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Alex Olshevsky
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Books and Theses
- 2010
[b1]Alexander Olshevsky:
Efficient information aggregation strategies for distributed control and signal processing. Massachusetts Institute of Technology, Cambridge, MA, USA, 2010
Journal Articles
- 2024
- [j49]Haoxing Tian
, Ioannis Ch. Paschalidis, Alex Olshevsky:
One-Shot Averaging for Distributed TD (λ) Under Markov Sampling. IEEE Control. Syst. Lett. 8: 1541-1546 (2024) - [j48]Qianqian Ma, Yang-Yu Liu, Alex Olshevsky:
Optimal Fixed Lockdown for Pandemic Control. IEEE Trans. Autom. Control. 69(7): 4538-4553 (2024) - 2023
- [j47]Rui Liu, Alex Olshevsky:
Distributed TD(0) With Almost No Communication. IEEE Control. Syst. Lett. 7: 2892-2897 (2023) - [j46]Alex Olshevsky, Bahman Gharesifard:
A Small Gain Analysis of Single Timescale Actor Critic. SIAM J. Control. Optim. 61(2): 980-1007 (2023) - 2022
- [j45]Alex Olshevsky:
Asymptotic Network Independence and Step-Size for a Distributed Subgradient Method. J. Mach. Learn. Res. 23: 69:1-69:32 (2022) - [j44]Shi Pu, Alex Olshevsky, Ioannis Ch. Paschalidis:
A Sharp Estimate on the Transient Time of Distributed Stochastic Gradient Descent. IEEE Trans. Autom. Control. 67(11): 5900-5915 (2022) - [j43]Claudio Altafini, Giacomo Como, Julien M. Hendrickx, Alexander Olshevsky, Alireza Tahbaz-Salehi:
Guest Editorial Special Issue on Dynamics and Behaviors in Social Networks. IEEE Trans. Control. Netw. Syst. 9(3): 1053-1055 (2022) - [j42]César A. Uribe, Alex Olshevsky, Angelia Nedic:
Nonasymptotic Concentration Rates in Cooperative Learning-Part I: Variational Non-Bayesian Social Learning. IEEE Trans. Control. Netw. Syst. 9(3): 1128-1140 (2022) - [j41]César A. Uribe, Alex Olshevsky, Angelia Nedic:
Nonasymptotic Concentration Rates in Cooperative Learning - Part II: Inference on Compact Hypothesis Sets. IEEE Trans. Control. Netw. Syst. 9(3): 1141-1153 (2022) - 2021
- [j40]Rui Liu, Alex Olshevsky:
Asymptotic Convergence Rate of Alternating Minimization for Rank One Matrix Completion. IEEE Control. Syst. Lett. 5(4): 1139-1144 (2021) - [j39]Milad Siami, Alexander Olshevsky, Ali Jadbabaie:
Deterministic and Randomized Actuator Scheduling With Guaranteed Performance Bounds. IEEE Trans. Autom. Control. 66(4): 1686-1701 (2021) - 2020
- [j38]Alex Olshevsky:
On a Relaxation of Time-Varying Actuator Placement. IEEE Control. Syst. Lett. 4(3): 656-661 (2020) - [j37]Artin Spiridonoff, Alex Olshevsky, Ioannis Ch. Paschalidis:
Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions. J. Mach. Learn. Res. 21: 58:1-58:47 (2020) - [j36]Yao Ma, Alex Olshevsky, Csaba Szepesvári, Venkatesh Saligrama:
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers. J. Mach. Learn. Res. 21: 133:1-133:36 (2020) - [j35]Shi Pu, Alex Olshevsky, Ioannis Ch. Paschalidis:
Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning: Examining Distributed and Centralized Stochastic Gradient Descent. IEEE Signal Process. Mag. 37(3): 114-122 (2020) - 2019
