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Markus Weimer

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

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NeurIPS Conference 2010 Conference Paper

Parallelized Stochastic Gradient Descent

Martin Zinkevich
Markus Weimer
Lihong Li
Alex Smola

With the increase in available data parallel machine learning has become an increasingly pressing problem. In this paper we present the first parallel stochastic gradient descent algorithm including a detailed analysis and experimental evidence. Unlike prior work on parallel optimization algorithms our variant comes with parallel acceleration guarantees and it poses no overly tight latency constraints, which might only be available in the multicore setting. Our analysis introduces a novel proof technique --- contractive mappings to quantify the speed of convergence of parameter distributions to their asymptotic limits. As a side effect this answers the question of how quickly stochastic gradient descent algorithms reach the asymptotically normal regime.

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NeurIPS Conference 2007 Conference Paper

COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking

Markus Weimer
Alexandros Karatzoglou
Quoc Le
Alex Smola

In this paper, we consider collaborative ﬁltering as a ranking problem. We present a method which uses Maximum Margin Matrix Factorization and optimizes rank- ing instead of rating. We employ structured output prediction to optimize directly for ranking scores. Experimental results show that our method gives very good ranking scores and scales well on collaborative ﬁltering tasks.

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