DOCSLIB.ORG

C⃝copyright 2016 Amin Jalali

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
C⃝copyright 2016 Amin Jalali ⃝c Copyright 2016 Amin Jalali Convex Optimization Algorithms and Statistical Bounds for Learning Structured Models Amin Jalali A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2016 Reading Committee: Professor Maryam Fazel, Chair Professor Jeffrey A. Bilmes Professor Ioana Dumitriu Program Authorized to Offer Degree: Electrical Engineering University of Washington Abstract Convex Optimization Algorithms and Statistical Bounds for Learning Structured Models Amin Jalali Chair of the Supervisory Committee: Professor Maryam Fazel Electrical Engineering Design and analysis of tractable methods for estimation of structured models from massive high-dimensional datasets has been a topic of research in statistics, machine learning and engineering for many years. Regularization, the act of simultaneously optimizing a data fidelity term and a structure-promoting term, is a widely used approach in different machine learning and signal processing tasks. Appropriate regularizers, with efficient optimization techniques, can help in exploiting the prior structural information on the underlying model. This dissertation is focused on exploring new structures, devising efficient convex relaxations for exploiting them, and studying the statistical performance of such estimators. We address three problems under this framework on which we elaborate below. In many applications, we aim to reconstruct models that are known to have more than one structure at the same time. Having a rich literature on exploiting common structures like sparsity and low rank at hand, one could pose similar questions about simultaneously struc- tured models with several low-dimensional structures. Using the respective known convex penalties for the involved structures, we show that multi-objective optimization with these penalties can do no better, order-wise, than exploiting only one of the present structures. This suggests that to fully exploit the multiple structures, we need an entirely new convex relaxation, not one that combines the convex relaxations for each structure. This work, while applicable for general structures, yields interesting results for the case of sparse and low-rank matrices which arise in applications such as sparse phase retrieval and quadratic compressed sensing. We then turn our attention to the design and efficient optimization of convex penalties for structured learning. We introduce a general class of semidefinite representable penalties, called variational Gram functions (VGF), and provide a list of optimization tools for solving regularized estimation problems involving VGFs. Exploiting the variational structure in VGFs, as well as the variational structure in many common loss functions, enables us to devise efficient optimization techniques as well as to provide guarantees on the solutions of many regularized loss minimization problems. Finally, we explore the statistical and computational trade-offs in the community detec- tion problem. We study recovery regimes and algorithms for community detection in sparse graphs generated under a heterogeneous stochastic block model in its most general form. In this quest, we were able to expand the applicability of semidefinite programs (in exact com- munity detection) to some new and important network configurations, which provides us with a better understanding of the ability of semidefinite programs in reaching statistical identifiability limits. TABLE OF CONTENTS Page List of Figures ....................................... iii List of Tables ........................................ v Chapter 1: Introduction ................................ 1 1.1 Recovery of Simultaneously Structured Models ................. 1 1.2 Variational Regularization in Structured Learning ............... 4 1.3 Community Detection .............................. 7 Chapter 2: Recovery of Simultaneously Structured Models ............. 11 2.1 Introduction .................................... 11 2.2 Problem Setup .................................. 20 2.3 Main Results ................................... 24 2.4 Measurement Ensembles ............................. 33 2.5 Upper bounds ................................... 39 2.6 General Simultaneously Structured Model Recovery .............. 42 2.7 Numerical Experiments .............................. 51 2.8 Discussion ..................................... 56 Chapter 3: Variational Gram Functions ........................ 