Parayil, J. Zhang, M. Wang, A. Koppel, ``On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control," Journal of Machine Learning Research, Jan. 2024.




    11. 2023

  11. A. Bedi, D. Peddireddy, V. Aggarwal, and A. Koppel,`` Sublinear Regret and Belief Complexity in Gaussian Process Bandits via Information Thresholding" in 2024 IEEE Transactions on Artificial Intelligence Aug. pp. 1-10, vol. 1 DOI Bookmark: 10.1109/TAI.2023.3332023, preprint available as arXiv 2003.10550.


  12. Q. Bai, A. S. Bedi, M. Agarwal, A. Koppel, V. Aggarwal,``Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach," in Journal of Artificial Intelligence Research (JAIR), Dec. 2023.


  13. H. Kumar, A. Koppel, and A. Ribeiro. ``On the Sample Complexity of Actor-Critic Method for Reinforcement Learning with Function Approximation" in Machine Learning, Springer, Feb 2023.


    14. 2022

  14. E. Noorani, Y. Savas, A. Koppel, J. Baras, U. Topcu, and B. M. Sadler,``Collaborative Beamforming Under Localization Errors: A Discrete Optimization Approach,” Elsevier Signal Processing, Volume 200, Nov. 2022, 108647


  15. E. Zobeidi, A. Koppel, and N. Atanasov, ``Dense Incremental Metric-Semantic Mapping via Sparse Gaussian Process Regression,” in IEEE Transactions on Robotics (T-RO), DOI: 10.1109/TRO.2022.3168733, May 2022.


  16. Z. Gao, A. Koppel, and A. Ribeiro,``Balancing Rates and Variance via Adaptive Batch-Size for Stochastic Optimization Problems,” IEEE Transactions on Signal Processing. DOI: 10.1109/TSP.2022.3186526, Jun. 2022


  17. A. Chakraborty, K. Rajawat, A. Koppel, ``Sparse Representations of Positive Functions via First and Second-Order Pseudo-Mirror Descent ," IEEE Transactions on Signal Processing , vol. 70, pp. 3148-3164, DOI: 10.1109/TSP.2022.3173146. May 2022

  18. A.S. Bedi, K. Rajawat, V. Aggarwal, and A. Koppel. ``Escaping Saddle Points in Successive Convex Approximation" in IEEE Trans. Signal Processing Volume: 70, pp. 307-321 , Issue: 3, May 2022. DOI: 10.1109/TSP.2021.3138242

  19. A. Koppel, A. S. Bedi, V. Elvira, and B.M. Sadler. ``Nearly Consistent Finite Particle Estimates in Streaming Importance Sampling,” in IEEE Trans. Signal Processing , 2022. DOI: 10.1109/TSP.2021.3120512


    20. 2021

  20. A. Koppel, H. Pradhan, K. Rajawat,``Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck,” in Statistics and Computing (Springer), Sept. 2021. [Code]


  21. J. Zhang, A. S. Bedi, M. Wang, and A. Koppel. ``Cautious Reinforcement Learning via Distributional Risk in the Dual Domain " in IEEE Journal on Selected Areas in Information Theory: Special Issue on Sequential, Active, and Reinforcement Learning, May 2021.


  22. R. Pradhan, Amrit S. Bedi, A. Koppel, and K. Rajawat. `` Adaptive Kernel Learning in Heterogeneous Networks " in IEEE Trans. Signal and Info. Processing over Networks, Mar. 2021.


  23. D. S. Kalhan, A. S. Bedi, A. Koppel, K. Rajawat, H. Hassani, A. Gupta, and A. Banerjee. `` Dynamic Online Learning via Frank-Wolfe Algorithm ,” in IEEE Trans. Signal Process , Jan. 2021


  24. Amrit S. Bedi, A. Koppel, P. Sanyal, and K. Rajawat. `` Nonparametric Compositional Stochastic Optimization for Risk-Sensitive Kernel Learning" " in IEEE Trans. Signal Processing , Jan. 2021.


    25. 2020

  25. A. Mokhtari and A. Koppel, ``High-Dimensional Nonconvex Stochastic Optimization by Doubly Stochastic Successive Convex Approximation" in IEEE Transactions on Signal Processing, Jun. 2020.


  26. Y. Tian, A. Koppel, and A. S. Bedi, and J. How,``Asynchronous and Parallel Distributed Pose Graph Optimization,” in IEEE Robotics and Automation Letters, Oct. 2020.


  27. K. Zhang, A. Koppel, H. Zhu, and T. M. Baser. ``Global Convergence of Policy Gradient Methods to (Almost) Locally Optimal Policies" in SIAM Journal on Control and Optimization, 2020.


