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Statistics Seminar

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**Seminars will be held In Person unless otherwise noted**

Fall 2025

Seminar One

September 11, 2025
Griffin Floyd Hall, Room 100

Martin Slawski
University of Virginia

Title:

Linked Data Analysis without Linkage

Abstract:

Record Linkage (RL) is widely regarded as the gold standard for leveraging information scattered across multiple files. At the same time, RL can be tedious, error-prone, and privacy-invasive, which has hampered a more widespread and streamlined adoption. In this talk, I introduce Unlinked Data Analysis as a novel paradigm enabling inference for associations of variables across files, without linking them.

In the first part, I discuss the task of learning a map between d-dimensional inputs and d-dimensional noisy outputs, without observing (input, output)-pairs, but only separate unordered lists of inputs and outputs. I show that this problem can be solved for Brenier maps and present a practical algorithm for denoising based on the Kiefer-Wolfowitz nonparametric maximum likelihood estimator (NPMLE) and techniques from optimal transport. I provide upper bounds on the resulting mean squared denoising error under (i) Gaussian noise and (ii) noise from a certain class of elliptic distributions.

In the second part, I give an overview of linear regression in the unlinked setting, with a particular focus on existing results and open problems related to identifiability.

Seminar Two

September 25, 2025
Zoom ID: https://ufl.zoom.us/j/93709716185

Jason Altschuler
University of Pennsylvania

Title:

Shifted Divergences for sampling, privacy, and beyond

Abstract:

Shifted divergences provide a principled way of making information-theoretic divergences (e.g., KL) geometrically aware via optimal transport smoothing. In this talk, I will argue that shifted divergences provide a powerful approach towards unifying central problems in optimization, sampling, privacy, functional inequalities, and beyond. For concreteness, I will describe these connections by mentioning several recent highlights, focusing on the first:
(1) Characterizing the mixing time of the Langevin Algorithm to its stationary       distribution for log-concave sampling.
(2) The fastest high-accuracy algorithm for sampling from log-concave distributions.
(3) A positive answer to the acceleration conjecture in sampling.
(4) Characterizing the differential privacy of Noisy-SGD, the standard algorithm for private convex optimization.
(5) Tight shift-Harnack inequalities and simple proofs of Wang’s celebrated dimension-free Harnack inequalities. A recurring theme is a certain notion of algorithmic stability, and the central technique for establishing this is shifted divergences (or its more powerful abstraction, the Shifted Composition Rule).

Based on joint works with Kunal Talwar, with Sinho Chewi, and with Jinho Bok and Weijie Su.

Seminar Three

October 9, 2025
Griffin-Floyd Hall, Room 100

Krishna Balasubramanian
University of California-Davis

Title:

Riemannian Proximal Sampler for High-accuracy Sampling on Manifolds

Abstract:

Sampling from densities defined on Riemannian manifolds is central to Bayesian inference, generative modeling, and differential privacy. We introduce the Riemannian Proximal Sampler (RPS), whose efficiency hinges on two oracles: Manifold Brownian Increments and the Riemannian Heat Kernel. We establish high-accuracy sampling guarantees for the Riemannian Proximal Sampler, showing that generating samples with ε-accuracy requires O(log(1/ε)) iterations in Kullback-Leibler divergence assuming access to exact oracles and O(log^2(1/ε)) iterations in the total variation metric assuming access to sufficiently accurate inexact oracles. Furthermore, we present two practical implementations of these oracles by leveraging heat-kernel truncation and Varadhan’s asymptotics, respectively. In the latter case, we interpret the Riemannian Proximal Sampler as a discretization of the entropy-regularized Riemannian Proximal Point Method on the associated Wasserstein space. We will discuss numerical results that illustrate the effectiveness of the proposed methodology.

Seminar Four

October 23, 2025
Griffin-Floyd Hall, Room 100

Sanjay Chaudhuri
University of Nebraska

Title:

TBD

Abstract:

TBD

Seminar Five-Challis Lecture-Day One

October 28, 2025
Reitz Union, Room G320

Nancy Reid 
University of Toronto

Title:

TBD

Abstract:

TBD

Seminar Six-Challis Lecture-Day Two

October 29, 2025
Reitz Union, Room G320

Nancy Reid 
University of Toronto

Title:

TBD

Abstract:

TBD

Seminar Seven

November 6, 2025
Griffin-Floyd Hall, Room 100

Didong Li
UNC Chapel Hill

Title:

TBD

Abstract:

TBD

Seminar Eight

November 13, 2025
Griffin-Floyd Hall, Room 100

Nandita Mitra
University of Pennsylvania

Title:

TBD

Abstract:

TBD