57 3.1 Introduction .................................... 57 3.2 Examples and Connections ............................ 60 3.3 Convex Analysis of VGF ............................. 64 3.4 Proximal Operators ................................ 78 3.5 Algorithms for Optimization with VGF ..................... 80 3.6 Numerical Example ................................ 90 3.7 Discussion ..................................... 95 i Chapter 4: Community Detection ........................... 96 4.1 Introduction .................................... 96 4.2 Main Results ................................... 101 4.3 Tradeoffs in Heterogenous SBM ......................... 105 4.4 Discussion ..................................... 111 Chapter 5: Discussions and Future Directions ..................... 113 5.1 Simultaneously Structured Models ........................ 113 5.2 Variational Gram Functions ........................... 115 5.3 Community Detection in Heterogenous Stochastic Block Model ........ 119 Bibliography ........................................ 121 Appendix A: Supplement to Chapter 2 ......................... 136 A.1 Proofs for Section 2.3.2 .............................. 136 A.2 Properties of Cones ................................ 140 A.3 Norms in Sparse and Low-rank Model ...................... 143 A.4 Results on Non-convex Recovery ......................... 145 Appendix B: Supplement to Chapter 3 ......................... 148 B.1 Proofs ....................................... 148 Appendix C: Supplement to Chapter 4 ......................... 151 C.1 Proofs for Convex Recovery ........................... 151 C.2 Proofs for Recoverability and Non-recoverability ................ 160 C.3 Detailed Computations for Examples in Section 4.3 .............. 170 C.4 Recovery by a Simple Counting Algorithm ................... 174 ii LIST OF FIGURES Figure Number Page 1.1 Composite penalties. s, often a smooth map, transforms the original struc- tured model x to one with a different structure. p, often a non-smooth real- valued map, penalizes s(x) according to the latter structure, hence providing a penalization scheme for the original structure. ................ 6 2.1 Depiction of the correlation between a vector x and a set S . s? achieves the largest angle with x , hence s? has the minimum correlation with x; i.e., ρ(x; S) = jx¯T s¯?j . ................................. 21 kpk2 2.2 For a scaled norm ball passing through x0, κ = , where p is any of the kx0k2 closest points on the scaled norm ball to the origin. .............. 22 2.3 Consider a point x0 represented in the figure by the dot. We need at least m measurements for x0 to be recoverable since for any ml < m this point is not on the Pareto optimal front. .......................... 24 2.4 An example of a decomposable norm: `1 norm is decomposable at x0 = (1; 0). The sign vector e, the support T , and shifted subspace T ? are illustrated. A ? subgradient g at x0 and its projection onto T are also shown. ....... 50 k k 2.5 Performance of the recovery program minimizing maxf tr(X) ; X 1;2 g with a tr(X0) kX0k1;2 PSD constraint. The dark region corresponds to the experimental region of failure due to insufficient measurements. As predicted by Theorem 10, the number of required measurements increases linearly with rd. ......... 52 k k 2.6 Performance of the recovery program minimizing maxf tr(X) ; X 1 g with a tr(X0) kX0k1 PSD constraint. r = 1; k = 8 and d is allowed to vary. The plot shows m versus d to illustrate the lower bound Ω(minfk2; drg) predicted by Theorem 10. ........................................ 53 2.7 Performance of the recovery program minimizing tr(X) + λkXk1 with a PSD constraint, for λ = 0:2 (left) and λ = 0:35 (right). ............... 54 2.8 90% frequency of failure where the threshold of recovery is 10−4 for the green k k (upper) and 0:05 for the red (lower) curve. maxf tr(X) ; X 1 g is minimized tr(X0) kX0k1 subject to the PSD constraint and the measurements. ............. 54 iii 2.9 We compare sample complexities of different approaches for a rank 1, 40 × 40 matrix as function of sparsity. The sample complexities were estimated by a search over m, where we chose the m with success rate closest to 50% (over 100 iterations). .................................. 55 3.1 The set in (3.3) (defined by M = [1; 0:8; 0:8; 1] ) and the cone of positive semidefinite matrices where 2 × 2 symmetric matrices are embedded into R3 . The thick edge of the cube is the set of all points with the same diagonal elements as M (see (3.40)), and the two endpoints constitute Meff . Positive semidefiniteness of Mf is a necessary and sufficient condition for the convexity n×2 of ΩM : R ! R for all n ≥ m − 1 = 1 . ................... 68 3.2 Mirror-Prox algorithm with adaptive line search. Here cdec > 1 and cinc > 1 are parameters controlling the decrease and increase of the step size γt in the line search trials. The stopping criterion for the line search is δt ≤ 0 where h − i − δt = γt F (wt); wt zt+1 Vzt (zt+1). ...................... 86 3.3 (a) An example of hierarchical classification
