{"id":24,"date":"2016-10-01T14:05:02","date_gmt":"2016-10-01T18:05:02","guid":{"rendered":"https:\/\/sites.bu.edu\/aolshevsky\/?page_id=24"},"modified":"2025-07-02T11:17:39","modified_gmt":"2025-07-02T15:17:39","slug":"publications","status":"publish","type":"page","link":"https:\/\/sites.bu.edu\/aolshevsky\/publications\/","title":{"rendered":""},"content":{"rendered":"

Journal Papers + ML Conferences<\/h3>\n
    \n
  1. Sample Complexity of the Linear Quadratic Regulator: A Reinforcement Learning Lens<\/a>
    \nA.N. Moghaddam, A. Olshevsky, B. Gharesifard
    \nJournal of Machine Learning Research<\/em>, 2025.<\/li>\n
  2. MDP Geometry, Normalization and Value Free Solvers<\/a>
    \nA. Mustafin, A. Pakharev, A. Olshevsky, I.C. Paschalidis
    \nProceedings AISTATS 2025<\/em>.<\/li>\n
  3. Network-based epidemic control through optimal travel and quarantine management<\/a>
    \nM. Talaei, A.I. Rikos, A. Olshevsky, L.F. White, I.C. Paschalidis
    \nIEEE Transactions on Control of Network Systems<\/em>, 2025.<\/li>\n
  4. Optimal Fixed Lockdown for Pandemic Control<\/a>
    \nQ. Ma, Y. Y. Liu, A. Olshevsky
    \nIEEE Transactions on Automatic Control, <\/em>2024.<\/em><\/li>\n
  5. One Shot Averaging For Distributed TD(\u03bb)<\/a>
    \nH. Tian, Y. Paschalidis, A. Olshevsky
    \nIEEE Control System Letters,\u00a0<\/em>2024.<\/li>\n
  6. Closing The Gap Between SVRG and TD-SVRG With Gradient Splitting<\/a>
    \nA. Mustafin, A. Olshevsky, Y. Paschalidis
    \nTransactions on Machine Learning Research, <\/em>2024.<\/li>\n
  7. Convergence of Actor-Critic with Multi-Layer Neural Networks<\/a>
    \nH. Tian, A. Olshevsky, Y. Paschalidis
    \nProceedings of NeurIPS 2023.<\/em><\/li>\n
  8. On the Performance of Temporal Difference Learning with Neural Networks<\/a>
    \nH. Tian, I. Paschalidis, A. Olshevsky
    \nProceedings of ICLR 2023<\/em>.<\/li>\n
  9. A Small Gain Analysis of Single Timescale Actor-Critic<\/a>
    \nA. Olshevsky, B. Gharesifard
    \nSIAM Journal on Control and Optimization, 2023<\/em>.<\/li>\n
  10. Distributed TD(0) with Almost No Communication<\/a>
    \nR. Liu, A. Olshevsky
    \nIEEE Control System Letters, 2023<\/em>.<\/li>\n
  11. Asymptotic Network Independence and Step-Size for a Distributed Subgradient Method<\/a>
    \nA. Olshevsky
    \nJournal of Machine Learning Research, 2022<\/em>.<\/li>\n
  12. A Sharp\u00a0Estimate on the Transient Time of Distributed Stochastic Gradient<\/a>
    \nDescent
    \nS. Pu, A. Olshevsky Y. Paschalidis
    \nIEEE Transactions on Automatic Control<\/em>, 2022.<\/li>\n
  13. Non-asymptotic Concentration Rates in Cooperative Learning Part I: Variational Non-Bayesian Social Learning<\/a>
    \nC. Uribe, A. Olshevsky, A. Nedich
    \nIEEE Transactions on Control of Network Systems<\/em>, 2022.