  28. A. Koppel, G. Warnell, E. Stump, P. Stone, and A. Ribeiro. ``Policy Evaluation in Continuous MDPs with Efficient Kernelized Gradient Temporal Difference," in IEEE Trans. Automatic Control, . [ArXiV verson]


  29. A. Mokhtari, A. Koppel, M. Takac, and A. Ribeiro, ``A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning," in Journal of Machine Learning Research 21(120):1−51, 2020


  30. A. Koppel, A. S. Bedi, K. Rajawat, and B.M. Sadler. ``Optimally Compressed Nonparametric Online Learning: Tradeoffs between memory and consistency," in IEEE Signal Processing Magazine, Volume: 37 , Issue: 3, May 2020.


    31. 2019

  31. A. Koppel, K. Zhang, H. Zhu, and T. M. Baser. ``Projected Stochastic Primal-Dual Method for Constrained Online Learning with Kernels" in IEEE Trans. Signal Processing, Vol: 67 , Issue: 10 , May 2019


  32. A. S. Bedi, A. Koppel, and K. Rajawat. ``Asynchronous Online Learning in Multi-Agent Systems with Proximity Constraints" in IEEE Trans. Signal Info. Process. Over Networks, Mar. 2019.


  33. A. Koppel, G. Warnell, E. Stump, and A. Ribeiro, ``Parsimonious Online Learning with Kernels via Sparse Projections in Function Space," in the Journal of Machine Learning Research, Jan. 2019. [Video]


  34. A. S. Bedi, A. Koppel, and K. Rajawat, ``Asynchronous Saddle Point Algorithm for Stochastic Optimization in Heterogeneous Networks" in IEEE Trans. Signal Process., Jan. 2019.


    35. 2018

  35. A. Koppel, S. Paternain, C. Richard, and A. Ribeiro, ``Decentralized Online Learning with Kernels", in IEEE Trans. Signal Process, Apr. 2018.


    36. 2017

  36. A. Simonetto, A. Koppel, A. Mokhtari, G. Leus, and A. Ribeiro, ``Decentralized Prediction-Correction Methods for Networked Time-Varying Convex Optimization," in IEEE Trans. Automatic Control, Apr. 2017.


  37. A. Koppel, B. Sadler, and A. Ribeiro, ``Proximity without Consensus in Online Multi-Agent Optimization," in IEEE Trans. Signal Process., Mar. 2017.


  38. A. Koppel, G. Warnell, E. Stump, and A. Ribeiro, ``D4L: Decentralized Dynamic Discrminative Dictionary Learning," in IEEE Trans. Signal and Info. Process over Networks, Feb. 2017. [Video]


    39. 2016

  39. A. Simonetto,  A. Mokhtari, A. Koppel, G. Leus, and A. Ribeiro, ``A Class of Prediction-Correction Methods for Time-Varying Convex Optimization," in IEEE Trans. Signal Process., May. 2016.


    40. 2015

  40. A. Koppel, F. Jakubeic, and A. Ribeiro, ``A saddle point algorithm for networked online convex optimization," in IEEE Trans. Signal Process., Oct 2015. [Video]


Conference Proceedings

    2024

  1. . Zeng, S. Bhatt, A. Koppel, and S. Ganesh, ``Partially Observable Contextual Bandits with Linear Payoffs"" in 2025 International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (L4DC) (submitted).


  2. A. Jain, S. Bhatt, V. Krishnamurthy, A. Koppel, ``Bimodal Bandits: Max-Mean Regret Minimization" in 2024 Asilomar Conference on Signals, Systems, and Computers. Best Paper Finalist


  3. M. A. Zaman, M. Lauriere, A. Koppel and T. Basar, ``Robust Cooperative Multi-Agent Reinforcement Learning: A Mean-Field Type Game Perspective"" in 2024 Learning for Dynamics and Control (L4DC).


  4. S. Zeng, S. Bhatt, A. Koppel, and S. Ganesh, ``Learning Payment-Free Resource Allocation Mechanisms" in 2024 ACM Economics and Computation Workshop: Computational Methods for Economic Dynamics, preprint available as arXiv 2311.10927v1.


  5. S. Zeng, S. Bhatt, E. Kreacic, P. Hassanzadeh, A. Koppel, and S. Ganesh, ``Mechanism Design Without Money: Trading Social Welfare for Incentive Compatibility" in 2024 Winter Simulation Conference (WSC), preprint available as ArXiv 2311.10927.


  6. M. Ding, S. Chakraborty, V. Agrawal, Z. Che, A. Koppel, M. Wang, A. Bedi, F. Huang, ``SAIL: Self-improving Efficient Online Alignment of Large Language Models" in ICML 2024 Workshop: Foundations of Reinforcement Learning and Control–Connections and Perspectives, preprint on Openreview.