Load more

Recommended publications
  • Download PDF Call for Papers
    2020 IEEE International Symposium on Information Theory Los Angeles, CA, USA |June 21-26, 2020 The Westin Bonaventure Hotel & Suites, Los Angeles Call for Papers Interested authors are encouraged to submit previously unpublished contributions in any area related to information theory, including but not limited to the following topic areas: Topics ➢ Communication and Storage Coding ➢ Distributed Storage ➢ Network Information Theory ➢ Coding Theory ➢ Emerging Applications of IT ➢ Pattern Recognition and ML ➢ Coded and Distributed Computing ➢ Information Theory and Statistics ➢ Privacy in Information Processing ➢ Combinatorics and Information Theory ➢ Information Theory in Biology ➢ Quantum Information Theory ➢ Communication Theory ➢ Information Theory in CS ➢ Shannon Theory ➢ Compressed Sensing and Sparsity ➢ Information Theory in Data Science ➢ Signal Processing ➢ Cryptography and Security ➢ Learning Theory ➢ Source Coding and Data Compression ➢ Detection and Estimation ➢ Network Coding and Applications ➢ Wireless Communication ➢ Deep Learning for Networks ➢ Network Data Analysis Researchers working in emerging fields of information theory or on novel applications of information theory are especially encouraged to submit original findings. The submitted work and the published version are limited to 5 pages in the standard IEEE format, plus an optional extra page containing references only. Information about paper formatting and submission policies can be found on the conference web page, noted below. The paper submission deadline is Sunday, January 12, 2020, at 11:59 PM, Eastern Time (New York, USA). Acceptance notifications will be sent out by Friday, March 27, 2020. We look forward to your participation in ISIT 2020! General Chairs: Salman Avestimehr, Giuseppe Caire, and Babak Hassibi TPC Chairs: Young-Han Kim, Frederique Oggier, Greg Wornell, and Wei Yu https://2020.ieee-isit.org .
    [Show full text]
  • Arxiv:2010.10473V2 [Cs.LG] 1 Feb 2021
    Proceedings of Machine Learning Research vol 134:1{22, 2021 Regret-optimal control in dynamic environments Gautam Goel [email protected] California Insititute of Technology Babak Hassibi [email protected] California Insititute of Technology Abstract We consider control in linear time-varying dynamical systems from the perspective of regret minimization. Unlike most prior work in this area, we focus on the problem of designing an online controller which minimizes regret against the best dynamic sequence of control actions selected in hindsight (dynamic regret), instead of the best fixed controller in some specific class of controllers (policy regret). This formulation is attractive when the environ- ment changes over time and no single controller achieves good performance over the entire time horizon. We derive the state-space structure of the regret-optimal controller via a novel reduction to H1 control and present a tight data-dependent bound on its regret in terms of the energy of the disturbance. Our results easily extend to the model-predictive setting where the controller can anticipate future disturbances and to settings where the controller only affects the system dynamics after a fixed delay. We present numerical experiments which show that our regret-optimal controller interpolates between the performance of the H2-optimal and H1-optimal controllers across stochastic and adversarial environments. Keywords: dynamic regret, robust control 1. Introduction The central question in control theory is how to regulate the behavior of an evolving system with state x that is perturbed by a disturbance w by dynamically adjusting a control action u. Traditionally, this question has been studied in two distinct settings: in the H2 setting, we assume that the disturbance w is generated by a stochastic process and seek to select the control u so as to minimize the expected control cost, whereas in the H1 setting we assume the noise is selected adversarially and instead seek to minimize the worst-case control cost.