    \nSpecial issue on Dynamics and Behaviors in Social Networks.<\/li>\n
  14. Non-asymptotic Concentration Rates in Cooperative Learning Part II: Inference on Compact Hypothesis Sets<\/a>
    \nC. Uribe, A. Olshevsky, A. Nedich
    \nIEEE Transactions on Control of Network Systems<\/em>, 2022.
    \nSpecial issue on Dynamics and Behaviors in Social Networks.<\/li>\n
  15. Communication-Efficient SGD: From Local SGD to One-Shot Averaging<\/a>
    \nA. Spiridonoff, A. Olshevsky, Y. Paschalidis
    \nProceedings of NeurIPS 2021<\/em>.<\/li>\n
  16. Temporal Difference Learning as Gradient Splitting<\/a>
    \nR. Liu, A. Olshevsky
    \nProceedings of ICML 2021<\/em>.<\/li>\n
  17. Deterministic and Randomized Actuator Scheduling with Guaranteed Performance Bounds<\/a>
    \nM. Siami, A. Jadbabaie, A. Olshevsky
    \nIEEE Transactions on Automatic Control<\/em>, 2021.<\/li>\n
  18. Adversarial Crowdsourcing Through Robust Rank-1 Matrix Completion<\/a>
    \nQ. Ma, A. Olshevsky
    \nProceedings of NeurIPS 2020<\/em>.<\/li>\n
  19. Gradient Descent for Sparse Rank-One Matrix Completion for Crowdsourced Aggregation of Sparsely Interacting Workers<\/a>
    \nY. Ma, A. Olshevsky, V. Saligrama, C. Szepesvari
    \nJournal of Machine Learning Research<\/em>, 2020.
    \nConference version was published as\u00a0Proceedings of ICML 2018<\/em><\/li>\n
  20. Robust Asynchronous Stochastic Gradient Push: Asymptotically Optimal and Network Independent Performance for Strongly Convex Functions<\/a>
    \nA. Spiridonoff, A. Olshevsky, Y. Paschalidis
    \nJournal of Machine Learning Research<\/em>,\u00a0 2020.<\/li>\n
  21. Asymptotic Network Independence in Distributed Optimization for Machine Learning<\/a>
    \nS. Pu, A. Olshevsky, Y. Paschlidis
    \nIEEE Signal Processing Magazine<\/em>,\u00a0 2020.<\/li>\n
  22. Asymptotic Convergence Rate of Alternating Minimization for Rank One Matrix Completion<\/a>
    \nR. Liu, A. Olshevsky
    \nIEEE Control Systems Letters<\/em>, 2020<\/p>\n

    (All papers below have author names in alphabetical order)<\/li>\n

  23. Minimax Rate for Pairwise Comparisons in the BTL Model,<\/a>
    \nJ. Hendrickx, A. Olshevsky, V. Saligrama
    \nProceedings of ICML 2020.<\/em><\/li>\n
  24. Minimax Rank-1 Matrix Factorization<\/a>
    \nJ. Hendrickx, A. Olshevsky, V. Saligrama
    \nProceedings of AISTATS 2020.<\/em><\/li>\n
  25. On A Relaxation of Time-Varying Actuator Placement<\/a>
    \nA. Olshevsky
    \nIEEE Control System Letters,\u00a0<\/em>2020<\/li>\n
  26. Graph Resistance and Learning from Pairwise Comparisons\u00a0<\/a>
    \nJ. Hendrickx, A. Olshevsky, V. Saligrama
    \nProceedings of ICML 2019.\u00a0<\/em><\/li>\n
  27. Graph Theoretic Analysis of Belief System Dynamics Under Logic Constraints<\/a>
    \nA. Nedic, A. Olshevsky, C. Uribe
    \nScientific Reports, <\/em>2019.<\/li>\n
  28. On The Inapproximability of the Discrete Witsenhausen Problem<\/a>
    \nA. Olshevsky
    \nIEEE Control System Letters, <\/em>2019.<\/li>\n