  7. S. Zeng, S. Bhatt, A. Koppel, and S. Ganesh, ``A Policy Optimization Approach to the Solution of Unregularized Mean Field Games" in ICML 2024 Workshop on Theoretical Foundations of Foundation Models, preprint available as arXiv: 2406.15567


  8. S. Chakraborty, A. Bedi, A. Koppel, H. Wang, D. Manocha, M. Wang, F. Huang, ``MaxMin-RLHF: Alignment with Diverse Human Preferences" in 2024 International Conference on Machine Learning (ICML), preprint available as arXiv:2402.08925


  9. A Koppel, S. Bhatt, J. Guo, J. Eappen, M. Wang, S. Ganesh, ``Information-Directed Pessimism for Offline Reinforcement Learning" in 2024 International Conference on Machine Learning (ICML).


  10. B. Patel, W. A Suttle, A. Koppel, V. Aggarwal, B. M. Sadler, D. Manocha, A. Bedi,``Towards Global Optimality for Practical Average Reward Reinforcement Learning without Mixing Time Oracles" in 2024 International Conference on Machine Learning (ICML).


  11. A. Lahoti, S. Senapati, K. Rajawat, A. Koppel, ``Sharpened Lazy Incremental Quasi-Newton Method" in 2024 Artificial Intelligence and Statistics (AISTATS)


  12. S. Chakraborty, A. Bedi, A. Koppel, H. Wang, D. Manocha, M. Wang, F. Huang, ``PARL: A Unified Framework for Policy Alignment in Reinforcement Learning" in 2024 International Conference on Learning Representations (ICLR), preprint available as arXiv:2308.02585v2.


  13. D. Goktas, A. Greenwald, S. Zhao, A. Koppel, S. Ganesh``Generative Adversarial Inverse Multiagent Learning" in 2024 International Conference on Learning Representations (ICLR) .


    14. 2023

  14. A. Mishler, Mohsen Ghassemi, A. Koppel, S. Ganesh, `` Model Robustness and Active Learning with Missing-Not-At-Random Outcomes," in 2023 UAI Workshop on Epistemic Uncertainty in Artificial Intelligence


  15. S. Chakraborty, A.S. Bedi, P. Tokekar, A. Koppel, M. Wang, and F. Huang, ``Principal-Driven Reward Design and Agent Policy Alignment via Bilevel-RL ," in ILHF Workshop at ICML 2023.


  16. W. A. Suttle, A.S. Bedi, B. Patel, B. M. Sadler, A. Koppel, D. Manocha, `` Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic," in 2023 International Conference on Machine Learning (ICML).


  17. S. Chakraborty, A.S. Bedi, P. Tokekar, A. Koppel, M. Wang, F. Huang, D. Manocha, `` STEERING: Stein Information Directed Sampling for Efficient Exploration in Model-Based Reinforcement Learning," in 2023 International Conference on Machine Learning (ICML).


  18. M. A. Zaman, M. Lauriere, A. Koppel, T. Basar, ``Receding Horizon Policy Gradient for Zero-Sum Mean-Field Type Games," in 2023 57th Annual Conference on Information Sciences and Systems (CISS)


  19. W. Suttle, A. Koppel, J. Liu, `` Information-Directed Policy Search in Sparse-Reward Settings via the Occupancy Information Ratio," in 2023 57th Annual Conference on Information Sciences and Systems (CISS)


  20. A. S. Bedi, C. Fan, A. Koppel, A. K. Sahu, F. Huang, D. Manocha, ``Federated Learning Beyond Consensus," in 2023 57th Annual Conference on Information Sciences and Systems (CISS)


  21. H. He, A. Koppel, A. S. Bedi, D. Stilwell, M. Farhood, B. Biggs, ``Bi-Level Nonstationary Kernels for Online Gaussian Process Regression," in 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE) [Link forthcoming on IEEExplore].


  22. M. A. Zaman, A. Koppel, S. Bhatt, and T. Basar, ``Oracle-free Reinforcement Learning in Mean-Field Games along a Single Sample Path ," in 2023 Artificial Intelligence and Statistics (AISTATS) [arXiv preprint arXiv:2208.11639 (2022)].


  23. D. Ying, Y. Ding., A. Koppel, J. Lavaei, ``Scalable Multi-Agent Reinforcement Learning with General Utilities ," in 2023 IEEE American Control Conference (ACC) (to appear) [ arXiv preprint arXiv:2302.07938] .

  24. S. Chakraborty, A.S. Bedi, P. Tokekar, A. Koppel, B.M. Sadler, F. Huang, D. Manocha, ``Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning ," in 2023 Conference on Artificial Intelligence (AAAI) [arXiv preprint arXiv:2206.01162 (2022)].