    [Show full text]
  • Linear Estimation in Krein Spaces-- Part II: Applications
    34 IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL 41, NO. 1, JANUARY 1996 stimation in Krein Spaces- art 11: Applications Babak Hassibi, Ali H. Sayed, Member, IEEE, and Thomas Kailath, Fellow, IEEE Abstract-We show that several interesting problems in Rw - by constructing the corresponding Krein-space "stoc filtering, quadratic game theory, and risk sensitive control and problems for which the Kalman-filter solutions can be written estimation follow as special cases of the Krein-space linear esti- down immediately; moreover, the conditions for a minimum mation theory developed in [l]. We show that a11 these problems can be cast into the problem of calculating the stationary point can also be expressed in terms of quantities easily related to the of certain second-order forms, and that by considering the ap- basic Riccati equations of the Kalman filter. This approach also propriate state space models and error Gramians, we can use the explains the many similarities between, say, the H" solutions ~re~n-spa~eestimation theory to calculate the stationary points and ~e classical LQ solutions and in addition marks out their and study their properties. The approach discussed here allows key differences. for interesting generalizations, such as finite memory adaptive filtering with varying sliding patterns. The paper is organized as follows. In Section I1 we introduce the H" estimation problem, state the conventional solution, and discuss its similarities with and differences from the I. INTRODUCTION Conventional Kalman filter. In Section I11 we reduce the H" LASSICAL results in linear least-squares estimation and estimation problem to guaranteeing the positivity of a certain Kalman filtering are based on an LZ or HZ criterion indefinite quadratic form.
    [Show full text]
  • Stochastic Mirror Descent on Overparameterized Nonlinear Models: Convergence, Implicit Regularization, and Generalization
    Stochastic Mirror Descent on Overparameterized Nonlinear Models: Convergence, Implicit Regularization, and Generalization Navid Azizan1, Sahin Lale2, Babak Hassibi2 1 Department of Computing and Mathematical Sciences 2 Department of Electrical Engineering California Institute of Technology {azizan,alale,hassibi}@caltech.edu Abstract Most modern learning problems are highly overparameterized, meaning that there are many more parameters than the number of training data points, and as a result, the training loss may have infinitely many global minima (parameter vectors that perfectly interpolate the training data). Therefore, it is important to understand which interpolating solutions we converge to, how they depend on the initialization point and the learning algorithm, and whether they lead to different generalization performances. In this paper, we study these questions for the family of stochastic mirror descent (SMD) algorithms, of which the popular stochastic gradient descent (SGD) is a special case. Our contributions are both theoretical and experimental. On the theory side, we show that in the overparameterized nonlinear setting, if the initialization is close enough to the manifold of global minima (something that comes for free in the highly overparameterized case), SMD with sufficiently small step size converges to a global minimum that is approximately the closest one in Bregman divergence. On the experimental side, our extensive experiments on standard datasets and models, using various initializations, various mirror descents,
    [Show full text]
  • 4 AN10 Abstracts
    4 AN10 Abstracts IC1 IC4 On Dispersive Equations and Their Importance in Kinematics and Numerical Algebraic Geometry Mathematics Kinematics underlies applications ranging from the design Dispersive equations, like the Schr¨odinger equation for ex- and control of mechanical devices, especially robots, to the ample, have been used to model several wave phenom- biomechanical modelling of human motion. The major- ena with the distinct property that if no boundary con- ity of kinematic problems can be formulated as systems ditions are imposed then in time the wave spreads out of polynomial equations to be solved and so fall within spatially. In the last fifteen years this field has seen an the domain of algebraic geometry. While symbolic meth- incredible amount of new and sophisticated results proved ods from computer algebra have a role to play, numerical with the aid of mathematics coming from different fields: methods such as polynomial continuation that make strong Fourier analysis, differential and symplectic geometry, an- use of algebraic-geometric properties offer advantages in alytic number theory, and now also probability and a bit of efficiency and parallelizability. Although these methods, dynamical systems. In this talk it is my intention to present collectively called Numerical Algebraic Geometry, are ap- few simple, but still representative examples in which one plicable wherever polynomials arise, e.g., chemistry, biol- can see how these different kinds of mathematics are used ogy, statistics, and economics, this talk will concentrate on in this context. applications in mechanical engineering. A brief review of the main algorithms of the field will indicate their broad Gigliola Staffilani applicability.