  29. Scaling Laws for Consensus Protocols Subject to Noise<\/a>
    \nA. Jadbabaie, A.\u00a0Olshevsky
    \nIEEE Transactions on Automatic Control<\/em>, 2019.<\/li>\n
  30. Minimal Reachability is Hard to Approximate<\/a>
    \nA. Jadbabaie, A. Olshevsky, G. Pappas, V. Tzoumas
    \nIEEE Transactions on Automatic Control<\/em>, 2019.<\/li>\n
  31. On (Non)Supermodularity of Average Control Energy<\/a>
    \nA. Olshevsky
    \nIEEE Transactions on Control of Network Systems<\/em>, 2018.<\/li>\n
  32. Network Topology and Communication-Computation Tradeoffs in Distributed Optimization<\/a>
    \nA. Nedic, A. Olshevsky, M. Rabbat
    \nProceedings of the IEEE<\/em>, 2018.<\/li>\n
  33. Federated Learning of Predictive Models from Federated Electronic Health Records<\/a>
    \nT. Brisimi, R. Chen, T. Mela, A. Olshevsky, Y. Paschalidis, W. Shi
    \nInternational Journal of Medical Informatics, <\/em>2018.
    \nSpecial issue on\u00a0Health Data Science.<\/li>\n
  34. Crowdsourcing with Sparsely Interacting Workers,<\/a>
    \n<\/span>Y. Ma,<\/span> <\/span>A. Olshevsky,<\/span> <\/span>V. <\/span>Saligrama, C. Szepesvari,<\/span>
    \n<\/span>Proceedings of ICML 2018.<\/span><\/em><\/li>\n
  35. Achieving Geometric Convergence for Distributed Optimization over Time-Varying Graphs<\/a>
    \nA. Nedic, A. Olshevsky, W. Shi
    \nSIAM Journal on Optimization<\/em>, 2017.<\/li>\n
  36. Linear Time Average Consensus on Fixed Graphs and Implications for Decentralized Optimization and Multi-Agent Control
    \n<\/a>A. Olshevsky
    \nSIAM Journal on Control and Optimization<\/em>,\u00a02017.<\/li>\n
  37. Fast Convergence Rates for Distributed Non-Bayesian Learning<\/a>
    \nA. Nedic, A. Olshevsky, C. Uribe
    \nIEEE Transactions on Automatic Control<\/em>, 2017.<\/li>\n
  38. Distributed Resource Allocation on Dynamic Networks in Quadratic Time<\/a>
    \nT. T. Doan, A. Olshevsky
    \nSystems & Control Letters<\/em>, 2017.<\/li>\n
  39. Stochastic Gradient-Push for Strongly Convex Functions on Time-Varying Directed Graphs<\/a>
    \nA. Nedic, A. Olshevsky
    \nIEEE Transactions on Automatic Control<\/em>, 2016.<\/li>\n
  40. Convergence Time of Quantized Metropolis Consensus over Time-Varying\u00a0Networks<\/a>
    \nT. Basar, S. R. Etesami, A. Olshevsky
    \nIEEE Transactions on Automatic Control,\u00a0<\/em>2016.<\/li>\n
  41. On Symmetric Continuum Opinion Dynamics<\/a>
    \nJ. M. Hendrickx, A. Olshevsky
    \nSIAM Journal on Control and Optimization<\/em>, 2016.<\/li>\n
  42. Eigenvalue Clustering, Control Energy, and Logarithmic Capacity<\/a>
    \nA. Olshevsky
    \nSystems & Control Letters, <\/em>2016.<\/li>\n
  43. On Primitivity of Sets of Matrices<\/a>
    \nV. Blondel, R. Jungers, A. Olshevsky
    \nAutomatica<\/em>, 2015.<\/li>\n
  44. Nonuniform Line Coverage from Noisy Scalar Measurements<\/a>
    \nP. Davison, N.E. Leonard, A. Olshevsky, M. Schwemmer
    \nIEEE Transactions on Automatic Control<\/em>, 2015.<\/li>\n
  45. Distributed Optimization over Time-Varying Directed Graphs<\/a>
