
Submissions Under Review
 Amrit S. Bedi, A. Koppel, and K. Rajawat. " Nonparametric Compositional Stochastic Optimization " in IEEE Trans. Signal Processing (submitted), Feb. 2019.
 K. Zhang, A. Koppel, H. Zhu, and T. M. Baser. "Global Convergence of Policy Gradient Methods: A Nonconvex Optimization Perspective" in SIAM Journal on Control and Optimization (submitted), Jan. 2019.
 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 (submitted), Apr. 2018.
 A. Koppel, E. Tolstaya, E. Stump, and A. Ribeiro. "Nonparametric Stochastic Compositional Gradient Descent for QLearning in Continuous Markov Decision Problems" in IEEE Trans. Automatic Control (submitted), Mar. 2018.
 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 (submitted),
Dec. 2017. [ArXiV verson]
 M. Fazlyab, A. Koppel, V. Preciado, and A. Ribeiro, ``A Variational Approach to Dual Methods for Constrained Convex Optimization," in IEEE Trans. Automatic Control (submitted), Nov. 2017.
 A. Mokhtari, A. Koppel, and A. Ribeiro, "A Class of Parallel Doubly Stochastic Algorithms for LargeScale Learning," in Journal of Machine Learning Research (under review), June 2016.
 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. (to appear), 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]
2019
2018
2017
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2015

2019
 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 (submitted), Nice, France, Dec. 1113, 2019.
 S. Bhatt, A. Koppel, V Krishnamurthy, “Policy Gradient using Weak Derivatives for Reinforcement Learning,” in IEEE Conference on Decision and Control (submitted), Nice, France, Dec. 1113, 2019.
 S. Bhatt, A. Koppel, V. Krishnamurthy, "Policy Search using Jordan Decomposition for Reinforcement Learning,"in IEEE Conference on Information Sciences and Systems (CISS) (to appear), Baltimore, MD, Mar. 2022, 2019.
 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) (to appear), Baltimore, MD, Mar. 2022, 2019.
 A. Koppel, A. S. Bedi, K. Rajawat, “Controlling the BiasVariance Tradeoff via Coherent Risk for Robust Learning with Kernels,” in IEEE American Control Conference (to appear), Philadelphia, PA, July 1012, 2019.
 A. Koppel , "Consistent Online Gaussian Process Regression Without the Sample Complexity Bottleneck,"in IEEE American Control Conference (ACC) (to appear), Philadelphia, PA, Jul. 1012, 2019.
 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.
 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.
 A. S. Bedi, A. Koppel, and K. Rajawat, "Asynchronous Saddle Point Method: Interference Management Through Pricing," in IEEE Conf. on Decision and Control (CDC) (to appear), Miami Beach, FL, Dec. 1719, 2018.
 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) (to appear), Miami Beach, FL, Dec. 1719, 2018.
 E. Tolstaya, E. Stump, A. Koppel, and A. Ribeiro, "Composable Learning with Sparse Kernel Representations," in International Conference on Intelligent Robots and Systems (IROS) (submitted), Madrid, Spain, Oct. 15, 2018.
 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]
 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”, 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." in Proc. 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 Discrminative 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 algorithnm," in Proc. Int. Conf. Acoustics Speech Signal Process., vol. , 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]
2018
2017
2016
2015
2014
 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.
 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.
2019
2018
2017
2016
2015