Seminars and Colloquia by Series

Thursday, October 10, 2013 - 15:05 , Location: Skyles 005 , Andrew Nobel , University of North Carolina, Chapel Hill , Organizer: Karim Lounici
The problem of finding large average submatrices of a real-valued matrix arises in the exploratory analysis of data from disciplines as diverse as genomics and social sciences. Motivated in part by previous work on this applied problem, this talk will present several new theoretical results concerning large average submatrices of an n x n Gaussian random matrix. We will begin by considering the average and joint distribution of the k x k submatrix having largest average value (the global maximum). We then turn our attention to submatrices with dominant row and column sums, which arise as the local maxima of a practical iterative search procedure for large average submatrices I will present a result characterizing the value and joint distribution of a local maximum, and show that a typical local maxima has an average value within a constant factor of the global maximum. In the last part of the talk I will describe several results concerning the *number* L_n(k) of k x k local maxima, including the asymptotic behavior of its mean and variance for fixed k and increasing n, and a central limit theorem for L_n(k) that is based on Stein's method for normal approximation. Joint work with Shankar Bhamidi (UNC) and Partha S. Dey (UIUC)
Thursday, October 3, 2013 - 15:05 , Location: Skiles 005 , Henry Matzinger , GaTech , Organizer: Ionel Popescu
We consider optimal alignments of random sequences of length n which are i.i.d. For such alignments we count which letters get aligned with which letters how often. This gives as for every opitmal alignment the frequency of the aligned letter pairs. These frequencies expressed as relative frequencies and put in vector form are called the "empirical distribution of letter pairs along an optimal alignment". It was previously established that if the scoring function is chosen at random, then the empirical distribution of letter pairs along an opitmal alignment converges. We show an upper bound for the rate of convergence which is larger thatn the rate of the alignement score. the rate of the alignemnt score can be obtained directly by Azuma-Hoeffding, but not so for the empirical distribution of the aligned letter pairs seen along an opitmal alignment: which changing on letter in one of the sequences, the optimal alginemnt score changes by at most a fixed quantity, but the empirical distribution of the aligned letter pairs potentially could change entirely.
Thursday, September 26, 2013 - 15:05 , Location: Skiles 005 , Nina Balcan , Georgia Tech College of Computing , Organizer: Ionel Popescu
We analyze active learning algorithms, which only receive the classifications of examples when they ask for them, and traditional passive (PAC) learning algorithms, which receive classifications for all training examples, under log-concave and nearly log-concave distributions. By using an aggressive localization argument, we prove that active learning provides an exponential improvement over passive learning when learning homogeneous linear separators in these settings.  Building on this, we then provide a computationally efficient algorithm with optimal sample complexity for passive learning in such settings. This provides the first bound for a polynomial-time algorithm that is tight for  an interesting infinite class of hypothesis functions under a general class of data-distributions, and also characterizes the distribution-specific sample complexity for each distribution in the class. We also illustrate the power of localization for efficiently learning linear separators in two challenging noise models (malicious noise and agnostic setting) where we provide efficient algorithms with significantly better noise tolerance than previously known.
Thursday, September 19, 2013 - 15:05 , Location: Skiles 005 , Brendan Farrell , Caltech , Organizer: Ionel Popescu
We consider two approaches to address angles between random subspaces: classical random matrix theory and free probability. In the former, one constructs random subspaces from vectors with independent random entries. In the latter, one has historically started with the uniform distribution on subspaces of appropriate dimension. We point out when these two approaches coincide and present new results for both. In particular, we present the first universality result for the random matrix theory approach and present the first result beyond uniform distribution for the free probability approach. We further show that, unexpectedly, discrete uncertainty principles play a natural role in this setting. This work is partially with L. Erdos and G. Anderson.
Thursday, September 12, 2013 - 15:05 , Location: Skiles 005 , Yuri Bakhtin , GaTech , Organizer: Ionel Popescu
The classical Freidlin--Wentzell theory on small random perturbations of dynamical systems operates mainly at the level of large deviation estimates. In many cases it would be interesting and useful to supplement those with central limit theorem type results. We are able to describe a class of situations where a Gaussian scaling limit for the exit point of conditioned diffusions holds. Our main tools are Doob's h-transform and new gradient estimates for Hamilton--Jacobi equations. Joint work with Andrzej Swiech.
Thursday, September 5, 2013 - 15:05 , Location: 006 , Ionel Popescu , GaTech , Organizer: Ionel Popescu
We show that on any Riemannian manifold with the Ricci curvature non-negative we can construct a coupling of two Brownian motions which are staying fixed distance for all times.  We show a more general version of this for the case of Ricci bounded from below uniformly by a constant k.   In the terminology of Burdzy, Kendall and others, a shy coupling is a coupling in which the Brownian motions do not couple in finite time with positive probability.   What we construct here is a strong version of shy couplings on Riemannian manifolds.   On the other hand, this can be put in contrast with some results of von Renesse and K. T. Sturm which give a characterization of the lower bound on the Ricci curvature in terms of couplings of Brownian motions and our construction optimizes this choice in a way which will be explained.  This is joint work with Mihai N. Pascu.      
Thursday, April 25, 2013 - 15:05 , Location: Skiles 006 , Sergio Almada , UNC Chapel Hill , Organizer:
The Kardar-Parisi-Zhang(KPZ) equation is a non-linear stochastic partial di fferential equation proposed as the scaling limit for random growth models in physics. This equation is, in standard terms, ill posed and the notion of solution has attracted considerable attention in recent years. The purpose of this talk is two fold; on one side, an introduction to the KPZ equation and the so called KPZ universality classes is given. On the other side, we give recent results that generalize the notion of viscosity solutions from deterministic PDE to the stochastic case and apply these results to the KPZ equation. The main technical tool for this program to go through is a non-linear version of Feyman-Kac's formula that uses Doubly Backward Stochastic Differential Equations (Stochastic Differential Equations with times flowing backwards and forwards at the same time) as a basis for the representation.
Thursday, April 18, 2013 - 15:05 , Location: Skyles 006 , Paul Bourgade , Harvard University , Organizer: Karim Lounici
Wigner stated the general hypothesis that the distribution of eigenvalue spacings of large complicated quantum systems is universal in the sense that it depends only on the symmetry class of the physical system but not on other detailed structures. The simplest case for this hypothesis concerns large but finite dimensional matrices. Spectacular progress was done in the past two decades to prove universality of random matrices presenting an orthogonal, unitary or symplectic invariance. These models correspond to log-gases with respective inverse temperature 1, 2 or 4. I will report on a joint work with L. Erdos and H.-T. Yau, which yields universality for log-gases at arbitrary temperature at the microscopic scale. A main step consists in the optimal localization of the particles, and the involved techniques include a multiscale analysis and a local logarithmic Sobolev inequality.
Thursday, April 11, 2013 - 15:05 , Location: Skyles 006 , Adrien Saumard , University of Washington , Organizer: Karim Lounici