    \nA. Nedic, A. Olshevsky
    \nIEEE Transactions on Automatic Control<\/em>,\u00a0<\/em>2015.<\/li>\n
  46. Cooperative Learning in Multi-Agent Systems From Intermittent Measurements<\/a>
    \nN. E. Leonard, A. Olshevsky
    \nSIAM Journal on Control and Optimization<\/em>, 2015.<\/li>\n
  47. Minimal Controllability Problems<\/a>
    \nA. Olshevsky
    \nIEEE Transactions on Control of Networked Systems, <\/em>2014.<\/li>\n
  48. How to Decide Consensus? A Combinatorial Necessary and Sufficient Condition and a Proof that Consensus is Decidable but NP-hard
    \n<\/a> V. Blondel, A. Olshevsky
    \nSIAM Journal on Control and Optimization<\/em>, 2014.<\/li>\n
  49. Consensus with Ternary Messages<\/a>
    \nA. Olshevsky
    \nSIAM Journal on Control and Optimization, <\/em>2014.<\/li>\n
  50. Graph Diameter, Eigenvalues, and Minimum-Time Consensus\u00a0<\/a>
    \nJ. M. Hendrickx, R. M. Jungers, A. Olshevsky, G. \u00a0Vankeerberghen
    \nAutomatica, <\/em>2014.<\/li>\n
  51. Nonuniform Coverage Control on the Line<\/a>
    \nN.E. Leonard, A. Olshevsky
    \nIEEE Transactions on Automatic Control, <\/em>2013. <\/em><\/li>\n
  52. Degree Fluctuations and the Convergence Time of Consensus Algorithms
    \n<\/a>A. Olshevsky, J.N. Tsitsiklis
    \nIEEE Transactions on Automatic Control,\u00a0<\/em>2013.<\/li>\n
  53. NP-hardness of Deciding Convexity for Quartic Polynomials and Related Problems<\/a>
    \nA.A. Ahmadi, A. Olshevsky, P.A. Parrilo, J.N. Tsitsiklis
    \nMathematical Programming<\/em>, 2013.<\/li>\n
  54. Convergence Speed in Distributed Consensus and Averaging<\/a>
    \nA. Olshevsky, J. N. Tsitsiklis
    \nSIAM Review<\/em>, 2011
    \nThis is a revision\u00a0of paper originally published in\u00a0SICON, <\/em>rewritten
    \nfor a\u00a0more general audience at the invitation of the SIAM Review<\/em>.<\/li>\n
  55. Distributed Anonymous Discrete Function Computation<\/a>
    \nJ. M. Hendrickx, A. Olshevsky, J.N. Tsitsklis
    \nIEEE Transactions on Automatic Control<\/em>, 2011.
    \nSpecial issue on Wireless Sensor and Actuator Networks.<\/li>\n
  56. A Lower Bound on Distributed Averaging on the Line Graph<\/a>
    \nA. Olshevsky, J.N. Tsitsiklis
    \nIEEE Transactions on Automatic Control<\/em>, 2011.<\/li>\n
  57. Matrix p-Norms are NP-hard to Approximate if p \u2260 1,2,\u221e\u00a0<\/a>
    \nJ. M. Hendrickx, A. Olshevsky
    \nSIAM Journal on Matrix Analysis and Applications<\/em>, 2010.<\/li>\n
  58. On Distributed Averaging Algorithms and Quantization Effects<\/a>
    \nA. Nedic, A. Olshevsky, A. Ozdaglar, J.N. Tsitsiklis
    \nIEEE Transactions on Automatic Control<\/em>, 2009.<\/li>\n
  59. On the NP-Hardness of Checking Matrix Polytope Stability and Continuous-Time Switching Stability<\/a>
    \nL. Gurvits, A. Olshevsky
    \nIEEE Transactions on Automatic Control<\/em>, 2009.<\/li>\n
  60. Convergence Speed in Distributed Consensus and Averaging<\/a>
    \nA. Olshevsky, J.N. Tsitsiklis
    \nSIAM Journal on Control and Optimization<\/em>, 2009.