  25. S. Chakraborty, A.S. Bedi, P. Tokekar, A. Koppel, B.M. Sadler, F. Huang, D. Manocha,``Stein Information Directed Sampling for Efficient Exploration in Model-Based Reinforcement Learning" Workshop paper at 2023 AAAI Workshop on Reinforcement Learning Ready for Production .


  26. W. A. Suttle, A. Koppel, J. Liu, ``Occupancy Information Ratio: Infinite-Horizon, Information-Directed, Parameterized Policy Search ," Workshop paper at 2023 AAAI Symposium on Machine Learning for Dynamical Systems [arXiv preprint arXiv:2201.08832 (2022)].


  27. H. He, A. Koppel, A. S. Bedi, D. Stilwell, M. Farhood, B. Biggs, ``Decentralized Multi-agent Exploration with Limited Inter-agent Communications ," in 2023 IEEE International Conference on Robotics and Automation (to appear) (ICRA), May 29 - June 2, 2023 (IROS).


    28. 2022

  28. Q. Jin, A. Koppel, K. Rajawat, A. Mokhtari `` Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood,” Spotlight in 2022 International Conference on Machine Learning (ICML),.


  29. A. S. Bedi, S. Chakraborty, A. Parayil, B. M. Sadler, P. Tokekar, A. Koppel, `` On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces,” Spotlight in 2022 International Conference on Machine Learning (ICML).


  30. S. Chakraborty, A.S. Bedi, A. Koppel, P. Tokekar, D. Manocha, ``Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policies ," in International Conference on Robotics and Automation (under review) (ICRA) (under review). [Paper]


  31. Y. Tian, A. S. Bedi, A. Koppel, M. Calvo-Fullana, D. M. Rosen, J. P. How, ``Distributed Riemannian Optimization with Lazy Communication for Collaborative Geometric Estimation ," in 2022 IEEE Conference on Intelligent Robotics and Systems (IROS) (to appear). Available as arXiv:2203.00851


  32. A. Koppel, A. S. Bedi, B. Ganguly, V. Aggarwal, ``Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming ," in 2022 IEEE Conference on Decision and Control (CDC) (submitted). Available as arXiv:2110.12929


  33. H. Pradhan, A. Koppel, K. Rajawat, ``On Submodular Set Cover Problems For Near-Optimal Online Kernel Basis Selection ," in 2022 IEEE International Conference on Acoustics, Speech, & Signal Processing (ICASSP) . [Paper]


  34. J. Di, E. Zobeidi, A. Koppel, N. Atanasov, ``Distributed Gaussian Process Mapping for Robot Teams with Time-varying Communication ," in 2022 IEEE American Control Conference (to appear). Available as arXiv:2110.06401


  35. W. Suttle, A. Koppel, and J. Liu, ``Policy gradient for ratio optimization: a case study ," in 2022 IEEE Conference on Information Science and Systems (CISS). [Paper] [Slides]


  36. Q. Bai, A. S. Bedi, M. Agarwal, A. Koppel, V. Aggarwal, ``Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach ," in 2022 Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22). Available as arXiv:2109.06332


  37. J. Zhang, A.S. Bedi, M. Wang, A. Koppel, ``MARL with General Utilities via Decentralized Shadow Reward Actor-Critic ," in 2022 Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22). Available as arXiv:2106.00543


    38. 2021

  38. A. S. Bedi, A. Koppel, M. Wang, and J. Zhang, ``Intermittent Communications in Decentralized Shadow Reward Actor-Critic,” in IEEE Conference on Decision and Control, Austin, TX, Dec. 13-15, 2021.


  39. S. Bhatt, W. Mao, A. Koppel, T. Basar, ``Semiparametric Information State Embedding for Policy Search under Imperfect Information,” in IEEE Conference on Decision and Control (to appear), Austin, TX, Dec. 13-15, 2021.


  40. E. Noorani, Y. Savas, A. Koppel, J. S. Baras, U. Topcu, B. M. Sadler, ``Collaborative Beamforming for Agents with Localization Errors,” in Asilomar Conf. Signals, Systems, and Computers. , Oct 31-Nov 3, 2021. [Slides]


  41. A. Koppel, A. S. Bedi, B. Ganguly, and V. Aggarwal, “Randomized Linear Programming for Tabular Average-Cost Multi-agent Reinforcement Learning” in Asilomar Conf. Signals, Systems, and Computers. , Oct 31-Nov 3, 2021. [Slides]


  42. A. Chakraborty, K. Rajawat, and A. Koppel, “Projected Pseudo-Mirror Descent in Reproducing Kernel Hilbert Space” in Asilomar Conf. Signals, Systems, and Computers. , Oct 31-Nov 3, 2021. [Slides]


  43. A. Koppel, A. S. Bedi, and V. Krishnamurthy “A Dynamical Systems Perspective on Online Bayesian Nonparametric Estimators with Adaptive Hyperparameters," in Int. Conf. Acoustics Speech Signal Process (ICASSP) , 6-11 June 2021. [Slides]