    [Show full text]
  • Going Off the Grid (Slides)
    Going off the grid Benjamin Recht University of California, Berkeley Joint work with Badri Bhaskar Parikshit Shah Gonnguo Tang We live in a continuous world... But we work with digital computers... What is the price of living on the grid? imaging astronomy seismology 520 IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS, VOL. 2, NO. 3, SEPTEMBER 2012 spectroscopy Fig. 6. NUS IC sampling timing and waveforms. Left panel: Interface timing between NUS die and ADC. Right panel: Simulated waveforms before and after being sampled by NIN. Horizontal scale is in nanoseconds. spacings vary between 12 and 27 clock cycles, the instanta- neous sampling rate of the NUS receiver varies between 163 and 367 MHz, which is within the range of the 400 MHz ADC. We designed the NUS pattern to repeat every 8192 Nyquist samples, during which time there are 440 pulses which set the k sampling locations. This corresponds to an average sample rate of 236 MHz. We evaluate the quality of our pattern using a third i2⇡fj t criterion: the Fourier transform of favorable patterns will tend to have a flat, noise-like spectrum. Fig. 10 compares two NUS x(t)= cje patterns with different inter-sample spacings. The pattern shown DOA Estimation in the top plots has strong resonances across the Nyquist band. Fig. 7. NUS IC die photo. Die size is mm. In contrast, the pattern shown in the bottom plots, which has j=1 undergone a randomization of its sample locations, has a much X whiter spectrum. The flat spectrum is preferred since then all signals have equal gain.
    [Show full text]
  • Speeding up the Sphere Decoder with H-Infinity and SDP Inspired Lower
    712 IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 56, NO. 2, FEBRUARY 2008 Speeding up the Sphere Decoder With r and SDP Inspired Lower Bounds Mihailo Stojnic, Haris Vikalo, and Babak Hassibi Abstract—It is well known that maximum-likelihood (ML) de- all points such that lies within a sphere of some adequately coding in many digital communication schemes reduces to solving chosen radius centered at , i.e., on finding all such that an integer least-squares problem, which is NP hard in the worst- case. On the other hand, it has recently been shown that, over a wide range of dimensions and signal-to-noise ratios (SNRs), the (2) sphere decoding algorithm can be used to find the exact ML solu- tion with an expected complexity that is often less than Q.How- ever, the computational complexity of sphere decoding becomes and then choosing the one that minimizes the objective function. prohibitive if the SNR is too low and/or if the dimension of the Using the -decomposition , problem is too large. In this paper, we target these two regimes and attempt to find faster algorithms by pruning the search tree be- where is upper triangular, and and yond what is done in the standard sphere decoding algorithm. The are such that is unitary, we search tree is pruned by computing lower bounds on the optimal can reformulate (2) as value of the objective function as the algorithm proceeds to descend down the search tree. We observe a tradeoff between the computa- tional complexity required to compute a lower bound and the size of the pruned tree: the more effort we spend in computing a tight (3) lower bound, the more branches that can be eliminated in the tree.
    [Show full text]
  • STFT Phase Retrieval: Uniqueness Guarantees and Recovery Algorithms
    1 STFT Phase Retrieval: Uniqueness Guarantees and Recovery Algorithms Kishore Jaganathany Yonina C. Eldarz Babak Hassibiy yDepartment of Electrical Engineering, Caltech zDepartment of Electrical Engineering, Technion, Israel Institute of Technology Abstract—The problem of recovering a signal from its Fourier We consider STFT phase retrieval, which is the problem magnitude is of paramount importance in various fields of engi- of reconstructing a signal from its STFT magnitude. In some neering and applied physics. Due to the absence of Fourier phase applications of phase retrieval, it is easy to obtain such information, some form of additional information is required in order to be able to uniquely, efficiently and robustly identify measurements. One example is Frequency Resolved Optical the underlying signal. Inspired by practical methods in optical Gating (FROG), which is a general method for measuring imaging, we consider the problem of signal reconstruction from ultrashort laser pulses [31]. Fourier ptychography [32]–[34], the Short-Time Fourier Transform (STFT) magnitude. We first a technology which has enabled X-ray, optical and electron develop conditions under which the STFT magnitude is an microscopy with increased spatial resolution without the need almost surely unique signal representation. We then consider a semidefinite relaxation-based algorithm (STliFT) and provide for advanced lenses, is another popular example. In applica- recovery guarantees. Numerical simulations complement our tions such as speech processing, it is natural to work with theoretical analysis and provide directions for future work. the STFT instead of the Fourier transform as the spectral Index Terms—Short-Time Fourier Transform (STFT), Phase content of speech changes over time [35].
