
Submissions Under Review
 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).
 W. A. Suttle, A. Koppel, J. Liu, ``Occupancy Information Ratio: InfiniteHorizon, InformationDirected, Parameterized Policy Search ," arXiv preprint arXiv:2201.08832 (2023). Under review at SIAM Journal on Control and Optimization (Major revision).
 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
 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
 A. S. Bedi, A. Parayil, J. Zhang, M. Wang, A. Koppel, ``On the Sample Complexity and Metastability of Heavytailed Policy Search in Continuous Control," Journal of Machine Learning Research, Jan. 2024.
 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. 110, vol. 1 DOI Bookmark: 10.1109/TAI.2023.3332023, preprint available as arXiv 2003.10550.
 Q. Bai, A. S. Bedi, M. Agarwal, A. Koppel, V. Aggarwal,``Achieving Zero Constraint Violation for Constrained Reinforcement Learning via PrimalDual Approach," in Journal of Artificial Intelligence Research (JAIR), Dec. 2023.
 H. Kumar, A. Koppel, and A. Ribeiro. ``On the Sample Complexity of ActorCritic Method for Reinforcement Learning with Function Approximation" in Machine Learning, Springer, Feb 2023.
 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
 E. Zobeidi, A. Koppel, and N. Atanasov, ``Dense Incremental MetricSemantic Mapping via Sparse Gaussian Process Regression,” in IEEE Transactions on Robotics (TRO), DOI: 10.1109/TRO.2022.3168733, May 2022.
 Z. Gao, A. Koppel, and A. Ribeiro,``Balancing Rates and Variance via Adaptive BatchSize for Stochastic Optimization Problems,” IEEE Transactions on Signal Processing. DOI: 10.1109/TSP.2022.3186526, Jun. 2022

A. Chakraborty, K. Rajawat, A. Koppel, ``Sparse Representations of Positive Functions via First and SecondOrder
PseudoMirror Descent ," IEEE Transactions on Signal Processing , vol. 70, pp. 31483164, DOI: 10.1109/TSP.2022.3173146. May 2022
 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. 307321 , Issue: 3, May 2022. DOI: 10.1109/TSP.2021.3138242
 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
 A. Koppel, H. Pradhan, K. Rajawat,``Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck,” in Statistics and Computing (Springer), Sept. 2021. [Code]
 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.
 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.
 D. S. Kalhan, A. S. Bedi, A. Koppel, K. Rajawat, H. Hassani, A. Gupta, and A. Banerjee. `` Dynamic Online Learning via FrankWolfe Algorithm ,” in IEEE Trans. Signal Process , Jan. 2021
 Amrit S. Bedi, A. Koppel, P. Sanyal, and K. Rajawat. `` Nonparametric Compositional Stochastic Optimization for RiskSensitive Kernel Learning" " in IEEE Trans. Signal Processing , Jan. 2021.
 A. Mokhtari and A. Koppel, ``HighDimensional Nonconvex Stochastic Optimization by Doubly Stochastic Successive Convex Approximation" in IEEE Transactions on Signal Processing, Jun. 2020.
 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.
 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.
 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]
 A. Mokhtari, A. Koppel, M. Takac, and A. Ribeiro, ``A Class of Parallel Doubly Stochastic Algorithms for LargeScale Learning," in Journal of Machine Learning Research 21(120):1−51, 2020
 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.
 A. Koppel, K. Zhang, H. Zhu, and T. M. Baser. ``Projected Stochastic PrimalDual Method for Constrained Online Learning with Kernels" in IEEE Trans. Signal Processing, Vol: 67 , Issue: 10 , May 2019
 A. S. Bedi, A. Koppel, and K. Rajawat. ``Asynchronous Online Learning in MultiAgent Systems with Proximity Constraints" in IEEE Trans. Signal Info. Process. Over Networks, Mar. 2019.
 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]
 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.
 A. Koppel, S. Paternain, C. Richard, and A. Ribeiro, ``Decentralized Online Learning with Kernels", in IEEE Trans. Signal Process, Apr. 2018.
 A. Simonetto, A. Koppel, A. Mokhtari, G. Leus, and A. Ribeiro, ``Decentralized PredictionCorrection Methods for Networked TimeVarying Convex Optimization," in IEEE Trans. Automatic Control, Apr. 2017.
