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- Volodymyr Kuleshov, Arun Tejasvi Chaganty, Percy Liang
- AISTATS
- 2015

Tensor factorization arises in many machine<lb>learning applications, such knowledge base<lb>modeling and parameter estimation in latent<lb>variable models. However, numerical meth-<lb>ods for tensor factorization have not reached<lb>the level of maturity of matrix factorization<lb>methods. In this paper, we propose a new<lb>method for CP tensor… (More)

- Volodymyr Kuleshov, Dan Xie, +5 authors Michael Snyder
- Nature biotechnology
- 2014

The rapid growth of sequencing technologies has greatly contributed to our understanding of human genetics. Yet, despite this growth, mainstream technologies have not been fully able to resolve the diploid nature of the human genome. Here we describe statistically aided, long-read haplotyping (SLRH), a rapid, accurate method that uses a statistical… (More)

- Volodymyr Kuleshov, Doina Precup
- ArXiv
- 2014

The stochastic multi-armed bandit problem is an important model for studying the explorationexploitation tradeoff in reinforcement learning. Although many algorithms for the problem are well-understood theoretically, empirical confirmation of their effectiveness is generally scarce. This paper presents a thorough empirical study of the most popular… (More)

The stochastic multi-armed bandit problem is an important model for studying the explorationexploitation tradeoff in reinforcement learning. Although many algorithms for the problem are well-understood theoretically, empirical confirmation of their effectiveness is generally scarce. This paper presents a thorough empirical study of the most popular… (More)

- Volodymyr Kuleshov
- Bioinformatics
- 2014

MOTIVATION
Accurate haplotyping-determining from which parent particular portions of the genome are inherited-is still mostly an unresolved problem in genomics. This problem has only recently started to become tractable, thanks to the development of new long read sequencing technologies. Here, we introduce ProbHap, a haplotyping algorithm targeted at such… (More)

- Volodymyr Kuleshov, Adrian Vetta
- SAGT
- 2010

We analyze the performance of resource allocation mechanisms for markets in which there is competition amongst both consumers and suppliers (namely, two-sided markets). Specifically, we examine a natural generalization of both Kelly’s proportional allocation mechanism for demand-competitive markets [9] and Johari and Tsitsiklis’ proportional allocation… (More)

- Volodymyr Kuleshov, Gordon T. Wilfong
- WINE
- 2012

- Volodymyr Kuleshov, Okke Schrijvers
- WINE
- 2015

One of the central questions in game theory deals with predicting the behavior of an agent. Here, we study the inverse of this problem: given the agents’ equilibrium behavior, what are possible utilities that motivate this behavior? We consider this problem in arbitrary normal-form games in which the utilities can be represented by a small number of… (More)

- Volodymyr Kuleshov, Chao Jiang, Wenyu Zhou, Fereshteh Kenari Jahanbani, Serafim Batzoglou, Michael Snyder
- Nature biotechnology
- 2016

Identifying bacterial strains in metagenome and microbiome samples using computational analyses of short-read sequences remains a difficult problem. Here, we present an analysis of a human gut microbiome using TruSeq synthetic long reads combined with computational tools for metagenomic long-read assembly, variant calling and haplotyping (Nanoscope and… (More)

- Volodymyr Kuleshov
- ICML
- 2013

We introduce new algorithms for sparse principal component analysis (sPCA), a variation of PCA which aims to represent data in a sparse low-dimensional basis. Our algorithms possess a cubic rate of convergence and can compute principal components with k non-zero elements at a cost of O(nk + k) flops per iteration. We observe in numerical experiments that… (More)