Theoretical CS Reading Group


The reading group meets once a week usually, Monday 1:30-2:30pm to discuss a particular paper or topic.

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Fall 2017

Tuesday, 5th September 2017 — Theory Seminar - Clayton Thomas, Purdue University
Maximally Recoverable Codes: the Bounded Case

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Modern distributed storage systems employ Maximally Recoverable codes that aim to bal- ance failure recovery capabilities with encoding/decoding efficiency tradeoffs. Recent works of Gopalan et al [SODA 2017] and Kane et al [FOCS 2017] show that the alphabet size of grid-like topologies of practical interest must be large, a feature that hampers decoding efficiency. To bypass such shortcomings, in this work we initiate the study of a weaker version of recoverability, where instead of being able to correct all correctable erasure patterns (as is the case for maximal recoverability), we only require to correct all erasure patterns of bounded size. The study of this notion reduces to a variant of a combinatorial problem studied in the literature, which is interesting in its own right. We study the alphabet size of codes withstanding all erasure patterns of small (constant) size. We believe the questions we propose are relevant to both real storage systems and combinatorial analysis, and merit further study. Joint work with Venkata Gandikota, Elena Grigorescu, Minshen Zhu.


Monday, 11th September 2017 — Theory Seminar - Young-San Lin, Purdue University
On Variants of Network Flow Stability

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We present a variant of the stable flow problem. Instead of the traditional flow problem that obeys Kirchhoff law, for each vertex, the outflow is monotone and piecewise linear to the inflow. In a directed and capacitated network, each vertex has strict preference over their incident edges. A stable flow assignment does not allow a group of vertices to benefit from privately rerouting along a path. In this paper, we first show the existence of flow stability by reducing this variant of stable flow problem to Scarf's Lemma, then introduce a path augmenting algorithm that runs in polynomial time.


Monday, 18th September 2017 — Brett Hemenway, Rafail Ostrovsky, and Mary Wootters
Local Correctability of Expander Codes
Presenter: Samson Zhou


Monday, 25th September 2017 — Swastik Kopparty and Shubhangi Saraf
Local Testing and Decoding of High-Rate Error-Correcting Codes
Presenter: GV


Monday, 2nd October 2017 — Theory Seminar - Alexander Block, Purdue University
A Sublinear Upper Bound on Our Combinatorial Problem


Wednesday, 11th October 2017TCS+ Talk - Moses Charikar, Stanford
Hashing-based-Estimators for Kernel Density in High Dimensions


Monday, 16th October 2017 — Luna Frank-Fischer, Venkatesan Guruswami, and Mary Wootters
Locality via Partially Lifted Codes
Presenter: Minshen Zhu


Monday, 23rd October 2017 — Theory Seminar - Evgenia-Maria Kontopoulou, Purdue University
Towards Randomized Algorithms for the Estimation of Log-Determinants and Von-Neumann Entropies

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In the first part, we introduce a novel randomized algorithm for approximating the log-determinant of a Symmetric Positive Definite (SPD) matrix. The algorithm approximates the trace of a small number of matrix powers of a specially constructed matrix, using a method from [1]. We will present theoretical and empirical results for our algorithm when the eigenvalues of the matrix lie in the interval (theta, 1), with 0 < theta < 1. In the second part we will briefly introduce a randomized algorithm to approximate the Von- Neumann entropy of Density matrices that is constructed along similar lines with the one for approximating the log-determinant of SPD matrices. We will present theoretical and preliminary experimental results. [1] H. Avron, S. Toledo (2011), "Randomized algorithms for estimating the trace of an implicit symmetric positive semi-definite matrix," J. ACM 58(2)8 [2] C. Boutsidis, P. Drineas, P. Kambadur, E. Kontopoulou, A. Zouzias (2017) "A randomized algorithm for approximating the log determinant of a symmetric positive definite matrix," Elsevier Journal of Linear Algebra and its Applications 533:95-117


Wednesday, 25th October 2017TCS+ Talk - Seth Pettie, Michigan
A Time Hierarchy Theorem for the LOCAL Model


Monday, 6th November 2017 — Theory Seminar - Abhiram Natarajan, Purdue University
Communication-efficient distributed learning of discrete probability distributions
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Abstract: We initiate a systematic investigation of distribution learning (density estimation) when the data is distributed across multiple servers. The servers must communicate with a referee and the goal is to estimate the underlying distribution with as few bits of communication as possible. We focus on non-parametric density estimation of discrete distributions with respect to the l1 and l2 norms. We provide the first non-trivial upper and lower bounds on the communication complexity of this basic estimation task in various settings of interest. Our results provide insights on the interplay between structure and communication efficiency for a range of fundamental distribution estimation tasks. Joint work with Ilias Diakonikolas, Elena Grigorescu, Jerry Li, Krzysztof Onak, Ludwig Schmidt


Tuesday, 14th November 2017 — Theory Seminar - Peng Zhang, Georgia Tech
Hardness Results for Structured Linear Systems
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Spielman and Teng (2004) showed that linear systems in Laplacian matrices can be solved in nearly-linear time. Since then, a major research goal has been to develop fast solvers for linear systems in other classes of matrices, e.g., connection Laplacians (Kyng et al., 2016), directed Laplacians (Cohen et al., 2017), etc. Connection Laplacians are a special case of PSD-Graph-Structured Block Matrices (Spielman, 2016), block matrices whose non-zero structure corresponds to a graph, and which additionally can be expressed as a sum of positive semi-definite matrices each corresponding to an edge in the graph. A major open question is whether fast solvers can be obtained for all PGSBMs (Spielman, 2016). Other important families of matrices in the PGSBM class include truss stiffness matrices, multi-commodity Laplacians, and total variation matrices, etc. In this talk, we show that if we can solve linear systems in just slightly larger families in PGSBMs, e.g., truss stiffness matrices, multi-commodity Laplacians, total variation matrices, then we can quickly solve all systems of linear equations over the reals. This result can be viewed either positively or negatively: either we will develop nearly-linear time algorithms for solving all real systems of linear equations, or progress on the families we can solve in nearly linear time will soon halt. Joint work with Rasmus Kyng.