 A. Koppel, B. Sadler, and A. Ribeiro, ``Proximity without Consensus in Online MultiAgent Optimization," in IEEE Trans. Signal Process., Mar. 2017.
 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]
 A. Simonetto, A. Mokhtari, A. Koppel, G. Leus, and A. Ribeiro, ``A Class of PredictionCorrection Methods for TimeVarying Convex Optimization," in IEEE Trans. Signal Process., May. 2016.
 A. Koppel, F. Jakubeic, and A. Ribeiro, ``A saddle point algorithm for networked online convex optimization," in IEEE Trans. Signal Process., Oct 2015. [Video]
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2024
 A. Lahoti, S. Senapati, K. Rajawat, A. Koppel, ``Sharpened Lazy Incremental QuasiNewton Method" in 2024 Artificial Intelligence and Statistics (AISTATS), preprint available as arXiv:2305.17283
 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.
 D. Goktas, A. Greenwald, S. Zhao, A. Koppel, S. Ganesh``Generative Adversarial Inverse Multiagent Learning" in 2024 International Conference on Learning Representations (ICLR) .
 A. Mishler, Mohsen Ghassemi, A. Koppel, S. Ganesh, `` Model Robustness and Active Learning with MissingNotAtRandom Outcomes," in 2023 UAI Workshop on Epistemic Uncertainty in Artificial Intelligence
 S. Chakraborty, A.S. Bedi, P. Tokekar, A. Koppel, M. Wang, and F. Huang, ``PrincipalDriven Reward Design and Agent Policy Alignment via BilevelRL ," in ILHF Workshop at ICML 2023.
 W. A. Suttle, A.S. Bedi, B. Patel, B. M. Sadler, A. Koppel, D. Manocha, `` Beyond Exponentially Fast Mixing in AverageReward Reinforcement Learning via MultiLevel Monte Carlo ActorCritic," in 2023 International Conference on Machine Learning (ICML).
 S. Chakraborty, A.S. Bedi, P. Tokekar, A. Koppel, M. Wang, F. Huang, D. Manocha, `` STEERING: Stein Information Directed Sampling for Efficient Exploration in ModelBased Reinforcement Learning," in 2023 International Conference on Machine Learning (ICML).
 M. A. Zaman, M. Lauriere, A. Koppel, T. Basar, ``Receding Horizon Policy Gradient for ZeroSum MeanField Type Games," in 2023 57th Annual Conference on Information Sciences and Systems (CISS)
 W. Suttle, A. Koppel, J. Liu, `` InformationDirected Policy Search in SparseReward Settings via the Occupancy Information Ratio," in 2023 57th Annual Conference on Information Sciences and Systems (CISS)
 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)
 H. He, A. Koppel, A. S. Bedi, D. Stilwell, M. Farhood, B. Biggs, ``BiLevel Nonstationary Kernels for Online Gaussian Process Regression," in 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE) [Link forthcoming on IEEExplore].
 M. A. Zaman, A. Koppel, S. Bhatt, and T. Basar, ``Oraclefree Reinforcement Learning in MeanField Games along a Single Sample Path ," in 2023 Artificial Intelligence and Statistics (AISTATS) [arXiv preprint arXiv:2208.11639 (2022)].
 D. Ying, Y. Ding., A. Koppel, J. Lavaei, ``Scalable MultiAgent Reinforcement Learning with General Utilities ," in 2023 IEEE American Control Conference (ACC) (to appear) [ arXiv preprint arXiv:2302.07938] .
 S. Chakraborty, A.S. Bedi, P. Tokekar, A. Koppel, B.M. Sadler, F. Huang, D. Manocha, ``Posterior Coreset Construction with Kernelized Stein Discrepancy for ModelBased Reinforcement Learning ," in 2023 Conference on Artificial Intelligence (AAAI) [arXiv preprint arXiv:2206.01162 (2022)].
 S. Chakraborty, A.S. Bedi, P. Tokekar, A. Koppel, B.M. Sadler, F. Huang, D. Manocha,``Stein Information Directed Sampling for Efficient Exploration in ModelBased Reinforcement Learning" Workshop paper at 2023 AAAI Workshop on Reinforcement Learning Ready for Production .