  44. A. Parayil, A. S. Bedi, and A. Koppel“ Joint Position and Beamforming Control via Alternating Nonlinear Least-Squares with a Hierarchical Gamma Prior,” in American Control Conf. (ACC) , May 26-28, 2021. [Slides]


  45. J. Zhang, A. S. Bedi, M. Wang, and A. Koppel“ Beyond Cumulative Returns via Reinforcement Learning over State-Action Occupancy Measures,” in American Control Conf. (ACC) , May 26-28, 2021. [Slides]


  46. M. Kepler, A. Koppel, A. S. Bedi, D. Stillwell “ Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference,” in IEEE Int. Conf. Intelligent Robotics and Systems (IROS), Sept 27 - Oct 1, 2021. [Slides]


    47. 2020

  47. Z. Gao, A. Koppel, A. Ribeiro “ Incremental Greedy BFGS: An Incremental Quasi-Newton Method with Explicit Superlinear Rate,” in Neural Information Processing Systems 2020 (NeurIPS), Spotlight , 11 Dec. 2020. [Slides]


  48. J. Zhang, A. Koppel, A. S. Bedi, C. Szepesvari, M. Wang “ Variational Policy Gradient Method for Reinforcement Learning with General Utilities,” in Neural Information Processing Systems 2020 (NeurIPS), Spotlight , 6-12 Dec. 2020. [Slides]


  49. H. Pradhan, A. Bedi, A. Koppel, K. Rajawat, “ Conservative Multi-agent Online Kernel Learning in Heterogeneous Networks,” in IEEE Proc. Asilomar Conf. Signals, Systems, Computers, Pacific Grove, CA, Nov. 8-11, 2020. [Slides]


  50. Y. Tian, A. Koppel, A.S. Bedi, and J. How, “ Asynchronous and Parallel Distributed Pose Graph Optimization,” in IEEE Proc. Int. Conf. Intelligent Robotics and Systems (IROS), Las Vegas, NV, Oct. 25, 2020. [Slides]


  51. E. Zobeidi, A. Koppel, and N. Atanasov, “ Dense Incremental Metric-Semantic Mapping via Sparse Gaussian Process Regression,” in IEEE Proc. Int. Conf. Intelligent Robotics and Systems (IROS), Las Vegas, NV, Oct. 25, 2020.


  52. A. S. Bedi, D. Peddireddy, V. Aggarwal, A. Koppel, “ Efficient Large-Scale Gaussian Process Bandits by Believing only Informative Actions,” in 2nd Annual Conference on Learning for Dynamics and Control (L4DC), Berkeley, CA, Jun. 6-9, 2020. [Slides of associated INFORMS talk]


  53. D. S. Kalhan, A. S. Bedi, A. Koppel, K. Rajawat, A. Gupta, and A. Banerjee, “ Projection Free Dynamic Online Learning,” in IEEE Proc. Int. Conf. Acoustics Speech Signal Process (ICASSP), Barcelona, Spain, May. 4-8, 2020. [Slides]


  54. Z. Gao, A. Koppel, and A. Ribeiro, “ Balancing Rates and Variance via Adaptive Batch-Sizes in First-Order Stochastic Optimization,” in IEEE Proc. Int. Conf. Acoustics Speech Signal Process (ICASSP), Barcelona, Spain, May. 4-8, 2020. [Slides]


  55. A.S. Bedi, A. Koppel, K. Rajawat, B.M. Sadler, “ Trading Dynamic Regret for Model Complexity in Nonstationary Nonparametric Optimization,” in IEEE American Control Conference, Denver, Colorado, Jul. 1-3, 2020.


    56. 2019

  56. A. Koppel, A.S. Bedi, B.M. Sadler, and V. Elvira, “A Projection Operator to Balance Consistency and Complexity in Importance Sampling,” in NeurIPS d Symposium on Advances in Approximate Bayesian Inference , Vancouver, CA, Dec. 14, 2019. [Poster]

  57. H. Kumar, A. Koppel, and A. Ribeiro, “On the Sample Complexity of Actor-Critic for Reinforcement Learning,” in NeurIPS Optimization Foundations of Reinforcement Learning Workshop , Vancouver, CA, Dec. 14, 2019. [Poster]


  58. K. Zhang, A. Koppel, H. Zhu, T. Basar, “Convergence and Iteration Complexity of Policy Gradient Methods for Infinite-horizon Reinforcement Learning,” in IEEE Conference on Decision and Control, Nice, France, Dec. 11-13, 2019. [Slides]


  59. S. Bhatt, A. Koppel, V Krishnamurthy, “Policy Gradient using Weak Derivatives for Reinforcement Learning,” in IEEE Conference on Decision and Control , Nice, France, Dec. 11-13, 2019. [Slides]