    \nSpecial issue on Control and Optimization in Cooperative Networks.<\/li>\n
  61. On the Nonexistence of Quadratic Lyapunov Functions for Consensus Algorithms<\/a>
    \nA. Olshevsky, J.N. Tsitsiklis
    \nIEEE Transactions on Automatic Control<\/em>, 2008.<\/li>\n
  62. Improved Approximation Algorithms for the Quality of Service Multicast Tree Problem<\/a>
    \nM. Karpinski, I. Mandoiu, A. Olshevsky, A. Zelikovsky
    \nAlgorithmica<\/em>, 2005.<\/li>\n
  63. Kharitonov’s Theorem and Bezoutians<\/a>
    \nA. Olshevsky, V. Olshevsky
    \nLinear Algebra and its Applications<\/em>, 2005.<\/li>\n<\/ol>\n

    Ph.D. Thesis\u00a0<\/strong><\/h3>\n

    Efficient Information Aggregation Strategies for Distributed Control and Signal Processing<\/a>
    \nDept. of EECS, MIT,\u00a0<\/em>Sep.\u00a02010.<\/p>\n

    Other Conferences and Book Chapters<\/strong><\/h3>\n

    (In inverse chronological order, author names always ordered alphabetically).<\/p>\n

      \n
    1. The Role of Control Theory in Systems-Oriented Learning
      \nM. Sznaier, A. Olshevsky, E. Sontag
      \nPresented at MTNS,<\/em> 2022<\/li>\n
    2. Leakage Certification Revisited: Bounding Model Errors in Side-Channel Security Evaluations<\/a>
      \nO. Bronchain, J. M. Hendrickx, C. Massart, A. Olshevsky, F. Standaert
      \nProceedings of Crypto 2019, Santa Barbara, USA, 2019.<\/em><\/li>\n
    3. Improved Convergence Rate for Distributed Resource Allocation
      \nA. Nedic, A. Olshevsky, W. Shi
      \nProceedings of CDC 2018, the IEEE Conference on Decision and Control, Orlando, USA, 2018.\u00a0<\/em><\/li>\n
    4. Decentralized Consensus Optimization and Resource Allocation
      \nA. Nedic, A. Olshevsky, W. Shi
      \nin Large Scale and Distributed Optimization,\u00a0<\/em>ed. P. Giselsson, A. Rantzer, Springer Lecture Notes in Mathematics, 2018.<\/li>\n
    5. Limitations and Tradeoffs in Minimum Input Selection Problems
      \nA. Jadbabaie, A. Olshevsky, M. Siami
      \nProc. ACC 2018, the American Control Conference, Milwaukee, USA, 2017.<\/em><\/li>\n
    6. Fully Asynchronous Push-Sum With Growing Intercommunication Intervals
      \n<\/span>A. Olshevsky, Y. Paschalidis, A. Spiridonoff
      \nProc. of ACC 2018, the American Control Conference, Milwaukee, USA, 2017.<\/em><\/li>\n
    7. Geometrically Convergent\u00a0<\/span>Distributed Optimization with Uncoordinated Step-Sizes
      \nA. Nedic, A. Olshevsky, W. Shi, C. Uribe
      \nProc. of ACC 2017, the American Control Conference, <\/em>Seattle, USA, 2017.\u00a0<\/span><\/li>\n
    8. Distributed Gaussian Learning over Time-Varying Directed Graphs
      \nA. Nedic, A. Olshevsky, C. Uribe
      \nProc. of Asilomar 2016, the 51st Asilomar\u00a0Conference on Signals, Systems, and Computers, <\/em>Monterey, USA, 2016.