 W. A. Suttle, A. Koppel, J. Liu, ``Occupancy Information Ratio: InfiniteHorizon, InformationDirected, Parameterized Policy Search ," Workshop paper at 2023 AAAI Symposium on Machine Learning for Dynamical Systems [arXiv preprint arXiv:2201.08832 (2022)].
 H. He, A. Koppel, A. S. Bedi, D. Stilwell, M. Farhood, B. Biggs, ``Decentralized Multiagent Exploration with Limited Interagent Communications ," in 2023 IEEE International Conference on Robotics and Automation (to appear) (ICRA), May 29  June 2, 2023 (IROS).
 Q. Jin, A. Koppel, K. Rajawat, A. Mokhtari `` Sharpened QuasiNewton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood,” Spotlight in 2022 International Conference on Machine Learning (ICML),.
 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).
 S. Chakraborty, A.S. Bedi, A. Koppel, P. Tokekar, D. Manocha, ``Dealing with Sparse Rewards in Continuous Control Robotics via HeavyTailed Policies ," in International Conference on Robotics and Automation (under review) (ICRA) (under review). [Paper]
 Y. Tian, A. S. Bedi, A. Koppel, M. CalvoFullana, 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
 A. Koppel, A. S. Bedi, B. Ganguly, V. Aggarwal, ``Convergence Rates of AverageReward Multiagent Reinforcement Learning via Randomized Linear Programming ," in 2022 IEEE Conference on Decision and Control (CDC) (submitted). Available as arXiv:2110.12929
 H. Pradhan, A. Koppel, K. Rajawat, ``On Submodular Set Cover Problems For NearOptimal Online Kernel Basis Selection ," in 2022 IEEE International Conference on Acoustics, Speech, & Signal Processing (ICASSP) . [Paper]
 J. Di, E. Zobeidi, A. Koppel, N. Atanasov, ``Distributed Gaussian Process Mapping for Robot Teams with Timevarying Communication ," in 2022 IEEE American Control Conference (to appear). Available as arXiv:2110.06401
 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]
 Q. Bai, A. S. Bedi, M. Agarwal, A. Koppel, V. Aggarwal, ``Achieving Zero Constraint Violation for Constrained Reinforcement Learning via PrimalDual Approach ," in 2022 ThirtySixth AAAI Conference on Artificial Intelligence (AAAI22). Available as arXiv:2109.06332
 J. Zhang, A.S. Bedi, M. Wang, A. Koppel, ``MARL with General Utilities via Decentralized Shadow Reward ActorCritic ," in 2022 ThirtySixth AAAI Conference on Artificial Intelligence (AAAI22). Available as arXiv:2106.00543
 A. S. Bedi, A. Koppel, M. Wang, and J. Zhang, ``Intermittent Communications in Decentralized Shadow Reward ActorCritic,” in IEEE Conference on Decision and Control, Austin, TX, Dec. 1315, 2021.
 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. 1315, 2021.