  60. A. Koppel, A.S. Bedi, B.M. Sadler, and V. Elvira, "Compressed Streaming Importance Sampling for Efficient Representations of Localization Distributions," in IEEE Asilomar Conf. on Signals, Systems, Computers, Pacific Grove, CA, Nov. 3-6, 2019. [Slides]


  61. S. Bhatt, A. Koppel, V. Krishnamurthy, "Policy Search using Jordan Decomposition for Reinforcement Learning,"in IEEE Conference on Information Sciences and Systems (CISS), Baltimore, MD, Mar. 20-22, 2019. [Slides]


  62. K. Zhang, A. Koppel, H. Zhu, T. Basar, "Policy Search in Infinite-Horizon Discounted Reinforcement Learning: Advances through Connections to Non-Convex Optimization," in IEEE Conference on Information Sciences and Systems (CISS), Baltimore, MD, Mar. 20-22, 2019. [Slides]


  63. A. Koppel, A. S. Bedi, K. Rajawat, “Controlling the Bias-Variance Tradeoff via Coherent Risk for Robust Learning with Kernels,” in IEEE American Control Conference, Philadelphia, PA, July 10-12, 2019.[Slides]


  64. A. Koppel , "Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck,"in IEEE American Control Conference (ACC), Philadelphia, PA, Jul. 10-12, 2019. [Slides]


    65. 2018

  65. A. S. Bedi, A. Koppel, and K. Rajawat, "Asynchronous Saddle Point Method: Interference Management Through Pricing," in IEEE Conf. on Decision and Control (CDC), Miami Beach, FL, Dec. 17-19, 2018. [Slides]


  66. K. Zhang, H. Zhu, T. Baser, and A. Koppel , "Projected Stochastic Primal-Dual Method for Constrained Online Learning with Kernels,"in IEEE Conf. on Decision and Control (CDC), Miami Beach, FL, Dec. 17-19, 2018. [Slides]


  67. H. Pradhan, A. S. Bedi, A. Koppel, and K. Rajawat, "Exact Decentralized Online Nonparametric Optimization," in IEEE Global Conf. on Signals and Info. Processing, Anaheim, CA, Nov. 26-28, 2018. [Slides]


  68. A. Koppel, S. Paternain, C. Richard, and A. Ribeiro, "Decentralized Online Nonparametric Learning," in IEEE Asilomar Conf. on Signals, Systems, Computers, Pacific Grove, CA, Oct. 28-31, 2018. [Slides]


  69. E. Tolstaya, E. Stump, A. Koppel, and A. Ribeiro, "Composable Learning with Sparse Kernel Representations," in International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, Oct. 1-5, 2018. [Slides]


  70. E. Tolstaya, A. Koppel, E. Stump, and A. Ribeiro, "Nonparametric Stochastic Compositional Gradient Descent for Q-Learning in Continuous Markov Decision Problems," in American Control Conference , Milwaukee, WI, June 27-29, 2018. [Slides][Code]


  71. A. Koppel,  A. Mokhtari, and A. Ribeiro, "Parallel Stochastic Successive Convex Approximation Method for Large-Scale Dictionary Learning," in Proc. Int. Conf. Acoustics Speech Signal Process , Calgary, Canada, Apr. 15-20, 2018. [Poster]


    72. 2017

  72. A. Koppel, S. Paternain, C. Richard, and A. Ribeiro, "Decentralized Efficient Nonparametric Stochastic Optimization", in IEEE Global Conference on Signal and Information Processing, Montreal, Canada, Nov. 14-16, 2017. [Slides]


  73. A. S. Bedi, A. Koppel, and K. Rejawat, "Beyond Consensus and Synchrony in Decentralized Online Optimization using Saddle Point Method" in Proc. Asilomar Conf. on Signals Systems Computers, Best Paper Finalist, Pacific Grove, CA, Oct. 29-Nov. 1, 2017. [Slides]


  74. M. Fazlyab, A. Koppel, V. Preciado, and A. Ribeiro, ``A Variational Approach to Dual Methods for Constrained Convex Optimization," in American Control Conference, Seattle, WA, May 24-26, 2017.