      \n<\/span><\/li>\n
    9. Fast Algorithms for Distributed Optimization and Hypothesis Testing: A Tutorial<\/a>
      \nA. Olshevsky
      \nProc. of CDC 2016, the 55th IEEE Conference on Decision and Control, <\/em>Las Vegas, USA, 2016.<\/span><\/li>\n
    10. A Tutorial on Distributed (Non-Bayesian) Learning: Problem, Algorithm, and Results<\/a>
      \nA. Nedic, A. Olshevsky, C. Uribe
      \nProc. of CDC 2016, the 55th IEEE Conference on Decision and Control, <\/em>Las Vegas, USA, 2016.<\/span><\/li>\n
    11. A Geometrically\u00a0Convergent Method for Distributed Optimization over\u00a0Time-Varying Graphs
      \nA. Nedic, A. Olshevsky, W. Shi
      \nProc. of CDC 2016, the 55th IEEE Conference on Decision and Control, <\/em>Las Vegas, USA, 2016.<\/li>\n
    12. Distributed Learning with Infinitely Many Hypotheses
      \nA. Nedic, A. Olshevsky, C. Uribe
      \nProc. of CDC 2016, the 55th IEEE Conference on Decision and Control, <\/em>Las Vegas, USA, 2016.<\/li>\n
    13. On Performance of Consensus Protocols Subject to Noise: Role of Hitting Times and Network Structure
      \nA. Jadbabaie, A. Olshevsky
      \nProc. of CDC 2016, the 55th IEEE Conference on Decision and Control, <\/em>Las Vegas, USA, 2016.<\/li>\n
    14. Linearly Convergent Decentralized Consensus Optimization over Directed Networks
      \nA. Nedic, A. Olshevsky. W. Shi
      \nProc. of GlobalSIP 2016, the IEEE Conferencence on Signal and Information Processing<\/em>, Washington DC, USA, 2016.<\/li>\n
    15. Algorithms and Intractability Results for Minimal Controllability Problems
      \nA. Olshevsky
      \nProc. MTNS 2016, the 22nd International Symposium on Mathematics of Networks and Systems,\u00a0<\/em>Minneapolis, USA, 2016.<\/li>\n
    16. Nonasymptotic Convergence Rates for Cooperative Learning\u00a0over Time-Varying Directed Graphs
      \nA. Nedic, A. Olshevsky, C. Uribe
      \nProc. ACC 2015, the American Control Conference<\/em>, Chicago, USA, 2015.<\/li>\n
    17. Minimum Input Selection for Structural Controllability<\/a>
      \nA. Olshevsky
      \nProc. ACC 2015, the American Control Conference<\/em>, Chicago, USA, 2015.<\/li>\n
    18. Linear Time Average Consensus
      \nA. Olshevsky
      \nProc. NecSys 2015, the 5th IFAC Conference on Distributed Estimation and Control in Networked Systems<\/em>, Philadelphia, USA, 2015.<\/li>\n
    19. Fast Convergence of Quantized Consensus Using Metropolis Weights
      \nT. Basar, S. Etesami, A. Olshevsky
      \nProc. CDC 14, the 53rd IEEE Conference on Decision and Control<\/em>, Los Angeles, USA, 2014.<\/li>\n
    20. Focused First-Followers Accelerate Aligning Followers with\u00a0the Leader in Reaching Network Consensus
      \nM. Cao, A. Olshevsky, W. Xia
      \nProc. of the 19th IFAC World<\/em> Congress, Cape Town, South Africa, 2014.<\/li>\n
    21. Distributed Optimization over Time-Varying Directed Graphs
      \nA. Nedic, A. Olshevsky
      \nProc. CDC 13<\/em>,\u00a0the 52nd IEEE Conference on Decision and Control<\/em>, Florence, Italy, 2013.<\/li>\n
    22. On Primitivity of Sets of Matrices