 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 31Nov 3, 2021. [Slides]
 A. Koppel, A. S. Bedi, B. Ganguly, and V. Aggarwal, “Randomized Linear Programming for Tabular AverageCost Multiagent Reinforcement Learning” in Asilomar Conf. Signals, Systems, and Computers. , Oct 31Nov 3, 2021. [Slides]
 A. Chakraborty, K. Rajawat, and A. Koppel, “Projected PseudoMirror Descent in Reproducing Kernel Hilbert Space” in Asilomar Conf. Signals, Systems, and Computers. , Oct 31Nov 3, 2021. [Slides]
 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) , 611 June 2021. [Slides]
 A. Parayil, A. S. Bedi, and A. Koppel“ Joint Position and Beamforming Control via Alternating Nonlinear LeastSquares with a Hierarchical Gamma Prior,” in American Control Conf. (ACC) , May 2628, 2021. [Slides]
 J. Zhang, A. S. Bedi, M. Wang, and A. Koppel“ Beyond Cumulative Returns via Reinforcement Learning over StateAction Occupancy Measures,” in American Control Conf. (ACC) , May 2628, 2021. [Slides]
 M. Kepler, A. Koppel, A. S. Bedi, D. Stillwell “ WassersteinSplitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference,” in IEEE Int. Conf. Intelligent Robotics and Systems (IROS), Sept 27  Oct 1, 2021. [Slides]
 Z. Gao, A. Koppel, A. Ribeiro “ Incremental Greedy BFGS: An Incremental QuasiNewton Method with Explicit Superlinear Rate,” in Neural Information Processing Systems 2020 (NeurIPS), Spotlight , 11 Dec. 2020. [Slides]
 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 , 612 Dec. 2020. [Slides]
 H. Pradhan, A. Bedi, A. Koppel, K. Rajawat, “ Conservative Multiagent Online Kernel Learning in Heterogeneous Networks,” in IEEE Proc. Asilomar Conf. Signals, Systems, Computers, Pacific Grove, CA, Nov. 811, 2020. [Slides]
 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]
 E. Zobeidi, A. Koppel, and N. Atanasov, “ Dense Incremental MetricSemantic Mapping via Sparse Gaussian Process Regression,” in IEEE Proc. Int. Conf. Intelligent Robotics and Systems (IROS), Las Vegas, NV, Oct. 25, 2020.
 A. S. Bedi, D. Peddireddy, V. Aggarwal, A. Koppel, “ Efficient LargeScale Gaussian Process Bandits by Believing only Informative Actions,” in 2nd Annual Conference on Learning for Dynamics and Control (L4DC), Berkeley, CA, Jun. 69, 2020. [Slides of associated INFORMS talk]
 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. 48, 2020. [Slides]
 Z. Gao, A. Koppel, and A. Ribeiro, “ Balancing Rates and Variance via Adaptive BatchSizes in FirstOrder Stochastic Optimization,” in IEEE Proc. Int. Conf. Acoustics Speech Signal Process (ICASSP), Barcelona, Spain, May. 48, 2020. [Slides]
 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. 13, 2020.
 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]
 H. Kumar, A. Koppel, and A. Ribeiro, “On the Sample Complexity of ActorCritic for Reinforcement Learning,” in NeurIPS Optimization Foundations of Reinforcement Learning Workshop , Vancouver, CA, Dec. 14, 2019. [Poster]
 K. Zhang, A. Koppel, H. Zhu, T. Basar, “Convergence and Iteration Complexity of Policy Gradient Methods for Infinitehorizon Reinforcement Learning,” in IEEE Conference on Decision and Control, Nice, France, Dec. 1113, 2019. [Slides]
 S. Bhatt, A. Koppel, V Krishnamurthy, “Policy Gradient using Weak Derivatives for Reinforcement Learning,” in IEEE Conference on Decision and Control , Nice, France, Dec. 1113, 2019. [Slides]
 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. 36, 2019. [Slides]
 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. 2022, 2019. [Slides]
 K. Zhang, A. Koppel, H. Zhu, T. Basar, "Policy Search in InfiniteHorizon Discounted Reinforcement Learning: Advances through Connections to NonConvex Optimization," in IEEE Conference on Information Sciences and Systems (CISS), Baltimore, MD, Mar. 2022, 2019. [Slides]
 A. Koppel, A. S. Bedi, K. Rajawat, “Controlling the BiasVariance Tradeoff via Coherent Risk for Robust Learning with Kernels,” in IEEE American Control Conference, Philadelphia, PA, July 1012, 2019.[Slides]
 A. Koppel , "Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck,"in IEEE American Control Conference (ACC), Philadelphia, PA, Jul. 1012, 2019. [Slides]
 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. 1719, 2018. [Slides]
 K. Zhang, H. Zhu, T. Baser, and A. Koppel , "Projected Stochastic PrimalDual Method for Constrained Online Learning with Kernels,"in IEEE Conf. on Decision and Control (CDC), Miami Beach, FL, Dec. 1719, 2018. [Slides]
 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. 2628, 2018. [Slides]
 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. 2831, 2018. [Slides]
 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. 15, 2018. [Slides]
 E. Tolstaya, A. Koppel, E. Stump, and A. Ribeiro, "Nonparametric Stochastic Compositional Gradient Descent for QLearning in Continuous Markov Decision Problems," in American Control Conference , Milwaukee, WI, June 2729, 2018. [Slides][Code]
 A. Koppel, A. Mokhtari, and A. Ribeiro, "Parallel Stochastic Successive Convex Approximation Method for LargeScale Dictionary Learning," in Proc. Int. Conf. Acoustics Speech Signal Process , Calgary, Canada, Apr. 1520, 2018. [Poster]
 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. 1416, 2017. [Slides]
 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. 29Nov. 1, 2017. [Slides]
 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 2426, 2017.
