127 results on '"Alex Olshevsky"'
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2. One-Shot Averaging for Distributed TD (λ) Under Markov Sampling.
3. MDP Geometry, Normalization and Value Free Solvers.
4. Network-Based Epidemic Control Through Optimal Travel and Quarantine Management.
5. On Value Iteration Convergence in Connected MDPs.
6. Tree Search for Simultaneous Move Games via Equilibrium Approximation.
7. One-Shot Averaging for Distributed TD(λ) Under Markov Sampling.
8. Sample Complexity of the Linear Quadratic Regulator: A Reinforcement Learning Lens.
9. Convex SGD: Generalization Without Early Stopping.
10. Distributed TD(0) With Almost No Communication.
11. A Small Gain Analysis of Single Timescale Actor Critic.
12. On the Performance of Temporal Difference Learning With Neural Networks.
13. Convergence of Actor-Critic with Multi-Layer Neural Networks.
14. Nonasymptotic Concentration Rates in Cooperative Learning-Part I: Variational Non-Bayesian Social Learning.
15. Nonasymptotic Concentration Rates in Cooperative Learning - Part II: Inference on Compact Hypothesis Sets.
16. Asymptotic Network Independence and Step-Size for a Distributed Subgradient Method.
17. A Sharp Estimate on the Transient Time of Distributed Stochastic Gradient Descent.
18. Communication-efficient SGD: From Local SGD to One-Shot Averaging.
19. Temporal Difference Learning as Gradient Splitting.
20. Minimax Rate for Learning From Pairwise Comparisons in the BTL Model.
21. Asymptotic Convergence Rate of Alternating Minimization for Rank One Matrix Completion.
22. A Small Gain Analysis of Single Timescale Actor Critic.
23. Closing the gap between SVRG and TD-SVRG with Gradient Splitting.
24. Leakage Certification Revisited: Bounding Model Errors in Side-Channel Security Evaluations.
25. On a Relaxation of Time-Varying Actuator Placement.
26. Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning: Examining Distributed and Centralized Stochastic Gradient Descent.
27. Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers.
28. Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions.
29. Fully Asynchronous Push-Sum With Growing Intercommunication Intervals.
30. Limitations and Tradeoffs in Minimum Input Selection Problems.
31. Improved Convergence Rates for Distributed Resource Allocation.
32. Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion.
33. Distributed TD(0) with Almost No Communication.
34. On the Inapproximability of the Discrete Witsenhausen Problem.
35. Scaling Laws for Consensus Protocols Subject to Noise.
36. Geometrically convergent distributed optimization with uncoordinated step-sizes.
37. On (Non)Supermodularity of Average Control Energy.
38. Federated learning of predictive models from federated Electronic Health Records.
39. Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization.
40. Temporal Difference Learning as Gradient Splitting.
41. Local SGD With a Communication Overhead Depending Only on the Number of Workers.
42. Asymptotic Network Independence and Step-Size for A Distributed Subgradient Method.
43. Asymptotic Convergence Rate of Alternating Minimization for Rank One Matrix Completion.
44. Network independent rates in distributed learning.
45. Distributed Gaussian learning over time-varying directed graphs.
46. Linearly convergent decentralized consensus optimization over directed networks.
47. A geometrically convergent method for distributed optimization over time-varying graphs.
48. On performance of consensus protocols subject to noise: Role of hitting times and network structure.
49. Fast algorithms for distributed optimization and hypothesis testing: A tutorial.
50. A tutorial on distributed (non-Bayesian) learning: Problem, algorithms and results.
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