  75. A. Mokhtari, A. Koppel,  and G. Scutari, A. Ribeiro, "Large-Scale Non-Convex Stochastic Optimization by Doubly Stochastic Successive Convex Approximation," in Proc. Int. Conf. Acoustics Speech Signal Process, New Orleans, LA, USA Mar. 5-9 2017. [Poster]


  76. A. Koppel, G. Warnell, E. Stump, and A. Ribeiro, "Parsimonious Online Learning with Kernels via Sparse Projections in Function Space," in Proc. Int. Conf. Acoustics Speech Signal Process, New Orleans, LA, USA Mar. 5-9 2017. [Poster]


    77. 2016

  77. A. Koppel, B. M. Sadler, and A. Ribeiro, "Decentralized Online Optimization with Heterogeneous Data Sources", in IEEE Global Conference on Signal and Information Processing, Dec. 7-9 2016.  [Slides]


  78. A. Koppel, A. Mokhtari, and A. Ribeiro, "Doubly Random Parallel Stochastic Methods for Large Scale Learning," in Proc. Asilomar Conf. on Signals Systems Computers, Pacific Grove, CA, November 7-9 2016. [Slides]


  79. A. Koppel, J. Fink, G. Warnell, E. Stump, and A. Ribeiro, "Online Learning for Characterizing Unknown Environments in Ground Robotic Vehicle Models," in Proc. Int. Conf. Intelligent Robotics and Systems, Daejeon, Korea, Oct 9-14 2016. [Slides]


  80. A. Simonetto, A. Koppel, A. Mokhtari, G. Leus, and A. Ribeiro, “A Quasi-Newton Prediction-Correction Method for Decentralized Dynamic Convex Optimization”, in European Control Conference, Aalborg, Denmark, June 29 - July 1, 2016.


  81. A. Mokhtari, A. Koppel, and A. Ribeiro, "Doubly Random Parallel Stochastic Methods for Large Scale Learning," in American Control Conference, Boston, MA, July 6-8 2016. [Slides]


  82. A. Koppel, B. M. Sadler and A. Ribeiro, "Proximity without consensus in online multi-agent optimization," in Proc. Int. Conf. Acoustics Speech Signal Process, Shanghai, China, Mar. 20-25 2016. [Poster]


    83. 2015

  83. A. Simonetto, A. Mokhtari, A. Koppel, G. Leus, and A. Ribeiro, "A Decentralized Prediction-Correction Method for Networked Time-Varying Convex Optimization", IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Dec. 13-16 2015.


  84. A. Koppel, A. Simonetto, A. Mokhtari, G. Leus, and A. Ribeiro, "Target Tracking with Dynamic Convex Optimization", in IEEE Global Conference on Signal and Information Processing, Dec. 14-16 2015. [Slides]

  85. A. Simonetto, A. Koppel, A. Mokhtari, G. Leeus, and A. Ribeiro "Prediction-Correction Methods for Time-Varying Convex Optimization." inProc. Asilomar Conf. on Signals Systems Computers, Pacific Grove, CA, November 8-11 2015. [Slides]


  86. A. Koppel, G. Warnell, and E. Stump, "Task-Driven Dictionary Learning in Distributed Online Settings."in Proc. Asilomar Conf. on Signals Systems Computers, Pacific Grove, CA, November 8-11 2015. [Slides]


  87. A. Koppel, G. Warnell, E. Stump, and A. Ribeiro,"D4L: Decentralized Dynamic Discriminative Dictionary Learning," in Proc. Int. Conf. Intelligent Robotics and Systems, Hamburg, Germany, Sep 28-Oct2 2 015. [Slides]


  88. A. Koppel, F. Jakubeic and A. Ribeiro, "Regret Bounds of a distributed saddle point algorithm," in Proc. Int. Conf. Acoustics Speech Signal Process., Brisbane Australia, Apr 19-24 2015. [Slides]


    89. 2014

  89. A. Koppel, F. Y. Jakubiec, and A. Ribeiro, "A Saddle Point Algorithm for Networked Online Convex Optimization.” in 39th Proc. Int. Conf. Acoust. Speech Signal Process., May 4-9 2014, pp. 8292–8296. [Post]


Theses and Technical Reports

  1. A. Koppel. "Stochastic Optimization for Multi-Agent Statistical Learning and Control," PhD Dissertation, Dept. of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, July 2017.


  2. A. Koppel. “Parameter Estimation in High-Dimensions using Doubly Stochastic Approximation," Master's Thesis, Statistics Department, The Wharton School, University of Pennsylvania, Philadelphia, PA, May 2017.


  3. V. Ganesan, A. Koppel, S. Han, J. Conroy, A. Wickenden, R. Murray, and W. Nothwang. “Implementation and Validation of Bioplausible Visual Servoing Control.” ARL-TR- 6387; U.S. Army Research Laboratory: Adelphi, MD, March 2013.


  4. A. Koppel, E. Stump, W. Nothwang, and B. Sadler. “An Adaptive Stochastic Differential System for Multi-Agent Coordination.” Army Research Laboratory Technical Report. Aug. 2012. (Preprint).

  5. A. Koppel*, V. Ganesan,* A. Wickenden, W. Nothwang. “Slow Computing Simulation of Bio-Plausible Control.” ARL-TR-5959; U.S. Army Research Laboratory: Adelphi, MD, March 2012


  6. A. Koppel and R. Feres. “Stochastic Methods for the Lotka-Volterra Model with Migration.” Bachelors Honors Thesis. Washington University in St. Louis, Mar. 2011.

