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• Sam Madden, assistant professor of electrical engineering and computer science and a member of the Computer Science and Artificial Intelligence Laboratory, who received his PhD from University of California–Berkeley • Pablo Parrilo, assistant professor of electrical engineering and computer science,

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Elad Hoffer, Itay Hubara, Daniel Soudry - Fix your classifier: the marginal value of training the last weight layer - ICLR 2018 . Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Nathan Srebro - The Implicit Bias of Gradient Descent on Separable Data - ICLR 2018

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Nathan Srebro NATI@TTIC.EDU Abstract We study implicit regularization when optimizing an underdetermined quadratic objective over a matrix X with gradient descent on a factorization of X. We conjecture and provide empirical and theoretical evidence that

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Nathan Srebro’s PhD thesis [SRJ04] summarizes a number of models ex- tending the framework to other loss functions (e.g., hinge loss and KL-divergence …

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Maximum Margin Matrix Factorization Code Publications Maximum Margin Matrix Factorizations Nathan Srebro, Jason Rennie and Tommi Jaakkola Advances in Neural Information Processing Systems (NIPS) 17, 2005 (December 2004 conference), [Slides in PDF], [Poster in PDF] We present a novel approach to collaborative prediction, using low-norm instead of low-rank factorizations.

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Rebecca Roelofs . I completed my PhD in Computer Science at UC Berkeley in June 2019. I was co-advised by Benjamin Recht and Jim Demmel.. My research interests revolve around the foundations of machine learning, often with the goal of making machine learning more reliable.

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Combined with subsequent paper by Nathan Srebro in journal version, Machine Learning Journal, 72(1-2):89--112, 2008. DOI 10.1007/s10994-008-5059-5. Special issue of for COLT 2007 (Invited). On PhD Thesis, 2008 Winner of the CMU SCS Distinguished Dissertation Award.

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Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence.

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I am currently an assistant Professor at the department of Computer Science at Cornell University. Prior to that I was a postdoctoral research associate at the University of Pennsylvania, jointly with Alexander Rakhlin and Michael Kearns. I obtained my PhD from Toyota Technological Institute at Chicago where my thesis advisor was Nathan Srebro

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with Professor Mikhail Belkin. Without his guidance and persistent help this dissertation would not been possible. It is with immense gratitude that I acknowledge the support of Professor Pedro F. Felzenszwalb and Professor L. Ridgway Scott during the most di–cult times of my studies. I would like to thank Professor Nathan Srebro for his

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Nathan Srebro | Department of Statistics and Data Science

Sivan Sabato, Shai Shalev-Shwartz, Nathan Srebro, Daniel Hsu, Tong Zhang. Journal of Machine Learning Research, 16(Jul):1275-1304, 2015. Faster Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization. Shai Shalev-Shwartz and Tong Zhang. Mathematical Programming SERIES A and B (to look). [pdf on arxiv ]

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Nathan Srebro Professor Toyota Technological Institute at Chicago Professor (part time) Dept. of Computer Science and Committee on Computational and Applied Mathematics, University of Chicago. Past PhD Students: Karthik Sridharan (Currently assistant professor at Cornell) Rina Foygel Barber

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(ICA). Nathan Srebro’s PhD thesis [SRJ04] extended the framework to other loss functions (e.g., hinge loss and KL-divergence loss), and added nuclear norm and max-norm regularization. In [SG08], the authors offer a complete view of the state of the literature on matrix factorization as of 2008 in Table 1 of their paper. They note that by

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Nathan Srebro Collaborative prediction (CP) is a problem of predicting unobserved entries in sparsely observed matrices, e.g. product ratings by different users in online recommender systems.

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The problem of characterizing learnability is the most basic question of statistical learning theory. A fundamental and long-standing answer, at least for the case of supervised classification and regression, is that learnability is equivalent to uniform convergence of the empirical risk to the population risk, and that if a problem is learnable, it is learnable via empirical risk minimization.

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Email neyshabur(at)google.com Follow @bneyshabur Curriculum Vitae. I am a senior research scientist at Google. Before that, I was a postdoctoral researcher at New York University working with Yann LeCun and a member of Theoretical Machine Learning program led by Sanjeev Arora at Institute for Advanced Study (IAS) in Princeton. In summer 2017, I received a PhD …

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Pushkar Shukla. B.Tech.: Uttarakhand Technical University M.S.: University of California, Santa Barbara Area of Focus: Machine learning and optimization, Speech and language, Vision and graphicsRobotics, Speech and language, Vision and graphics Advisors: Greg Shakhnarovich and Matt Walter Email

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Dr. Nathan Srebro Professor PhD - MIT. Dr. Srebro is interested in statistical and computational aspects of machine learning, and the interaction between them. He has done theoretical work in statistical learning theory and in algorithms, devised novel learning models and optimization techniques, and has worked on applications in computational

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Learning From An Optimization Viewpoint by Karthik Sridharan Submitted to : Toyota Technological Institute at Chicago 6045 S. Kenwood Ave, Chicago, IL, 60637 For the degree of Doctor of Philosophy in Computer Science Thesis Committee : Nathan Srebro (Thesis Supervisor), David McAllester, Arkadi Nemirovski, Alexander Razborov