[1] S. Arlot and P. Massart. Data-driven calibration of penalties for least-squares regression. J. Mach. Learn.
Res., 10:245.279 (electronic), 2009.
[2] L. Birgé and P. Massart. Minimal penalties for Gaussian model selection. Probab. Theory Related Fields,
138(1-2):33.73, 2007.
[3] Vladimir Koltchinskii. Oracle inequalities in empirical risk minimization and sparse recovery problems,
volume 2033 of Lecture Notes in Mathematics. Springer, Heidelberg, 2011. Lectures from the 38th Prob-
ability Summer School held in Saint-Flour, 2008, École d.Été de Probabilités de Saint-Flour. [Saint-Flour
Probability Summer School].
[4] Pascal Massart. Concentration inequalities and model selection, volume 1896 of Lecture Notes in Math-
ematics. Springer, Berlin, 2007. Lectures from the 33rd Summer School on Probability Theory held in
Saint-Flour, July 6.23, 2003, With a foreword by Jean Picard. 

  The systematical study of model selection procedures, especially since the early nineties, has led to the design of penalties that often allow to achieve minimax rates of convergence and adaptivity for the selected model, in the general setting of risk minimization (Koltchinskii [3], Massart [4]). However, the proposed penalties often form their dependencies on unknown or unrealistic constants. As a matter of fact, under-penalization has generally disastrous e.ects in terms of e¢ ciency. Indeed, the model selection procedure then looses any bias-variance trade-o. and so, tends to select one of the biggest models in the collection. Birgé and Massart ([2]) proposed quite recently a method that empirically adjusts the level of penalization in a linear Gaussian setting. This method of calibration is called "slope heuristics" by the authors, and is proved to be optimal in their setting. It is based on the existence of a minimal penalty, which is shown to be half the optimal one. Arlot and Massart ([1]) have then extended the slope heuristics to the more general framework of empirical risk minimization. They succeeded in proving the optimality of the method in heteroscedastic least-squares regression, a case where the ideal penalty is no longer linear in the dimension of the models, not even a function of it. However, they restricted their analysis to histograms for technical reasons. They conjectured a wide range of applicability for the method. We will present some results that prove the validity of the slope heuristics in heteroscedastic least-squares regression for more general linear models than histograms. The models considered here are equipped with a localized orthonormal basis, among other things. We show that some piecewise polynomials and Haar expansions satisfy the prescribed conditions. We will insist on the analysis when the model is .xed. In particular, we will focus on deviations bounds for the true and empirical excess risks of the estimator. Empirical process theory and concentration inequalities are central tools here, and the results at a .xed model may be of independent interest. 
Thursday, April 4, 2013 - 15:05 , Location: Skiles 006 , Thomas Mikosch , University of Copenhagen , Organizer: Liang Peng
In recent work, an idea of Adam Jakubowski was used to prove infinite stable limit theory and precise large deviation results for sums of strictly stationary regularly varying sequences. The idea of Jakubowski consists of approximating tail probabilities of distributions for such sums with increasing index by the corresponding quantities for sums with fixed index. This idea can also be made to work for Laplace functionals of point processes, the distribution function of maxima and the characteristic functions of partial sums of stationary sequences. In each of these situations, extremal dependence manifests in the appearance of suitable cluster indices (extremal index for maxima, cluster index for sums,...). The proposed method can be easily understood and has the potential to function as heuristics for proving limit results for weakly dependent heavy-tailed sequences.