Recently Added

Generative Modeling by Estimating Gradients of the Data Distribution

Stefano Ermon
Stanford University
May 12, 2020
Existing generative models are typically based on explicit representations of probability distributions (e.g., autoregressive or VAEs) or implicit sampling procedures (e.g., GANs). We propose an alternative approach based on modeling directly the vector field of gradients of the data distribution (scores). Our framework allows flexible energy-based model architectures, requires no sampling during training or the use of adversarial training methods.

Quantitative decompositions of Lipschitz mappings

Guy C. David
Ball State University
May 12, 2020
Given a Lipschitz map, it is often useful to chop the domain into pieces on which the map has simple behavior. For example, depending on the dimensions of source and target, one may ask for pieces on which the map behaves like a bi-Lipschitz embedding or like a linear projection. For many issues, it is even more useful if this decomposition is quantitative, i.e., with bounds independent of the particular map or spaces involved.

Convex Set Disjointness, Distributed Learning of Halfspaces, and Linear Programming

Shay Moran
Member, School of Mathematics
May 12, 2020
Distributed learning protocols are designed to train on distributed data without gathering it all on a single centralized machine, thus contributing to the efficiency of the system and enhancing its privacy. We study a central problem in distributed learning, called Distributed Learning of Halfspaces: let U \subset R^d be a known domain of size n and let h:R^d —> R be an unknown target affine function. A set of examples {(u,b)} is distributed between several parties, where u \in U is a point and b = sign(h(u)) \in {-1, +1} is its label.

Using discrepancy theory to improve the design of randomized controlled trials

Daniel Spielman
Yale University
May 11, 2020
In randomized experiments, such as a medical trials, we randomly assign the treatment, such as a drug or a placebo, that each experimental subject receives. Randomization can help us accurately estimate the difference in treatment effects with high probability. We also know that we want the two groups to be similar: ideally the two groups would be similar in every statistic we can measure beforehand. Recent advances in algorithmic discrepancy theory allow us to divide subjects into groups with similar statistics.

On Math, Shadows, and Digital Sundials

Silvia Ghinassi
School of Mathematics
May 8, 2020
Geometric measure theory is a branch of mathematics that, loosely said, focuses on studying geometric properties of objects, such as size and shape. In this talk, Silvia Ghinassi will discuss: How do we measure size? How do we know when an object is one-dimensional, or two-dimensional? What if it is neither? How can we describe shape? What does a cylinder look like if I look at it from below? And from the side?

Spectral characterizations of Besse and Zoll Reeb flows

Marco Mazzucchelli
École normale supérieure de Lyon
May 8, 2020
In this talk, I will address a geometric inverse problem from contact geometry: is it possible to recognize whether all orbits of a given Reeb flow are closed from the knowledge of the action spectrum? Borrowing the terminology from Riemannian geometry, Reeb flows all of whose orbits are closed are sometimes called Besse, and Besse Reeb flows all of whose orbits have the same minimal period are sometimes called Zoll.

On triple product L functions

Jayce Robert Getz
Duke University
May 7, 2020
Establishing the conjectured analytic properties of triple product L-functions is a crucial case of Langlands functoriality. However, little is known. I will present work in progress on the case of triples of automorphic representations on GL_3; in some sense this is the smallest case that appears out of reach via standard techniques. The approach is based on the beautiful fibration method of Braverman and Kazhdan for constructing Schwartz spaces and proving analogues of the Poisson summation formula.

Learning probability distributions; What can, What can't be done

Shai Ben-David
University of Waterloo
May 7, 2020
A possible high level description of statistical learning is that it aims to learn about some unknown probability distribution ("environment”) from samples it generates ("training data”). In its most general form, assuming no prior knowledge and asking to find accurate approximations to the data generating distributions, there can be no success guarantee. In this talk I will discuss two major directions of relaxing that too hard problem.

Boosting Simple Learners

Shay Moran
May 5, 2020
We study boosting algorithms under the assumption that the given weak learner outputs hypotheses from a class of bounded capacity. This assumption is inspired by the common convention that weak hypotheses are “rules-of-thumbs” from an “easy-to-learn class”. Formally, we assume the class of weak hypotheses has a bounded VC dimension.

We focus on two main questions:

Cutting Planes Proofs of Tseitin and Random Formulas

Noah Fleming
University of Toronto
May 5, 2020
Proof Complexity studies the length of proofs of propositional tautologies in various restricted proof systems. One of the most well-studied is the Cutting Planes proof system, which captures reasoning which can be expressed using linear inequalities. A series of papers proved lower bounds on the length of Cutting Planes using the method of feasible interpolation whereby proving lower bounds on the size of Cutting Planes lower bounds proofs of a certain restricted class of formulas is reduced to monotone circuit lower bounds.