    [Show full text]
  • Future Directions in Compressed Sensing and the Integration of Sensing and Processing What Can and Should We Know by 2030?
    Future Directions in Compressed Sensing and the Integration of Sensing and Processing What Can and Should We Know by 2030? Robert Calderbank, Duke University Guillermo Sapiro, Duke University Workshop funded by the Basic Research Office, Office of the Assistant Prepared by Brian Hider and Jeremy Zeigler Secretary of Defense for Research & Engineering. This report does not Virginia Tech Applied Research Corporation necessarily reflect the policies or positions of the US Department of Defense Preface OVER THE PAST CENTURY, SCIENCE AND TECHNOLOGY HAS BROUGHT REMARKABLE NEW CAPABILITIES TO ALL SECTORS of the economy; from telecommunications, energy, and electronics to medicine, transportation and defense. Technologies that were fantasy decades ago, such as the internet and mobile devices, now inform the way we live, work, and interact with our environment. Key to this technological progress is the capacity of the global basic research community to create new knowledge and to develop new insights in science, technology, and engineering. Understanding the trajectories of this fundamental research, within the context of global challenges, empowers stakeholders to identify and seize potential opportunities. The Future Directions Workshop series, sponsored by the Basic Research Office of the Office of the Assistant Secretary of Defense for Research and Engineering, seeks to examine emerging research and engineering areas that are most likely to transform future technology capabilities. These workshops gather distinguished academic and industry researchers from the world’s top research institutions to engage in an interactive dialogue about the promises and challenges of these emerging basic research areas and how they could impact future capabilities. Chaired by leaders in the field, these workshops encourage unfettered considerations of the prospects of fundamental science areas from the most talented minds in the research community.
    [Show full text]
  • Technology and Engineering International Journal of Recent
    International Journal of Recent Technology and Engineering ISSN : 2277 - 3878 Website: www.ijrte.org Volume-7 Issue-5S4, FEBRUARY 2019 Published by: Blue Eyes Intelligence Engineering and Sciences Publication d E a n n g y i n g o e l e o r i n n h g c e T t n e c Ijrt e e E R X I N P n f L O I O t T R A o e I V N O l G N r IN n a a n r t i u o o n J a l www.ijrte.org Exploring Innovation Editor-In-Chief Chair Dr. Shiv Kumar Ph.D. (CSE), M.Tech. (IT, Honors), B.Tech. (IT), Senior Member of IEEE Blue Eyes Intelligence Engineering & Sciences Publication, Bhopal (M.P.), India Associated Editor-In-Chief Chair Dr. Dinesh Varshney Professor, School of Physics, Devi Ahilya University, Indore (M.P.), India Associated Editor-In-Chief Members Dr. Hai Shanker Hota Ph.D. (CSE), MCA, MSc (Mathematics) Professor & Head, Department of CS, Bilaspur University, Bilaspur (C.G.), India Dr. Gamal Abd El-Nasser Ahmed Mohamed Said Ph.D(CSE), MS(CSE), BSc(EE) Department of Computer and Information Technology , Port Training Institute, Arab Academy for Science ,Technology and Maritime Transport, Egypt Dr. Mayank Singh PDF (Purs), Ph.D(CSE), ME(Software Engineering), BE(CSE), SMACM, MIEEE, LMCSI, SMIACSIT Department of Electrical, Electronic and Computer Engineering, School of Engineering, Howard College, University of KwaZulu- Natal, Durban, South Africa. Scientific Editors Prof. (Dr.) Hamid Saremi Vice Chancellor of Islamic Azad University of Iran, Quchan Branch, Quchan-Iran Dr.
