Martin Zinkevich

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<lb>With the increase in available data parallel machine learning has become an in-<lb>creasingly pressing problem. In this paper we present the first parallel stochastic<lb>gradient descent algorithm including a detailed analysis and experimental evi-<lb>dence. Unlike prior work on parallel optimization algorithms [5, 7] our variant<lb>comes with parallel(More)
Promising approaches to structured learning problems have recently been developed in the maximum margin framework. Unfortunately, algorithms that are computationally and memory efficient enough to solve large scale problems have lagged behind. We propose using simple subgradient-based techniques for optimizing a regularized risk formulation of these(More)
Online learning algorithms have impressive convergence properties when it comes to risk minimization and convex games on very large problems. However, they are inherently sequential in their design which prevents them from taking advantage of modern multi-core architectures. In this paper we prove that online learning with delayed updates converges well,(More)
Sequential decision-making with multiple agents and imperfect information is commonly modeled as an extensive game. One efficient method for computing Nash equilibria in large, zero-sum, imperfect information games is counterfactual regret minimization (CFR). In the domain of poker, CFR has proven effective, particularly when using a domain-specific(More)
Maximum margin structured learning (MMSL) has recently gained recognition within the machine learning community as a tractable method for large scale learning. However, most current methods are limited in terms of scalability, convergence, or memory requirements. The original Structured SMO method proposed in (Taskar et al., 2003) is slow to converge,(More)
This paper examines the notion of symmetry in Markov decision processes (MDPs). We define symmetry for an MDP and show how it can be exploited for more effective learning in single agent systems as well as multiagent systems and multirobot systems. We prove that if an MDP possesses a symmetry, then the optimal value function andQ function are similarly(More)