TILOS Seminar Series

Elchanan Mossel, Professor of Mathematics, MIT Abstract: Classical Machine Learning theory is largely built upon the assumption that data samples are independent and identically distributed (i.i.d.) from general distribution families. In this talk, I will present novel insights that emerge when we move beyond these traditional assumptions, exploring both dependent sampling scenarios and structured generative […]

Rebecca Willett, University of Chicago Abstract: Algorithmic stability holds when our conclusions, estimates, fitted models, predictions, or decisions are insensitive to small changes to the training data. Stability has emerged as a core principle for reliable data science, providing insights into generalization, cross-validation, uncertainty quantification, and more. Whereas prior literature has developed mathematical tools for […]

Jason Lee, Princeton University Abstract: The incredible success of transformers on sequence modeling tasks can be largely attributed to the self-attention mechanism, which allows information to be transferred between different parts of a sequence. Self-attention allows transformers to encode causal structure which makes them particularly suitable for sequence modeling. However, the process by which transformers […]

Rajesh Rajamani, University of Minnesota Abstract: This talk presents some interesting problems in estimation related to vehicle autonomy. First, a teleoperation application in which a remote operator can intervene to control an autonomous vehicle is considered. Fundamental challenges here include the need to design an effective teleoperation station, bandwidth and time-criticality constraints in wireless communication, […]

Surbhi Goel, University of Pennsylvania Abstract: Understanding how different components of the machine learning pipeline—spanning data composition, architectural choices, and optimization dynamics—shape model behavior remains a fundamental challenge. In this talk, I will argue that synthetic tasks, which enable precise control over data distribution and task complexity, serve as powerful testbeds for analyzing and attributing […]

Claire Boyer, Université Paris-Saclay Abstract: Attention-based models, such as Transformer, excel across various tasks but lack a comprehensive theoretical understanding, especially regarding token-wise sparsity and internal linear representations. To address this gap, we introduce the single-location regression task, where only one token in a sequence determines the output, and its position is a latent random […]

Michael W. Mahoney, ICSI, LBNL, and Department of Statistics, UC Berkeley Abstract: The remarkable successes of ChatGPT in natural language processing (NLP) and related developments in computer vision (CV) motivate the question of what foundation models would look like and what new advances they would enable, when built on the rich, diverse, multimodal data that […]

Petar Veličković, Google DeepMind Abstract: Recent years have seen a significant surge in complex AI systems for competitive programming, capable of performing at admirable levels against human competitors. While steady progress has been made, the highest percentiles still remain out of reach for these methods on standard competition platforms such as Codeforces. In this talk, […]

Yury Polyanskiy, MIT Abstract: The main building block of large language models is matrix multiplication, which is often bottlenecked by the speed of loading these matrices from memory. A number of recent quantization algorithms (SmoothQuant, GPTQ, QuIP, SpinQuant etc) address this issue by storing matrices in lower precision. We derive optimal asymptotic information-theoretic tradeoff between […]