 A. Mokhtari, A. Koppel, and G. Scutari, A. Ribeiro, "LargeScale NonConvex Stochastic Optimization by Doubly Stochastic Successive Convex Approximation," in Proc. Int. Conf. Acoustics Speech Signal Process, New Orleans, LA, USA Mar. 59 2017. [Poster]
 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. 59 2017. [Poster]
 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. 79 2016. [Slides]
 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 79 2016. [Slides]
 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 914 2016. [Slides]
 A. Simonetto, A. Koppel, A. Mokhtari, G. Leus, and A. Ribeiro, “A QuasiNewton PredictionCorrection Method for Decentralized Dynamic Convex Optimization”, in European Control Conference, Aalborg, Denmark, June 29  July 1, 2016.
 A. Mokhtari, A. Koppel, and A. Ribeiro, "Doubly Random Parallel Stochastic Methods for Large Scale Learning," in American Control Conference, Boston, MA, July 68 2016. [Slides]
 A. Koppel, B. M. Sadler and A. Ribeiro, "Proximity without consensus in online multiagent optimization," in Proc. Int. Conf. Acoustics Speech Signal Process, Shanghai, China, Mar. 2025 2016. [Poster]
 A. Simonetto, A. Mokhtari, A. Koppel, G. Leus, and A. Ribeiro, "A Decentralized PredictionCorrection Method for Networked TimeVarying Convex Optimization", IEEE International Workshop on Computational Advances in MultiSensor Adaptive Processing, Dec. 1316 2015.
 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.
1416 2015. [Slides]
 A. Simonetto, A. Koppel, A. Mokhtari, G. Leeus, and A. Ribeiro "PredictionCorrection Methods for TimeVarying Convex Optimization." inProc. Asilomar Conf. on Signals Systems Computers, Pacific Grove, CA, November 811 2015. [Slides]
 A. Koppel, G. Warnell, and E. Stump, "TaskDriven Dictionary Learning in Distributed Online Settings."in Proc. Asilomar Conf. on Signals Systems Computers, Pacific Grove, CA, November 811 2015. [Slides]
 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 28Oct2 2 015. [Slides]
 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 1924 2015. [Slides]
 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 49 2014, pp. 8292–8296. [Post]
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 A. Koppel. "Stochastic Optimization for MultiAgent Statistical Learning and Control," PhD Dissertation, Dept. of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, July 2017.
 A. Koppel. “Parameter Estimation in HighDimensions using Doubly Stochastic Approximation," Master's Thesis, Statistics Department, The Wharton School, University of Pennsylvania, Philadelphia, PA, May 2017.
 V. Ganesan, A. Koppel, S. Han, J. Conroy, A. Wickenden, R. Murray, and W. Nothwang. “Implementation and Validation of Bioplausible Visual Servoing Control.” ARLTR 6387; U.S. Army Research Laboratory: Adelphi, MD, March 2013.
 A. Koppel, E. Stump, W. Nothwang, and B. Sadler. “An
Adaptive Stochastic Differential System for MultiAgent Coordination.” Army Research
Laboratory Technical Report. Aug. 2012. (Preprint).
 A. Koppel*, V. Ganesan,* A. Wickenden, W. Nothwang. “Slow Computing Simulation of BioPlausible Control.” ARLTR5959; U.S. Army Research Laboratory: Adelphi, MD, March 2012
 A. Koppel and R. Feres. “Stochastic
Methods
for
the LotkaVolterra Model with Migration.” Bachelors Honors Thesis. Washington University in St. Louis, Mar. 2011.