      \nV. Blondel, R. Jungers, A. Olshevsky
      \nProc. CDC 13<\/em>,\u00a0the 52nd IEEE Conference on Decision and Control<\/em>, Florence, Italy, 2013.<\/li>\n
    23. Cooperative Learning in Multi-Agent Systems from Intermittent Measurements
      \nN. E. Leonard, A. Olshevsky
      \nProc. CDC 13<\/em>,\u00a0the 52nd IEEE Conference on Decision and Control<\/em>, Florence, Italy, 2013.<\/li>\n
    24. Symmetric Continuum Opinion Dynamics: Convergence, but Sometimes Only in Distribution
      \nJ. M. Hendrickx, A. Olshevsky
      \nProc. CDC 13<\/em>,\u00a0the 52nd IEEE Conference on Decision and Control<\/em>, Florence, Italy, 2013.<\/li>\n
    25. Consensus with Ternary Messages
      \nA. Olshevsky
      \nProc. CDC 13<\/em>,\u00a0the 52nd IEEE Conference on Decision and Control<\/em>, Florence, Italy, 2013.<\/li>\n
    26. Distributed Optimization of Strongly Convex Functions over Time-Varying Graphs
      \nA. Nedic, A. Olshevsky
      \nProc. GlobalSIP 2013, the 1st IEEE Conference on Signal and Information Processing, <\/i>Austin, USA, 2013.<\/li>\n
    27. Combinatorial Bounds and Scaling Laws for Noise Amplification in Networks
      \nA. Jadbabaie, A. Olshevsky
      \nProc. ECC 13, the European Control Conference,\u00a0<\/em>Zurich, Switzerland, 2013.<\/li>\n
    28. On the Cost of Deciding Consensus
      \nV. Blondel, A. Olshevsky
      \nProc. CDC 12<\/em>,\u00a0the 51st IEEE Conference on Decision and Control<\/em>, Maui, HI, 2012.<\/li>\n
    29. Nonuniform Coverage Control on the Line
      \nN.E. Leonard, A. Olshevsky
      \nProc. CDC 11<\/em>,\u00a0the 50th IEEE Conference on Decision and Control<\/em>, Orlando, FL, 2011.<\/li>\n
    30. Degree Fluctuations and the Convergence Times of Consensus Algorithms
      \nA. Olshevsky, J.N. Tsitsiklis
      \nProc. CDC 11<\/em>,\u00a0the 50th IEEE Conference on Decision and Control<\/em>, Orlando, FL, 2011.<\/li>\n
    31. A Lower Bound on Distributed Averaging on the Line Graph
      \nA. Olshevsky, J.N. Tsitsiklis
      \nProc. CDC 10, the 49th IEEE Conference on Decision and Control<\/em>, Atlanta, GA, 2010.<\/li>\n
    32. Distributed Anonymous Function Computation in Information Fusion and Multiagent Systems<\/a>
      \nJ. M. Hendrickx, A. Olshevsky, J.N. Tsitsiklis
      \nProc. Allerton 09, the Forty-Seventh Allerton Conference on Communication, Control, and Computing,\u00a0<\/em>Monticello, Illinois, 2009.<\/li>\n
    33. Distributed Subgradient Methods and Quantization Effects<\/a>
      \nA. Nedic, A. Olshevsky, A. Ozdaglar, J.N. Tsitsiklis
      \nProc. CDC 08, the 47th IEEE Conference on Decision and Control<\/em>, Cancun, Mexico, 2008.<\/li>\n
    34. On Distributed Averaging Algorithms and Quantization Effects<\/a>
      \nA. Nedic, A. Olshevsky, A. Ozdaglar, J.N. Tsitsiklis,
      \nProc. CDC 08, the 47th IEEE Conference on Decision and Control<\/em>, Cancun, Mexico, 2008.<\/li>\n
    35. Stability Testing of Matrix Polytopes<\/a>
      \nL. Gurvits, A. Olshevsky,
      \nProc. of ECC 07, the European Control Conference<\/em>, Kos, Greece, 2007.<\/li>\n
