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Mathematics of Information
Seminar    (CS286)

4:00-5:00 pm in Annenberg 314
(except where otherwise noted)

October 2, 2018
Ben Lee Volk
Algebraic Complexity Theory: Lower bounds and Derandomization (1st of 2 parts)
    Algebraic Complexity theory is a subfield of complexity theory, studying computational problems of algebraic flavor such as multiplying matrices, computing the determinant, and more generally, computing polynomials over a field, where the complexity is measured by the number of algebraic operations needed to compute it.
    Two basic open problems in the area are (1) proving superpolynomial circuit lower bounds for explicit polynomials (which is the algebraic analog to the P vs. NP problem), and (2) designing deterministic efficient algorithms for the Polynomial Identity Testing problem, that asks to determine, given a circuit computing a polynomial, whether it computes the zero polynomial. Studying these questions often leads to beautiful mathematical questions of combinatorial, algebraic and geometric nature.
    In this talk, we will discuss several old and new results in the area, explore the mysterious, bidirectional and intricate connections between the two problems above, and give some open problems.

October 9, 2018
Ben Lee Volk
Algebraic Complexity Theory: Lower bounds and Derandomization (2nd of 2 parts)

October 16, 2018
Alessandro Zocca
Hard-core interactions on graphs
    In this talk I will introduce the hard-core model with Metropolis transition probabilities on finite graphs and its multi-state generalization. Motivated by the study of random-access networks performance, I will focus on the low-temperature regime and study the asymptotic behavior of these interacting particle systems by looking at first hitting times between stable states and mixing times. In particular, I will show how the order-of-magnitude of these hitting and mixing times depends on the underlying graph structure and derive precise asymptotics for various regular lattices. These results have been obtained extending the so-called "pathwise approach" developed in the statistical physics literature to study metastability phenomena and yielded a rigorous understanding of the root cause for poor delay performance of random-access networks.

October 23, 2018
Alessandro Achille (UCLA)
Complexity, Noise, and Emergent Properties of Learning Deep Representations
    I will show that Information Theoretic quantities control and describe a large part of the training process of Deep Neural Networks, and can be used to explain how properties, such as invariance to nuisance variability and disentanglement of semantic factors, emerge naturally in the learned representation. The resulting theory has connections with several fields ranging from algorithmic complexity to variational inference. This framework not only predicts the asymptotic behavior of deep networks, but also shows that the initial learning transient has a large irreversible effect on the outcome of the training, which gives rise to critical learning periods akin to those observed in biological systems. This urges us to study the complex, and so far neglected, initial phase of learning.

October 30, 2018
Omer Tamuz
Combining and Comparing Experiments
    A classical statistical model of hypothesis testing is the Blackwell Le Cam experiment, in which an observer makes inferences regarding the veracity of an hypothesis by observing a random outcome whose distribution depends on whether or not the hypothesis holds. We address a number of natural, classical questions: when does one experiment have more information than another? How can we quantify the amount of information an experiment holds? Our results include an answer to an old question of Blackwell, as well as a novel axiomatization of Kullback-Leibler divergence.
    Joint with Xiaosheng Mu, Luciano Pomatto and Philipp Strack

November 27, 2018
Rasmus Kyng (Harvard)
Approximate Gaussian Elimination for Laplacians
    We show how to perform sparse approximate Gaussian elimination for Laplacian matrices. We present a simple, nearly linear time algorithm that approximates a Laplacian matrix by a sparse LU factorization. We compute this factorization by subsampling standard Gaussian elimination. This gives the simplest known nearly linear time solver for Laplacian equations. The crux of our proof is the use of matrix martingales to analyze the algorithm. Finally, we will take a look at ongoing efforts to implement robust and fast Laplacian solvers based on these ideas.
    Based on joint work with Sushant Sachdeva and Daniel Spielman.