 Industrial and Operations Engineering Department Colloquium, University of Michigan, Ann Arbor, MI, Jan 17, 2020
 IEEE Conference on Decision and Control, Nice, France, Dec 1113, 2019
 Workshop on New Directions in Reinforcement Learning and Control, Institute for Advanced Study, Princeton, NJ, Nov. 8, 2019 [Video]
 IEEE Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 46, 2019
 INFORMS Annual Meeting, Seattle, WA, Oct. 2023, 2019
 SIAM International Conference on Continuous Optimization (ICCOPT), Technical University (TU) of Berlin, Berlin, Germany, Aug. 58, 2019
 Learning for Dynamics and Control (L4DC) Poster, Massachusetts Institute of Technology, Cambridge, MA, May 30, 2019
 Artificial Intelligence and Machine Learning Seminar, Lehigh University, Bethlehem, PA, May 9, 2019
 Industrial and Systems Engineering Seminar, Lehigh University, Bethlehem, PA, May 8, 2019
 Intelligent Systems Seminar, Applied Physics Laboratory, Johns Hopkins University, Laurel, MD, May 2, 2019
 Visiting Colloquium, Johns Hopkins University, Baltimore, MD, Mar. 12, 2019
 Electrical and Computer Engineering Colloquium, Princeton University, Princeton, NJ, Feb. 27, 2019
 Visiting Colloquium, Cornell Tech, New York, NY, Feb. 26, 2019
 Machine Learning Colloquium, Samsung AI Center, New York, NY, Feb. 25, 2019
 Visiting Colloquium, University of Pennsylvania, Philadelphia, PA, Jan. 31, 2019
 Workshop on Machine Learning for Networked Data, New York University, New York, NY, Jan. 29, 2019
 Electrical and Computer Engineering Colloquium, George Mason University, Fairfax, VA, Jan. 23, 2019
 Computer Science Colloquium, Washington University, St. Louis, MO, Nov. 16, 2018
 Intelligent Systems Seminar, U.S. Army Research Laboratory, Adelphi, MD, Nov. 13, 2018
 Machine Learning Seminar, Facebook Artificial Intelligence Research (FAIR), Menlo Park, CA, Nov. 1, 2018
 DIMACS/MOPTA/TRIPODS Workshop on Machine Learning and Optimization, Lehigh University, Bethlehem, PA, Aug 1315, 2018
 International Symposium on Mathematical Programming (ISMP), Place de la Victoire, University of Bordeaux, Bordeaux, France, July 25, 2018
 Learning Theory Seminar, Cornell Tech., New York, NY, May 8, 2018
 INFORMS Optimization Society Conference, Denver, CO, Mar. 23, 2018
 Artificial Intelligence Seminar, Carnegie Melon University, Pittsburgh, PA, Feb. 20, 2018
 Machine Learning Seminar, Army Research Laboratory, Adelphi,
MD, Feb. 15, 2018
 Laboratory on Information and Decision Systems (LIDS) Seminar,
Massachusetts Institute of Technology, Cambridge, MA, Jan. 30, 2018
 Machine Learning Seminar,
George Washington University, Washington, DC, Jan. 23, 2018
 Intelligent Systems Seminar, Army Research Laboratory, Adelphi,
MD, Dec. 10, 2017
 INFORMS Annual Meeting, Houston, TX, Oct. 24, 2017
 Science Cafe, Army Research Laboratory, Adelphi, MD, Oct. 10,
2017
 DIMACS Workshop on Distributed Optimization, Information
Processing, and Learning, Rutgers University, New Brunswick, NJ, Aug.
23, 2017
 Learning Theory Seminar, Microsoft Research, Redmond, WA, May 23,
2017
 Applied Probability Seminar, IBM Watson Research Center, Yorktown
Heights, NY, Oct. 5, 2016
 Phd Student Colloquium, University of Pennsylvania, Philadelphia,
PA,
Sep. 21, 2016
 Optimization and Learning Seminar, University of Science &
Technology of China (USTC) Hefei, China, Mar. 29, 2016
 INFORMS Optimization Society Conference, Princeton University,
Princeton, NJ, Mar. 19, 2016
 Communications and Networking Seminar, University of Southern
California, Aug. 19, 2015.
 Signal and Information Processing Seminar, University of
California, Los Angeles (UCLA), AUG. 18, 2015.
2020
2019
2018
2017
2016
2015