- [j34]Alex Olshevsky:
On the Inapproximability of the Discrete Witsenhausen Problem. IEEE Control. Syst. Lett. 3(3): 529-534 (2019) - [j33]Ali Jadbabaie, Alexander Olshevsky, George J. Pappas, Vasileios Tzoumas:
Minimal Reachability is Hard To Approximate. IEEE Trans. Autom. Control. 64(2): 783-789 (2019) - [j32]Ali Jadbabaie, Alex Olshevsky:
Scaling Laws for Consensus Protocols Subject to Noise. IEEE Trans. Autom. Control. 64(4): 1389-1402 (2019) - 2018
- [j31]Theodora S. Brisimi, Ruidi Chen, Theofanie Mela, Alex Olshevsky, Ioannis Ch. Paschalidis, Wei Shi:
Federated learning of predictive models from federated Electronic Health Records. Int. J. Medical Informatics 112: 59-67 (2018) - [j30]Angelia Nedic, Alex Olshevsky, Michael G. Rabbat:
Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization. Proc. IEEE 106(5): 953-976 (2018) - [j29]Alex Olshevsky:
On (Non)Supermodularity of Average Control Energy. IEEE Trans. Control. Netw. Syst. 5(3): 1177-1181 (2018) - 2017
- [j28]Thinh T. Doan, Alex Olshevsky:
Distributed resource allocation on dynamic networks in quadratic time. Syst. Control. Lett. 99: 57-63 (2017) - [j27]Alex Olshevsky:
Linear Time Average Consensus and Distributed Optimization on Fixed Graphs. SIAM J. Control. Optim. 55(6): 3990-4014 (2017) - [j26]Angelia Nedic, Alex Olshevsky, Wei Shi:
Achieving Geometric Convergence for Distributed Optimization Over Time-Varying Graphs. SIAM J. Optim. 27(4): 2597-2633 (2017) - [j25]Angelia Nedic, Alex Olshevsky, Cesar A. Uribe:
Fast Convergence Rates for Distributed Non-Bayesian Learning. IEEE Trans. Autom. Control. 62(11): 5538-5553 (2017) - 2016
- [j24]Alex Olshevsky:
Eigenvalue clustering, control energy, and logarithmic capacity. Syst. Control. Lett. 96: 45-50 (2016) - [j23]Julien M. Hendrickx, Alex Olshevsky:
On Symmetric Continuum Opinion Dynamics. SIAM J. Control. Optim. 54(5): 2893-2918 (2016) - [j22]Angelia Nedic, Alex Olshevsky:
Stochastic Gradient-Push for Strongly Convex Functions on Time-Varying Directed Graphs. IEEE Trans. Autom. Control. 61(12): 3936-3947 (2016) - [j21]Tamer Basar, Seyed Rasoul Etesami, Alex Olshevsky:
Convergence Time of Quantized Metropolis Consensus Over Time-Varying Networks. IEEE Trans. Autom. Control. 61(12): 4048-4054 (2016) - 2015
- [j20]Vincent D. Blondel, Raphaël M. Jungers, Alex Olshevsky:
On primitivity of sets of matrices. Autom. 61: 80-88 (2015) - [j19]Naomi Ehrich Leonard, Alex Olshevsky:
Cooperative Learning in Multiagent Systems from Intermittent Measurements. SIAM J. Control. Optim. 53(1): 1-29 (2015) - [j18]Angelia Nedic, Alex Olshevsky:
Distributed Optimization Over Time-Varying Directed Graphs. IEEE Trans. Autom. Control. 60(3): 601-615 (2015) - [j17]Peter Davison, Naomi Ehrich Leonard, Alex Olshevsky, Michael Schwemmer:
Nonuniform Line Coverage From Noisy Scalar Measurements. IEEE Trans. Autom. Control. 60(7): 1975-1980 (2015) - 2014
- [j16]Julien M. Hendrickx, Raphaël M. Jungers, Alexander Olshevsky, Guillaume Vankeerberghen:
Graph diameter, eigenvalues, and minimum-time consensus. Autom. 50(2): 635-640 (2014) - [j15]Alex Olshevsky:
Consensus with Ternary Messages. SIAM J. Control. Optim. 52(2): 987-1009 (2014) - [j14]Vincent D. Blondel, Alex Olshevsky:
How to Decide Consensus? A Combinatorial Necessary and Sufficient Condition and a Proof that Consensus is Decidable but NP-Hard. SIAM J. Control. Optim. 52(5): 2707-2726 (2014) - [j13]Alex Olshevsky:
Minimal Controllability Problems. IEEE Trans. Control. Netw. Syst. 1(3): 249-258 (2014) - 2013
- [j12]Amir Ali Ahmadi, Alexander Olshevsky, Pablo A. Parrilo, John N. Tsitsiklis:
NP-hardness of deciding convexity of quartic polynomials and related problems. Math. Program. 137(1-2): 453-476 (2013) - [j11]Alex Olshevsky, John N. Tsitsiklis:
Degree Fluctuations and the Convergence Time of Consensus Algorithms. IEEE Trans. Autom. Control. 58(10): 2626-2631 (2013) - [j10]Naomi Ehrich Leonard, Alex Olshevsky:
Nonuniform Coverage Control on the Line. IEEE Trans. Autom. Control. 58(11): 2743-2755 (2013) - 2011
- [j9]Alexander Olshevsky, John N. Tsitsiklis:
Convergence Speed in Distributed Consensus and Averaging. SIAM Rev. 53(4): 747-772 (2011) - [j8]Julien M. Hendrickx, Alexander Olshevsky, John N. Tsitsiklis:
Distributed Anonymous Discrete Function Computation. IEEE Trans. Autom. Control. 56(10): 2276-2289 (2011) - [j7]Alexander Olshevsky, John N. Tsitsiklis:
A Lower Bound for Distributed Averaging Algorithms on the Line Graph. IEEE Trans. Autom. Control. 56(11): 2694-2698 (2011) - 2010
- [j6]Julien M. Hendrickx, Alexander Olshevsky:
Matrix p-Norms Are NP-Hard to Approximate If p!=q1, 2, INFINITY. SIAM J. Matrix Anal. Appl. 31(5): 2802-2812 (2010) - 2009
- [j5]Alexander Olshevsky, John N. Tsitsiklis:
Convergence Speed in Distributed Consensus and Averaging. SIAM J. Control. Optim. 48(1): 33-55 (2009) - [j4]Leonid Gurvits, Alexander Olshevsky:
On the NP-Hardness of Checking Matrix Polytope Stability and Continuous-Time Switching Stability. IEEE Trans. Autom. Control. 54(2): 337-341 (2009) - [j3]Angelia Nedic, Alexander Olshevsky, Asuman E. Ozdaglar, John N. Tsitsiklis:
On Distributed Averaging Algorithms and Quantization Effects. IEEE Trans. Autom. Control. 54(11): 2506-2517 (2009) - 2008
- [j2]Alexander Olshevsky, John N. Tsitsiklis:
On the Nonexistence of Quadratic Lyapunov Functions for Consensus Algorithms. IEEE Trans. Autom. Control. 53(11): 2642-2645 (2008) - 2005
- [j1]Marek Karpinski, Ion I. Mandoiu, Alexander Olshevsky, Alexander Zelikovsky:
Improved Approximation Algorithms for the Quality of Service Multicast Tree Problem. Algorithmica 42(2): 109-120 (2005)
Conference and Workshop Papers
- 2023
- [c44]Haoxing Tian, Ioannis Ch. Paschalidis, Alex Olshevsky:
On the Performance of Temporal Difference Learning With Neural Networks. ICLR 2023 - [c43]Haoxing Tian, Alex Olshevsky, Yannis Paschalidis:
Convergence of Actor-Critic with Multi-Layer Neural Networks. NeurIPS 2023 - 2021
- [c42]Rui Liu, Alex Olshevsky:
Temporal Difference Learning as Gradient Splitting. ICML 2021: 6905-6913 - [c41]Artin Spiridonoff, Alex Olshevsky, Yannis Paschalidis:
Communication-efficient SGD: From Local SGD to One-Shot Averaging. NeurIPS 2021: 24313-24326 - 2020
- [c40]Venkatesh Saligrama, Alexander Olshevsky, Julien M. Hendrickx:
Minimax Rank-$1$ Matrix Factorization. AISTATS 2020: 3426-3436 - [c39]Julien M. Hendrickx, Alex Olshevsky, Venkatesh Saligrama:
Minimax Rate for Learning From Pairwise Comparisons in the BTL Model. ICML 2020: 4193-4202 - [c38]Qianqian Ma, Alex Olshevsky:
Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion. NeurIPS 2020 - 2019
- [c37]Olivier Bronchain, Julien M. Hendrickx, Clément Massart, Alex Olshevsky, François-Xavier Standaert:
Leakage Certification Revisited: Bounding Model Errors in Side-Channel Security Evaluations. CRYPTO (1) 2019: 713-737 - [c36]Julien M. Hendrickx, Alexander Olshevsky, Venkatesh Saligrama:
Graph Resistance and Learning from Pairwise Comparisons. ICML 2019: 2702-2711 - 2018
- [c35]Ali Jadbabaie, Alex Olshevsky, Milad Siami:
Limitations and Tradeoffs in Minimum Input Selection Problems. ACC 2018: 185-190 - [c34]Alex Olshevsky, Ioannis Ch. Paschalidis, Artin Spiridonoff:
Fully Asynchronous Push-Sum With Growing Intercommunication Intervals. ACC 2018: 591-596 - [c33]Angelia Nedic, Alex Olshevsky, Wei Shi:
Improved Convergence Rates for Distributed Resource Allocation. CDC 2018: 172-177 - [c32]Yao Ma, Alexander Olshevsky, Csaba Szepesvári, Venkatesh Saligrama:
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers. ICML 2018: 3341-3350 - 2017
- [c31]Angelia Nedic, Alex Olshevsky, Wei Shi, Cesar A. Uribe:
Geometrically convergent distributed optimization with uncoordinated step-sizes. ACC 2017: 3950-3955 - 2016
- [c30]Angelia Nedic, Alex Olshevsky, Cesar A. Uribe:
Distributed Gaussian learning over time-varying directed graphs. ACSSC 2016: 1710-1714 - [c29]Angelia Nedic, Alex Olshevsky, Cesar A. Uribe:
Network independent rates in distributed learning. ACC 2016: 1072-1077 - [c28]Ali Jadbabaie, Alex Olshevsky:
On performance of consensus protocols subject to noise: Role of hitting times and network structure. CDC 2016: 179-184 - [c27]Angelia Nedich, Alex Olshevsky, Wei Shi:
A geometrically convergent method for distributed optimization over time-varying graphs. CDC 2016: 1023-1029 - [c26]Angelia Nedic, Alex Olshevsky, Cesar A. Uribe:
Distributed learning with infinitely many hypotheses. CDC 2016: 6321-6326 - [c25]Angelia Nedic, Alex Olshevsky, Cesar A. Uribe:
A tutorial on distributed (non-Bayesian) learning: Problem, algorithms and results. CDC 2016: 6795-6801 - [c24]Alex Olshevsky:
Fast algorithms for distributed optimization and hypothesis testing: A tutorial. CDC 2016: 6802-6807 - [c23]Angelia Nedic, Alex Olshevsky, Wei Shi:
Linearly convergent decentralized consensus optimization over directed networks. GlobalSIP 2016: 485-489 - 2015
- [c22]Alex Olshevsky:
Minimum input selection for structural controllability. ACC 2015: 2218-2223 - [c21]Angelia Nedic, Alex Olshevsky, Cesar A. Uribe:
Nonasymptotic convergence rates for cooperative learning over time-varying directed graphs. ACC 2015: 5884-5889 - 2014
- [c20]Tamer Basar, Seyed Rasoul Etesami, Alex Olshevsky:
Fast convergence of quantized consensus using Metropolis chains. CDC 2014: 1330-1334 - 2013
- [c19]Vincent D. Blondel, Raphaël M. Jungers, Alex Olshevsky:
On primitivity of sets of matrices. CDC 2013: 1360-1365 - [c18]Julien M. Hendrickx, Alex Olshevsky:
Symmetric continuum opinion dynamics: Convergence, but sometimes only in distribution. CDC 2013: 1989-1994 - [c17]Alex Olshevsky:
Consensus with ternary messages. CDC 2013: 6213-6217 - [c16]Angelia Nedic, Alex Olshevsky:
Distributed optimization over time-varying directed graphs. CDC 2013: 6855-6860 - [c15]Naomi Ehrich Leonard, Alex Olshevsky:
Cooperative learning in multi-agent systems from intermittent measurements. CDC 2013: 7492-7497 - [c14]Ali Jadbabaie, Alex Olshevsky:
Combinatorial bounds and scaling laws for noise amplification in networks. ECC 2013: 596-601 - [c13]Angelia Nedic, Alex Olshevsky:
Distributed optimization of strongly convex functions on directed time-varying graphs. GlobalSIP 2013: 329-332 - 2012
- [c12]Vincent D. Blondel, Alex Olshevsky:
On the cost of deciding consensus. CDC 2012: 2213-2218 - 2011
- [c11]Naomi Ehrich Leonard, Alexander Olshevsky:
Nonuniform coverage control on the line. CDC/ECC 2011: 753-758 - [c10]Alexander Olshevsky, John N. Tsitsiklis:
Degree fluctuations and the convergence time of consensus algorithms. CDC/ECC 2011: 6602-6607 - 2010
- [c9]Alexander Olshevsky, John N. Tsitsiklis:
A lower bound for distributed averaging algorithms on the line graph. CDC 2010: 4523-4528 - 2009
- [c8]Julien M. Hendrickx, Alex Olshevsky, John N. Tsitsiklis:
Distributed anonymous function computation in information fusion and multiagent systems. Allerton 2009: 1582-1589 - 2008
- [c7]Angelia Nedic, Alexander Olshevsky, Asuman E. Ozdaglar, John N. Tsitsiklis:
Distributed subgradient methods and quantization effects. CDC 2008: 4177-4184 - [c6]Angelia Nedic, Alexander Olshevsky, Asuman E. Ozdaglar, John N. Tsitsiklis:
On distributed averaging algorithms and quantization effects. CDC 2008: 4825-4830 - 2006
- [c5]Alex Olshevsky, John N. Tsitsiklis:
Convergence Rates in Distributed Consensus and Averaging. CDC 2006: 3387-3392 - 2005
- [c4]Vincent D. Blondel, Julien M. Hendrickx, Alex Olshevsky, John N. Tsitsiklis:
Convergence in Multiagent Coordination, Consensus, and Flocking. CDC/ECC 2005: 2996-3000 - 2003
- [c3]Gruia Calinescu, Sanjiv Kapoor, Alexander Olshevsky, Alexander Zelikovsky:
Network Lifetime and Power Assignment in ad hoc Wireless Networks. ESA 2003: 114-126 - [c2]Gruia Calinescu, Cristina G. Fernandes, Ion I. Mandoiu, Alexander Olshevsky, K. Yang, Alexander Zelikovsky:
Primal-dual algorithms for QoS multimedia multicast. GLOBECOM 2003: 3631-3635 - [c1]Marek Karpinski, Ion I. Mandoiu, Alexander Olshevsky, Alexander Zelikovsky:
Improved Approximation Algorithms for the Quality of Service Steiner Tree Problem. WADS 2003: 401-411