    36. Convergence Speed in Distributed Consensus and Averaging<\/a>
      \nA. Olshevsky, J.N. Tsitsiklis,
      \nProc. CDC 06, the 45th IEEE Conference on Decision and Control<\/em>, San Diego, USA, 2006.<\/li>\n
    37. Convergence in Multiagent Coordination, Consensus, and Flocking
      \nV.D. Blondel, J.M. Hendrickx, A. Olshevsky, J.N. Tsitsiklis
      \nProc. CDC 05, the 44th IEEE Conference on Decision and Control<\/em>, Seville, Spain, 2005.<\/li>\n
    38. Quality of Service in Multimedia Multicast Routing
      \nI. Mandoiu, A. Olshevsky, A. Zelikovsky
      \nin\u00a0Approximation Algorithms and Metaheuristics<\/em>, T. Gonzales (editor), Chapman and Hall, 2007.<\/li>\n
    39. Primal-Dual Algorithms for QoS Multimedia Multicast
      \nG. Calinescu, C. Fernaneds, I. Mandoiu, A. Olshevsky, K. Yang, A. Zelikovsky
      \nProc. GLOBECOM 03, the IEEE Global Communication Conference<\/em>, San Francisco, USA, 2003.<\/li>\n
    40. A Comrade-Matrix-Based Derivation of the Different Version of Fast Cosine and Sine Transforms<\/a>
      \nA. Olshevsky, V. Olshevsky, J. Wang
      \nProc. SPIE 03, Advanced Signal Processing Algorithms, Architectures, and Implementations XII<\/em>, 2003.<\/li>\n
    41. Improved Approximation Algorithms for the Quality of Service Steiner Tree Problem
      \nM. Karpinski, I. Mandoiu, A. Olshevsky, A. Zelikovsky
      \nProc. WADS 03, the Workshop and Algorithms and Data Structures<\/em>, Ottawa, Canada, 2003<\/li>\n
    42. Network Lifetime and Power Assignment for Ad-Hoc Wireless Networks<\/a>
      \nG. Galinescu, S. Kapoor, A. Olshevsky, and A. Zelikovsky
      \nProc. ESA 03, the European Sympoisum on Algorithms<\/em>, Budapest, Hungary, 2003.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

      Journal Papers + ML Conferences Sample Complexity of the Linear Quadratic Regulator: A Reinforcement Learning Lens A.N. Moghaddam, A. Olshevsky, B. Gharesifard Journal of Machine Learning Research, 2025. MDP Geometry, Normalization and Value Free Solvers A. Mustafin, A. Pakharev, A. Olshevsky, I.C. Paschalidis Proceedings AISTATS 2025. Network-based epidemic control through optimal travel and quarantine management […]<\/p>\n","protected":false},"author":12455,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/sites.bu.edu\/aolshevsky\/wp-json\/wp\/v2\/pages\/24"}],"collection":[{"href":"https:\/\/sites.bu.edu\/aolshevsky\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.bu.edu\/aolshevsky\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/aolshevsky\/wp-json\/wp\/v2\/users\/12455"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/aolshevsky\/wp-json\/wp\/v2\/comments?post=24"}],"version-history":[{"count":50,"href":"https:\/\/sites.bu.edu\/aolshevsky\/wp-json\/wp\/v2\/pages\/24\/revisions"}],"predecessor-version":[{"id":1212,"href":"https:\/\/sites.bu.edu\/aolshevsky\/wp-json\/wp\/v2\/pages\/24\/revisions\/1212"}],"wp:attachment":[{"href":"https:\/\/sites.bu.edu\/aolshevsky\/wp-json\/wp\/v2\/media?parent=24"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}