Statistics Seminar - Fall 2025

For questions about the seminar schedule, please contact Chenlu Shi and Yan Sun

September 18

Dr. Yingcong Li, NJIT Department of Data Science

Data, Architecture & Algorithms in In‑Context Learning

This talk introduces recent theoretical advancements on the in-context learning (ICL) capability of sequence models, focusing on the intricate interplay of data characteristics, architectural design, and the implicit algorithms models learn. We discuss how diverse architectural designs—ranging from linear attention to state-space models to gating mechanisms—implicitly emulate optimization algorithms that operate on the context and draw connections to variations of gradient descent and expectation maximization. We elucidate the critical influence of data characteristics, such as distributional alignment, task correlation, and the presence of unlabeled examples, on ICL performance, quantifying their benefits and revealing the mechanisms through which models leverage such information. Furthermore, we will explore the optimization landscapes governing ICL, establishing conditions for unique global minima and highlighting the architectural features (e.g., depth and dynamic gating) that enable sophisticated algorithmic emulation. As a central message, we advocate that the power of architectural primitives can be gauged from their capability to handle in-context regression tasks with varying sophistication.

Homepage: https://yingcong-li.github.io/

October 9

Dr. Yonghoon Lee, University of Pennsylvania, Department of Statistics

Distribution-free inference: toward conditional inference

We discuss the problem of distribution-free conditional predictive inference. Prior work has established that achieving exact finite-sample control of conditional coverage without distributional assumptions is impossible, suggesting the need for relaxed settings or targets. In Part 1, we consider data with a hierarchical structure and discuss possible targets of conditional predictive inference under repeated measurements. We show that the $L^k$-norm of the conditional miscoverage rate can generally be controlled and provide procedures that achieve this coverage guarantee. In Part 2, we turn to the standard i.i.d. setting and introduce an inferential target motivated by the multiaccuracy condition, which enables conditional inference with an interpretable guarantee. Our method controls the $L^k$-norm of a relaxed notion of the conditional miscoverage rate, with a finite-sample, distribution-free guarantee.

Homepage: https://yhoon31.github.io/index.html

October 30

Dr. Kieran Murphy, NJIT Department of Computer Science

Making neural networks tell you what information they use

While artificial neural networks are undoubtedly excellent information processing systems, their predominant formulation as deterministic point-to-point transformations makes it hard to say anything about what they actually do with information from the perspective of information theory.  By inserting a probabilistic representation space into the system -- nothing more complicated than what you'd find in a variational autoencoder (VAE) -- we can quantify and characterize all information passing through the space.  In this talk, we'll view such a space as an optimizable communication channel, and then construct communication networks that reveal where information resides in the original data and how different architectures process it.  We'll close by exploring ways to characterize how information is organized in these learned spaces.

Homepage: https://kieranamurphy.com/

November 20

Dr. Boxiang Wang, University of Iowa

Title/Abstract Forthcoming

Homepage: https://stat.uiowa.edu/people/boxiang-wang

Updated: October 16, 2025