Invited Talks and Seminars


    2020

  1. Industrial and Operations Engineering Department Colloquium, University of Michigan, Ann Arbor, MI, Jan 17, 2020

    2. 2019

  2. IEEE Conference on Decision and Control, Nice, France, Dec 11-13, 2019

  3. Workshop on New Directions in Reinforcement Learning and Control, Institute for Advanced Study, Princeton, NJ, Nov. 8, 2019 [Video]

  4. IEEE Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 4-6, 2019

  5. INFORMS Annual Meeting, Seattle, WA, Oct. 20-23, 2019

  6. SIAM International Conference on Continuous Optimization (ICCOPT), Technical University (TU) of Berlin, Berlin, Germany, Aug. 5-8, 2019

  7. Learning for Dynamics and Control (L4DC) Poster, Massachusetts Institute of Technology, Cambridge, MA, May 30, 2019

  8. Artificial Intelligence and Machine Learning Seminar, Lehigh University, Bethlehem, PA, May 9, 2019

  9. Industrial and Systems Engineering Seminar, Lehigh University, Bethlehem, PA, May 8, 2019

  10. Intelligent Systems Seminar, Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, May 2, 2019

  11. Visiting Colloquium, Johns Hopkins University, Baltimore, MD, Mar. 12, 2019

  12. Electrical and Computer Engineering Colloquium, Princeton University, Princeton, NJ, Feb. 27, 2019

  13. Visiting Colloquium, Cornell Tech, New York, NY, Feb. 26, 2019

  14. Machine Learning Colloquium, Samsung AI Center, New York, NY, Feb. 25, 2019

  15. Visiting Colloquium, University of Pennsylvania, Philadelphia, PA, Jan. 31, 2019

  16. Workshop on Machine Learning for Networked Data, New York University, New York, NY, Jan. 29, 2019

  17. Electrical and Computer Engineering Colloquium, George Mason University, Fairfax, VA, Jan. 23, 2019

    18. 2018

  18. Computer Science Colloquium, Washington University, St. Louis, MO, Nov. 16, 2018

  19. Intelligent Systems Seminar, U.S. Army Research Laboratory, Adelphi, MD, Nov. 13, 2018

  20. Machine Learning Seminar, Facebook Artificial Intelligence Research (FAIR), Menlo Park, CA, Nov. 1, 2018

  21. DIMACS/MOPTA/TRIPODS Workshop on Machine Learning and Optimization, Lehigh University, Bethlehem, PA, Aug 13-15, 2018

  22. International Symposium on Mathematical Programming (ISMP), Place de la Victoire, University of Bordeaux, Bordeaux, France, July 2-5, 2018

  23. Learning Theory Seminar, Cornell Tech., New York, NY, May 8, 2018

  24. INFORMS Optimization Society Conference, Denver, CO, Mar. 23, 2018

  25. Artificial Intelligence Seminar, Carnegie Melon University, Pittsburgh, PA, Feb. 20, 2018

  26. Machine Learning Seminar, Army Research Laboratory, Adelphi, MD, Feb. 15, 2018

  27. Laboratory on Information and Decision Systems (LIDS) Seminar, Massachusetts Institute of Technology, Cambridge, MA, Jan. 30, 2018

  28. Machine Learning Seminar, George Washington University, Washington, DC, Jan. 23, 2018

    29. 2017

  29. Intelligent Systems Seminar, Army Research Laboratory, Adelphi, MD, Dec. 10, 2017

  30. INFORMS Annual Meeting, Houston, TX, Oct. 24, 2017

  31. Science Cafe, Army Research Laboratory, Adelphi, MD, Oct. 10, 2017

  32. DIMACS Workshop on Distributed Optimization, Information Processing, and Learning, Rutgers University, New Brunswick, NJ, Aug. 23, 2017

  33. Learning Theory Seminar, Microsoft Research, Redmond, WA, May 23, 2017

    34. 2016

  34. Applied Probability Seminar, IBM Watson Research Center, Yorktown Heights, NY, Oct. 5, 2016

  35. Phd Student Colloquium, University of Pennsylvania, Philadelphia, PA, Sep. 21, 2016

  36. Optimization and Learning Seminar, University of Science & Technology of China (USTC) Hefei, China, Mar. 29, 2016

  37. INFORMS Optimization Society Conference, Princeton University, Princeton, NJ, Mar. 19, 2016

    38. 2015

  38. Communications and Networking Seminar, University of Southern California, Aug. 19, 2015.

  39. Signal and Information Processing Seminar, University of California, Los Angeles (UCLA), AUG. 18, 2015.