Parts in Books or Collections
- 2018
- [p1]Ion I. Mandoiu, Alex Olshevsky, Alexander Zelikovsky:
QoS Multimedia Multicast Routing. Handbook of Approximation Algorithms and Metaheuristics (2) 2018
Reference Works
- 2007
- [r1]Ion I. Mandoiu, Alex Olshevsky, Alexander Zelikovsky:
QoS Multimedia Multicast Routing. Handbook of Approximation Algorithms and Metaheuristics 2007
Informal and Other Publications
- 2024
- [i57]Julien M. Hendrickx, Alex Olshevsky:
Convex SGD: Generalization Without Early Stopping. CoRR abs/2401.04067 (2024) - [i56]Haoxing Tian, Ioannis Ch. Paschalidis, Alex Olshevsky:
One-Shot Averaging for Distributed TD(λ) Under Markov Sampling. CoRR abs/2403.08896 (2024) - [i55]Amirreza Neshaei Moghaddam, Alex Olshevsky, Bahman Gharesifard:
Sample Complexity of the Linear Quadratic Regulator: A Reinforcement Learning Lens. CoRR abs/2404.10851 (2024) - [i54]Arsenii Mustafin, Alex Olshevsky, Ioannis Ch. Paschalidis:
On Value Iteration Convergence in Connected MDPs. CoRR abs/2406.09592 (2024) - [i53]Ryan Yu, Alex Olshevsky, Peter Chin:
Tree Search for Simultaneous Move Games via Equilibrium Approximation. CoRR abs/2406.10411 (2024) - [i52]Arsenii Mustafin, Aleksei Pakharev, Alex Olshevsky, Ioannis Ch. Paschalidis:
MDP Geometry, Normalization and Value Free Solvers. CoRR abs/2407.06712 (2024) - [i51]Mahtab Talaei, Apostolos I. Rikos, Alex Olshevsky, Laura F. White, Ioannis Ch. Paschalidis:
Network-Based Epidemic Control Through Optimal Travel and Quarantine Management. CoRR abs/2407.19133 (2024) - 2023
- [i50]Rui Liu, Alex Olshevsky:
Distributed TD(0) with Almost No Communication. CoRR abs/2305.16246 (2023) - [i49]Haoxing Tian, Ioannis Ch. Paschalidis, Alex Olshevsky:
On the Performance of Temporal Difference Learning With Neural Networks. CoRR abs/2312.05397 (2023) - 2022
- [i48]Alex Olshevsky, Bahman Gharesifard:
A Small Gain Analysis of Single Timescale Actor Critic. CoRR abs/2203.02591 (2022) - [i47]Arsenii Mustafin, Alex Olshevsky, Ioannis Ch. Paschalidis:
Closing the gap between SVRG and TD-SVRG with Gradient Splitting. CoRR abs/2211.16237 (2022) - 2021
- [i46]Rui Liu, Alex Olshevsky:
Distributed TD(0) with Almost No Communication. CoRR abs/2104.07855 (2021) - [i45]Artin Spiridonoff, Alex Olshevsky, Ioannis Ch. Paschalidis:
Communication-efficient SGD: From Local SGD to One-Shot Averaging. CoRR abs/2106.04759 (2021) - 2020
- [i44]Alex Olshevsky:
Asymptotic Network Independence and Step-Size for A Distributed Subgradient Method. CoRR abs/2003.06739 (2020) - [i43]Artin Spiridonoff, Alex Olshevsky, Ioannis Ch. Paschalidis:
Local SGD With a Communication Overhead Depending Only on the Number of Workers. CoRR abs/2006.02582 (2020) - [i42]Rui Liu, Alex Olshevsky:
Asymptotic Convergence Rate of Alternating Minimization for Rank One Matrix Completion. CoRR abs/2008.04988 (2020) - [i41]Qianqian Ma, Alex Olshevsky:
Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion. CoRR abs/2010.12181 (2020) - [i40]Rui Liu, Alex Olshevsky:
Temporal Difference Learning as Gradient Splitting. CoRR abs/2010.14657 (2020) - 2019
- [i39]Julien M. Hendrickx, Alex Olshevsky, Venkatesh Saligrama:
Graph Resistance and Learning from Pairwise Comparisons. CoRR abs/1902.00141 (2019) - [i38]Alex Olshevsky:
On the Inapproximability of the Discrete Witsenhausen Problem. CoRR abs/1904.05701 (2019) - [i37]Yao Ma, Alex Olshevsky, Venkatesh Saligrama, Csaba Szepesvári:
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers. CoRR abs/1904.11608 (2019) - [i36]Alex Olshevsky, Ioannis Ch. Paschalidis, Shi Pu:
A Non-Asymptotic Analysis of Network Independence for Distributed Stochastic Gradient Descent. CoRR abs/1906.02702 (2019) - [i35]Alex Olshevsky, Ioannis Ch. Paschalidis, Shi Pu:
Asymptotic Network Independence in Distributed Optimization for Machine Learning. CoRR abs/1906.12345 (2019) - [i34]Alex Olshevsky:
On A Relaxation of Time-Varying Actuator Placement. CoRR abs/1912.09454 (2019) - [i33]Olivier Bronchain, Julien M. Hendrickx, Clément Massart, Alex Olshevsky, François-Xavier Standaert:
Leakage Certification Revisited: Bounding Model Errors in Side-Channel Security Evaluations. IACR Cryptol. ePrint Arch. 2019: 132 (2019) - 2018
- [i32]Ali Jadbabaie, Alex Olshevsky, Milad Siami:
Deterministic and Randomized Actuator Scheduling With Guaranteed Performance Bounds. CoRR abs/1805.00606 (2018) - [i31]Angelia Nedic, Alex Olshevsky, César A. Uribe:
Graph-Theoretic Analysis of Belief System Dynamics under Logic Constraints. CoRR abs/1810.02456 (2018) - 2017
- [i30]Angelia Nedic, Alex Olshevsky, César A. Uribe:
Distributed Learning for Cooperative Inference. CoRR abs/1704.02718 (2017) - [i29]Angelia Nedic, Alex Olshevsky, Wei Shi:
Improved Convergence Rates for Distributed Resource Allocation. CoRR abs/1706.05441 (2017) - [i28]Yao Ma, Alex Olshevsky, Venkatesh Saligrama, Csaba Szepesvári:
Crowdsourcing with Sparsely Interacting Workers. CoRR abs/1706.06660 (2017) - [i27]Angelia Nedic, Alex Olshevsky, Michael G. Rabbat:
Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization. CoRR abs/1709.08765 (2017) - [i26]Ali Jadbabaie, Alexander Olshevsky, George J. Pappas, Vasileios Tzoumas:
Minimal Reachability is Hard To Approximate. CoRR abs/1710.10244 (2017) - 2016
- [i25]Angelia Nedic, Alex Olshevsky, Cesar A. Uribe:
Distributed Learning with Infinitely Many Hypotheses. CoRR abs/1605.02105 (2016) - [i24]Angelia Nedic, Alex Olshevsky, Wei Shi, Cesar A. Uribe:
Geometrically Convergent Distributed Optimization with Uncoordinated Step-Sizes. CoRR abs/1609.05877 (2016) - [i23]Angelia Nedic, Alex Olshevsky, Cesar A. Uribe:
A Tutorial on Distributed (Non-Bayesian) Learning: Problem, Algorithms and Results. CoRR abs/1609.07537 (2016) - [i22]Alex Olshevsky:
On (Non)Supermodularity of Average Control Energy. CoRR abs/1609.08706 (2016) - [i21]Angelia Nedic, Alex Olshevsky, Cesar A. Uribe:
Distributed Gaussian Learning over Time-varying Directed Graphs. CoRR abs/1612.01600 (2016) - 2015
- [i20]Tamer Basar, Seyed Rasoul Etesami, Alex Olshevsky:
Fast Convergence of Quantized Consensus Using Metropolis Chains Over Static and Dynamic Networks. CoRR abs/1504.01438 (2015) - [i19]Ali Jadbabaie, Alex Olshevsky:
On performance of consensus protocols subject to noise: role of hitting times and network structure. CoRR abs/1508.00036 (2015) - [i18]Alex Olshevsky:
Eigenvalue Clustering, Control Energy, and Logarithmic Capacity. CoRR abs/1511.00205 (2015) - 2014
- [i17]Angelia Nedic, Alex Olshevsky:
Stochastic Gradient-Push for Strongly Convex Functions on Time-Varying Directed Graphs. CoRR abs/1406.2075 (2014) - [i16]Alex Olshevsky:
Minimum Input Selection for Structural Controllability. CoRR abs/1407.2884 (2014) - [i15]Alex Olshevsky:
Average Consensus in Nearly Linear Time on Fixed Graphs and Implications for Decentralized Optimization and Multi-Agent Control. CoRR abs/1411.4186 (2014) - 2013
- [i14]Angelia Nedic, Alexander Olshevsky:
Distributed optimization over time-varying directed graphs. CoRR abs/1303.2289 (2013) - [i13]Alex Olshevsky:
The Minimal Controllability Problem. CoRR abs/1304.3071 (2013) - [i12]Vincent D. Blondel, Raphaël M. Jungers, Alex Olshevsky:
On Primitivity of Sets of Matrices. CoRR abs/1306.0729 (2013) - [i11]Peter Davison, Naomi Ehrich Leonard, Alex Olshevsky, Michael Schwemmer:
Nonuniform Line Coverage from Noisy Scalar Measurements. CoRR abs/1310.4188 (2013) - [i10]Julien M. Hendrickx, Alex Olshevsky:
On symmetric continuum opinion dynamics. CoRR abs/1311.0355 (2013) - 2012
- [i9]Naomi Ehrich Leonard, Alexander Olshevsky:
Cooperative learning in multi-agent systems from intermittent measurements. CoRR abs/1209.2194 (2012) - [i8]Julien M. Hendrickx, Raphaël M. Jungers, Alexander Olshevsky, Guillaume Vankeerberghen:
Diameter, Optimal Consensus, and Graph Eigenvalues. CoRR abs/1211.6324 (2012) - [i7]Alex Olshevsky:
Consensus with Ternary Messages. CoRR abs/1212.5768 (2012) - 2011
- [i6]Alexander Olshevsky, John N. Tsitsiklis:
Degree Fluctuations and the Convergence Time of Consensus Algorithms. CoRR abs/1104.0454 (2011) - [i5]Naomi Ehrich Leonard, Alexander Olshevsky:
Nonuniform Coverage Control on the Line. CoRR abs/1104.0457 (2011) - 2010
- [i4]Julien M. Hendrickx, Alexander Olshevsky, John N. Tsitsiklis:
Distributed anonymous discrete function computation. CoRR abs/1004.2102 (2010) - [i3]Amir Ali Ahmadi, Alexander Olshevsky, Pablo A. Parrilo, John N. Tsitsiklis:
NP-hardness of Deciding Convexity of Quartic Polynomials and Related Problems. CoRR abs/1012.1908 (2010) - 2009
- [i2]Julien M. Hendrickx, Alexander Olshevsky, John N. Tsitsiklis:
Distributed anonymous function computation in information fusion and multiagent systems. CoRR abs/0907.2949 (2009) - [i1]Julien M. Hendrickx, Alexander Olshevsky:
Matrix P-norms are NP-hard to approximate if p \neq 1,2,\infty. CoRR abs/0908.1397 (2009)
Coauthor Index
Angelia Nedic
aka: Angelia Nedich
aka: Angelia Nedich
Ioannis Ch. Paschalidis
aka: Yannis Paschalidis
aka: Yannis Paschalidis
César A. Uribe
aka: Cesar A. Uribe
aka: Cesar A. Uribe
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