UWMadison SILO Seminar

Sanjay Shakkottai - "Towards discrete diffusion models for language and image generation"

Peter Frazier - "Bayesian Preference Exploration: Making Optimization Accessible to Non-Experts"

Michael W. Mahoney - "Random Matrix Theory and Modern Machine Learning"

Sujay Sanghavi - "Faster Diffusion Language Models"

Philip Thomas - "Qualia Optimization: Exploring Mathematical Formulations of AI Experience"

Allen Liu - "Revealing the Low Rank Structure of Language Models through Sequences of Logits"

Searching for architectures and BERT moments in specialized AI applications

Kevin Jamieson - "Some Online Combinatorial Optimization and Dynamic Pricing Problems"

Gavin Brown - "Stable Estimators for Fast Private Statistics"

Oktay Günlük – “Recovering Dantzig-Wolfe Bounds by Cutting Planes”

Ben Grimmer - "Optimizing Optimization Methods, To and Beyond Minimax Optimality"

Trevor Campbell - "Automating Statistical Inference for Modern Probabilistic Models

Debdeep Pati - "Variational inference – reconciling statistical and convergence guarantees"

Jeff Schneider - "Reinforcement Learning and Bayesian Optimization for Nuclear Fusion"

Ilias Zadik - "Characterizing the power of MCMC methods for sparse estimation"

Devavrat Shah - “On counterfactual inference with unobserved confounding via exponential family”

Arash Amini - "Polynomial Graph Neural Networks: Theoretical Limits and Graph Noise Impact"

Kaiqing Zhang - “Towards Principled AI-Agents with Decentralized and Asymmetric Information”

Alberto Del Pia - “Minimizing quadratics over integers”

Jean Kossaifi - "Neural Operators for Scientific Applications: Learning on Function Spaces“

Sandeep Silwal - "Efficiently searching for distributions"

Dimitris Papailiopoulos - "Self-Improving Transformers: Overcoming Length Generalization Challenges"