    [Show full text]
  • IEEE Information Theory Society
    IEEE Information Theory Society The 2021 IEEE International Symposium on Information Theory Melbourne, Victoria, Australia Awards Ceremony July 2021 IEEE Medals & Technical Field Awards 2021 IEEE Medal of Honor The IEEE Medal of Honor, established in 1917, is the highest IEEE award. It is presented when a candidate is identified as having made a particular contribution that forms a clearly exceptional addition to the science and technology of concern to IEEE. Jacob Ziv For fundamental contributions to information theory and data compression technology, and for distinguished research leadership. 2021 IEEE Richard E. Hamming Medal Recognizes exceptional contributions to information sciences, systems, and technology, sponsored by Qualcomm. Raymond W. Yeung For fundamental contributions to information theory and pioneering network coding and its applications. 2021 IEEE Jack S. Kilby Signal Processing Medal Recognizes outstanding achievements in signal processing, sponsored by The Kilby Medal Fund. Emmanuel Candès For groundbreaking contributions to compressed sensing. 2021 IEEE Leon K. Kirchmayer Graduate Teaching Award Recognizes inspirational teaching of graduate students in the IEEE fields of interest, sponsored by the Leon K. Kirchmayer Memorial Fund. Andrea Goldsmith For educating, developing, guiding, and energizing generations of highly successful students and postdoctoral fellows. 2021 Eric E. Sumner Award Recognizes outstanding contributions to communications technology, sponsored by Nokia Bell Labs. En-hui Yang For contributions to the theory and practice of source coding. 1 2021 Claude E. Shannon Award Recognizes consistent and profound contributions to the field of information theory. Alon Orlitsky received B.Sc. degrees in Mathematics and Electrical Engineering from Ben Gurion University in 1980 and 1981, and M.Sc.
    [Show full text]
  • Report on the First Iran Workshop on Communication and Information Theory (IWCIT 2013)
    IEEE Information Theory Society Newsletter Vol. 63, No. 4, December 2013 Editor: Tara Javidi ISSN 1059-2362 Editorial committee: Frank Kschischang, Giuseppe Caire, Meir Feder, Tracey Ho, Joerg Kliewer, Anand Sarwate, Andy Singer, and Sergio Verdú President’s Column Gerhard Kramer The 2013 Information Theory Workshop was nominate outstanding dissertations by held in Sevilla, Spain, in mid-September. Be- Jan. 15, 2014. Instructions can be found at fore the Workshop began, the Board of Gov- www.itsoc.org. ernors held their third and final meeting of the year to discuss and vote on a variety of 3) The ISIT Student Paper Award was issues concerning our Society. I would like renamed the IEEE Jack Keil Wolf ISIT to thank the members of the “core” team for Student Paper Award. I appreciate Paul their support during this eventful year: past Siegel’s efforts in managing the change. and future presidents Giuseppe Caire, Mu- riel Médard, Abbas El Gamal, Michelle Eff- 4) A Shannon stamp initiative was started ros, and Alon Orlitsky, treasurer Aylin Yener, at the suggestion of Sergio Verdú, and secretary Edmund Yeh, conference commit- Michelle Effros kindly agreed to orga- tee chair Elza Erkip, and editors-in-chief nize a web page with electronic signa- Helmut Bölcskei and Yiannis Kontoyiannis. tures. If you have not done so already, please add your support at http:// The December column gives opportunity to list some of the www.itsoc.org/about/shannons-centenary-us- important changes that the Board deemed useful and neces- postal-stamp sary for 2014. These changes were thoroughly discussed by an active and supportive Board, and I appreciate their con- 5) Two new Annual Schools of Information Theory were structive comments, criticisms, and suggestions throughout founded in Australia and East Asia.
    [Show full text]