December 4, 2018
Jan Hazla (MIT)
Reasoning in Bayesian Opinion Exchange Networks is Computationally Hard
    Bayesian models of opinion exchange are extensively studied in economics, dating back to the work of Aumann on the agreement theorem. An important class of such models features agents arranged on a network (representing, e.g., social interactions), with the network structure determining which agents communicate with each other. It is often argued that the Bayesian computations needed by agents in such models are difficult, but prior to our work there were no rigorous arguments for such hardness.
    We consider a well-studied model where fully rational agents receive private signals indicative of an unknown state of the world. Then, they repeatedly announce the state of the world they consider most likely to their neighbors, at the same time updating their beliefs based on their neighbors' announcements.
    I will discuss our complexity-theoretic results establishing hardness of agents' computations in this model. Specifically, we show that these computations are NP-hard and extend this result to PSPACE-hardness. We show hardness not only for exact computations, but also that it is computationally difficult even to approximate the rational opinion in any meaningful way.
    Joint work with Ali Jadbababie, Elchanan Mossel and Amin Rahimian.

January 22, 2019
Gautam Kamath
Privately Learning High-Dimensional Distributions
    We present novel, computationally efficient, and differentially private algorithms for two fundamental high-dimensional learning problems: learning a multivariate Gaussian in R^d and learning a product distribution in {0,1}^d in total variation distance. The sample complexity of our algorithms nearly matches the sample complexity of the optimal non-private learners for these tasks in a wide range of parameters. Thus, our results show that private comes essentially for free for these problems, providing a counterpoint to the many negative results showing that privacy is often costly in high dimensions. Our algorithms introduce a novel technical approach to reducing the sensitivity of the estimation procedure that we call recursive private preconditioning, which may find additional applications.
    Based on joint work with Jerry Li, Vikrant Singhal, and Jonathan Ullman.

February 19, 2019
Vijay Vazirani (UCI)
Planar Graph Perfect Matching is in NC
    Is matching in NC, i.e., is there a deterministic fast parallel algorithm for it? This has been an outstanding open question in TCS for over three decades, ever since the discovery of Random NC matching algorithms. Within this question, the case of planar graphs has remained an enigma: On the one hand, counting the number of perfect matchings is far harder than finding one (the former is #P-complete and the latter is in P), and on the other, for planar graphs, counting has long been known to be in NC whereas finding one has resisted a solution!
    The case of bipartite planar graphs was solved by Miller and Naor in 1989 via a flow-based algorithm. In 2000, Mahajan and Varadarajan gave an elegant way of using counting matchings to finding one, hence giving a different NC algorithm.
    However, non-bipartite planar graphs still didn't yield: the stumbling block being odd tight cuts. Interestingly enough, these are also a key to the solution: a balanced tight odd cut leads to a straight-forward divide and conquer NC algorithm. However, a number of ideas are needed to find such a cut in NC; the central one being an NC algorithm for finding a face of the perfect matching polytope at which $\Omega(n)$ new conditions, involving constraints of the polytope, are simultaneously satisfied.
    Paper available at: https://arxiv.org/pdf/1709.07822.pdf
    Joint work with Nima Anari.

March 12, 2019
Nisheeth Vishnoi (Yale)
topic TBA

CMI Faculty Lunches

by invitation

November 8, 2018
Venkat Chandrasekaran
Newton Polytopes and Relative Entropy Optimization
    We discuss how relative entropy is uniquely suited to optimization problems involving sparse polynomials. Our results connect to the work of Descartes (the rule of signs) and Khovanskii (the theory of fewnomials). The Newton polytope associated to a polynomial plays a central role in our development. This is joint work with Riley Murray and Adam Wierman.

December 13, 2018
Omer Tamuz
Equitable Voting Rules
    Joint with Laurent Bartholdi, Wade Hann-Caruthers, Maya Josyula and Leeat Yariv.
    Given n voters and two candidates A and B, a voting rule is a map from {A,B,abstain}^n to {A,B,tie}. An important example is majority, which has a desirable symmetry property: permuting the voters leaves the result unchanged. We study a weakening of this symmetry assumption, which allows for a far richer set of rules that still treat voters equally. We show that these rules can have very small---but not too small---winning coalitions (i.e., a set of voters that can control the election outcome).
    I will discuss the relation to various known results and open problems in the theory of finite groups. No previous knowledge required.

January 10, 2019


February 14, 2019


March 14, 2019
Anima Anandkumar


April 11, 2019
Peter Schröder


May 9, 2019
Adam Wierman


June 13, 2019
Babak Hassibi