Samet oymak information theory pdf

Ieee international symposium on information theory, 20. By christos thrampoulidis, samet oymak and babak hassibi. Neural information processing systems neurips, 2014. Download pdf proceedings of machine learning research. Pdf stochastic gradient descent learns state equations. Statistics machine learning, computer science information theory. Samet oymak university of california, riverside mahdi soltanolkotabi university of southern california mahdi soltanolkotabi is an assistant professor in the ming hsieh department of electrical and computer engineering and computer science at the university of southern california where he holds an andrew and erna viterbi early career chair. The heckscherohlin theory explains why countries trade goods and services with each other, the emphasize being on the difference of resources between two countries.

To view the rest of this content please follow the download pdf link above. Samet oymak, zalan fabian, mingchen li, mahdi soltanolkotabi sep 25, 2019 blind submission readers. The problem of signal recovery from the autocorrelation, or equivalently, the magnitudes of the fourier transform, is of paramount importance in various fields of engineering. Department of electrical engineering, california institute of technology, pasadena, ca, usa. Samet oymak and mahdi soltanolkotabi \fast and reliable parameter estimation from nonlinear observations, siam journal on. Information theory and its relation to machine learning article pdf available in lecture notes in electrical engineering 336 january 2015 with 1,324 reads how we measure reads. Simonsberkeley research fellowship on information theory, spring 2015. Examples include recovering sparse or groupsparse vectors, lowrank matrices, and the sum of sparse and lowrank matrices, among others.

The topic of recovery of a structured model given a small number of linear observations has been wellstudied in recent years. Phase retrieval under a generative prior proceedings of. Learning compact neural networks with regularization samet oymak1 abstract proper regularization is critical for speeding up training, improving generalization performance, and learning compact models that are cost ef. Samet oymak academic experience research interests. Approximation power of random neural networks deepai. Weiyu xu, samet oymak, juhwan yoo and matthew thill. A survey of spectral factorization methods sayed 2001. Living on the edge all faculty duke electrical and. Subspace expanders and matrix rank minimization core. We focus on the minimization of a leastsquares objective. Samet oymak, benjamin recht, and mahdi soltanolkotabi. The typical scenario that arises in most big data problems is one where the ambient dimension of the signal is very large e. Simple bounds for noisy linear inverse problems with exact side information.

We analyze two programs based on regularized nuclear norm minimization, with a goal to recover the low rank part of the adjacency matrix. In various applications in signal processing and machine learning, the model of interest is known to be structured in several. Xinghao pan dimitris papailiopoulos samet oymak benjamin recht kannan ramchandran michael jordan 2014 poster. Learning compact neural networks with regularization. The phase transition of matrix recovery from gaussian. Petersburg,russia 31 july 5 august2011 pages22633016 4ieee ieeecatalognumber. Sharp timedata tradeoffs for linear inverse problems core. Recovery of sparse 1d signals from the magnitudes of. Date name title of presentation 432019 yuwei hsieh, the university of southern california a semiparametric discretechoice aggregate. All presentations will use my own laptop, and thus need to be turned in by noon. Samet oymak, mahdi soltanolkotabi, and benjamin recht \sharp timedata tradeo s for linear inverse problems, transactions on information theory, june 2018.

Mathematics genealogy project department of mathematics north dakota state university p. Ranked 1st in electrical engineering qualifying exam, caltech, january 2010. Connecting clustering to classification yingzhen yang feng liang shuicheng yan zhangyang wang thomas s huang pdf. An unbiased approach to low rank recovery request pdf. Sample complexity of kalman filtering for unknown systems. While the rank function is useful for regularization it.

Anilesh kollagunta krishnaswamy stanford university. Request pdf an unbiased approach to low rank recovery low rank recovery problems have been a subject of intense study in recent years. These results provide a precise understanding of the various tradeoffs involved between statistical and computational resources as well as a priori side information available for such nonlinear parameter estimation problems. Radha krishna ganti \coverage and rate in cellular networks with multiuser spatial multiplexing, sreejith t. Information theory, inference, and learning algorithms david j. We empirically demonstrate that the jacobian of neural networks exhibit a lowrank structure and harness this property to develop new optimization and generalization guarantees. Time series prediction is a fundamental problem across control theory. Neural information processing systems nips 2014, montreal, canada. On a theory of nonparametric pairwise similarity for clustering. The gaussian minmax theorem in the presence of convexity.

Phase transitions in random convex programs joel a. By samet oymak, benjamin recht and mahdi soltanolkotabi. Generalization guarantees for neural networks via harnessing the lowrank structure of the jacobian samet oymak. In rm problem, one aims to find the matrix with the lowest rank that satisfies a set of linear constraints. View samet oymaks profile on linkedin, the worlds largest professional community. Eldar, fellow, ieee, and babak hassibi, member, ieee abstractrecovering structured models e. Vaidyanathan, alex dimakis and tracy ho for the insightful comments on this dissertation. Isometric sketching of arbitrary sets via the restricted isometry property. Universality in learning from linear measurements nips. Information theory, pattern recognition, and neural. We consider the problem of finding clusters in graphs which are partially observed.

Simple bounds for noisy linear inverse problems with exact. See the complete profile on linkedin and discover samets. Theory and application to carm conebeam tomography. This dimension reduction procedure succeeds when it preserves certain geometric features of the set. Dimension reduction is the process of embedding highdimensional data into a lower dimensional space to facilitate its analysis.

Samet oymak, mahdi soltanolkotabi, and benjamin recht. In the euclidean setting, one fundamental technique for dimension reduction is to apply a random linear map to the data. Information theory proceedings isit, 20 ieee international symposium on. Econometrics seminars spring 2019 ucr department of. The mathematics genealogy project is in need of funds to help pay for student help and other associated costs.

When available, the papers may be downloaded as pdf files, which can be read or printed using the acrobat reader. In the context of information theory and communications, classical coding theory is often associated with the transmission of a message in a manner which is robust to various types of corruption. If you would like to contribute, please donate online using credit card or bank transfer or mail your taxdeductible contribution to. Proceedings of the thirtysecond conference on learning theory held in. Computer science information theory, mathematics optimization and control. Matrix rank minimization rm problems recently gained extensive attention due to numerous applications in machine learning, system identification and graphical models. By kishore jaganathan, samet oymak and babak hassibi. A proximalgradient homotopy method for the sparse least. Sharp timedata tradeoffs for linear inverse problems. In matrix recovery, one takes n information theory.

Ramya korlakai vinayak, samet oymak, babak hassibi. Gaussian comparison theorems are useful tools in probability theory. Combinatorial regression and improved basis pursuit for. Computer science information theory, computer science learning. This paper investigates the approximation power of three types of random neural networks. Kishore jaganathan, samet oymak, and babak hassibi. I am also thankful to the members of my candidacy and defense talk committee, professors joel tropp, yaser abu mostafa, p. Pdf information theory and its relation to machine learning. Pdf in this position paper, i first describe a new perspective on machine learning ml by four basic problems or levels, namely, what to. Simple bounds for noisy linear inverse problems with exact side. This model shows that the comparative advantage is actually influenced by the interaction between the resources countries have relative abundance of. Murat kocaoglu karthikeyan shanmugam alexandros dimakis adam klivans. In this paper we characterize sharp timedata tradeoffs for optimization problems used for solving linear inverse problems. Eldar, and babak hassibi simultaneously structured models with application to sparse and lowrank matrices, arxiv.