Spring 2025

Feb 7 – Rajarshi Mukherjee, Harvard University
Inference for ATE & GLM’s when p/n→δ∈(0,∞)

Feb 14 – No Seminar

Feb 21 – David Alvarez-Melis, Harvard University
Towards a ‘Chemistry of AI’: Unveiling the Structure of Training Data for more Scalable and Robust Machine Learning

Feb 28 – Ashia Wilson, MIT
Two Approaches Towards Adaptive Optimization 

Mar 7 – Krishna Balasubramanian, University of California – Davis
Finite-Particle Convergence Rates for Stein Variational Gradient Descent

Mar 14 – Murat A. Erdogdu, University of Toronto
Feature Learning and Scaling Laws in Two-layer Neural Networks: A high dimensional analysis

Mar 21 – Claire Donnat, University of Chicago
Structured Topic Modeling: Leveraging Sparsity and Graphs for Improved Inference

Mar 28 – No Seminar

Apr 4 – Jessica Hullman, Northwestern University
The value of information in model assisted decision-making

Apr 11 – Jann Spiess, Stanford University
Causal Inference on Outcomes Learned from Text

Apr 18 – Dennis Shen, University of Southern California
Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data

Apr 25 – Richard Samworth, Unviersity of Cambridge
How should we do linear regression?

May 2 – Aaron Roth, University of Pennsylvania
Tractable Agreement Protocols

May 9 – Sivaraman Balakrishnan, Carnegie Mellon University
Fundamental statistical limits in causal inference