IFDS

SILO: Connections Between Replicability, Privacy, and Perfect Generalization
2023-10-04 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Jessica Sorrell, UPenn

IFDS Ideas Forum
2023-10-09 12:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Geometry-Aware Adaptation for Pretrained Models
Speaker: Nicholas Roberts

SILO: Distributional Shifts Problems and Distributionally Robust Solutions
2023-10-12 16:00 (America/Chicago) | Researchers’ Link, 330 N Orchard St, Madison, WI, 53715, United States
U Wash, Zaid Harchaoui

IFDS Ideas Forum
2023-10-16 12:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Stochastic Regularized Majorization-Minimization / Variance Reduction Beyond Minimization
Speaker: Hanbaek Lyu / Ahmet Alacaoglu

SILO: Example Selection and Post-Training Quantization
2023-10-18 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Christopher De Sa, Cornell

NSF TRIPODS GRAPFiCs Workshop
2023-10-19 - 2023-10-20

IFDS Ideas Forum
2023-10-23 12:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
A Theoretical Analysis of In-context Task Retrieval and Learning/On Squared-Variable Formulations (and LP)
Speakers: Ziqian Lin/ Lijun Ding

SILO: Understanding Deep Learning through Optimization Bias
2023-10-25 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Nati Srebro, TTIC

IFDS Ideas Forum
2023-10-30 12:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Human-in-the-Loop Out of Distribution Detection with False Positive Rate Guarantees
Speakers: Harit Vishwakarma/Ramya Vinayak

SILO: Balanced Filtering via Disclosure-Controlled Proxies
2023-11-01 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Emily Diana, TTIC

IFDS Ideas Forum
2023-11-06 12:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
TBD
Speakers: Joe Shenouda/Matthew Zurek

SILO: Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty
2023-11-08 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Ellen Vitercik, Stanford

IFDS Ideas Forum
2023-11-13 12:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
TBD
Speakers: William Powell / Andrew Lowy

SILO: A Theory of Universal Learning
2023-11-15 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Purdue, Steve Hanneke

IFDS Ideas Forum
2023-11-20 12:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
TBD
Speakers: Karan Srivastava / Chenghui Li

SILO: World Knowledge in the Time of Large Models
2023-11-22 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
DeepMind, Kenneth Marino

IFDS Ideas Forum
2023-11-27 12:30 (America/Chicago)
TBD
Speaker: Zarifis Nikos

SILO: Understanding deep nets: on local Lipschitz functions and learned proximal networks
2023-11-29 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Jeremias Sulam, JHU

IFDS Ideas Forum
2023-12-04 12:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
TBD
Speakers: Jitian Zhao / Zhexuan Liu

SILO: Invariant Low-Dimensional Subspaces in Gradient Descent for Learning Deep Networks
2023-12-06 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Qing Qu, UMich

IFDS Ideas Forum
2023-12-11 12:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
TBD
Speaker: Thanasis Pittas

IFDS Ideas Forum
2023-12-18 12:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
TBD
Speaker: Sushrut Karmalkar

SILO: Funsearch: a novel approach to using large language models as part of mathematical practice
2024-01-24 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Jordan Ellenberg

How do neural networks learn features from data?
2024-01-26 13:30 - 14:30 (America/Los_Angeles) | CSE (Allen) 403
Speaker Bio: Adit is currently the George F. Carrier Postdoctoral Fellow in the School of Engineering and Applied Sciences at Harvard. He completed his Ph.D. in electrical engineering and computer science (EECS) at MIT advised by Caroline Uhler and was a Ph.D. fellow at the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard. His research focuses on advancing theoretical foundations of machine learning and developing new methods for tackling biomedical problems.

Abstract: Understanding how neural networks learn features, or relevant patterns in data, for prediction is necessary for their reliable use in technological and scientific applications. We propose a unifying mechanism that characterizes feature learning in neural network architectures. Namely, we show that features learned by neural networks are captured by a statistical operator known as the average gradient outer product (AGOP). Empirically, we show that the AGOP captures features across a broad class of network architectures including convolutional networks and large language models. Moreover, we use AGOP to enable feature learning in general machine learning models through an algorithm we call Recursive Feature Machine (RFM). We show that RFM automatically identifies sparse subsets of features relevant for prediction and explicitly connects feature learning in neural networks with classical sparse recovery and low rank matrix factorization algorithms. Overall, this line of work advances our fundamental understanding of how neural networks extract features from data, leading to the development of novel, interpretable, and effective models for use in scientific applications.

SILO: A scalable method to exploit screening in Gaussian process models with noise
2024-01-31 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Chris Geoga

Towards a new toolbox of optimal statistical primitives
2024-02-05 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Abstract: Given society’s increasing reliance on data, its collection and processing into useful information is a technical problem of growing focus, and perhaps paradoxically, a critical bottleneck in many data science and machine learning applications. My research focuses on designing algorithms that push the limits of both statistical efficiency and computational efficiency. In particular, my work tackles the divide between the theory and practice of data science, which exists even for the most basic statistical problems including mean and (co)variance estimation. Conventional methods such as the sample mean, while supported by theoretical results under strong assumptions, are often brittle in the presence of extreme data points. To counter such deficiencies, practitioners often use ad-hoc and unprincipled “outlier removal” heuristics, revealing a marked gap between the theory and practice even for these fundamental problems.
In this talk, I will describe my work towards building a new toolbox of optimal statistical primitives, bridging the theory-practice divide. I will specifically highlight 3 works: A) constructing a statistically-optimal and computationally-efficient 1-dimensional mean estimator, whose estimation error is optimal even in the leading multiplicative constant, under bare minimum distributional assumptions, B) a rather different but also optimal mean estimator for the “very high-dimensional” regime, and C) a recent result on robustly clustering Gaussian mixtures based on their covariances even in the presence of adversarial data corruption. To conclude the talk, I will discuss my vision for the new theory and toolbox, serving as a blueprint for my long-term future research.
Bio: Jasper Lee is a postdoctoral research associate at the University of Wisconsin-Madison, mentored by Ilias Diakonikolas in the Department of Computer Sciences, and also affiliated with the Institute for Foundations of Data Science. He completed his PhD at Brown University, advised by Paul Valiant.
His research interests are broadly in the foundations of data science, aiming to design practical, data-efficient and computationally-efficient algorithms for a variety of statistical applications.
His work is partially supported by a Croucher Fellowship for Postdoctoral Research.

SILO: Universality in High-Dimensional Statistics
2024-02-07 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Rishabh Dudeja

Policy Optimization with Compatible Mirror Approximation
2024-02-09 13:30 - 14:30 (America/Los_Angeles) | Zoom
Speaker Bio: Zhihan is a fourth-year PhD student in the Paul G. Allen School of Computer Science & Engineering at University of Washington, advised by Prof. Maryam Fazel. His research interests are broadly in statistics, optimization and machine learning.

Abstract: We propose Compatible Mirror Policy Optimization (CoMPO), a framework that incorporates general function approximation into policy mirror descent methods. In contrast to the popular approach of using the $L_2$ norm to measure function approximation errors (regardless of the mirror map), CoMPO uses the Bregman divergence induced by the specific mirror map for policy projection. Such a compatibility bridges the gap between theory and practice: not only does it achieve fast linear convergence with general function approximation, but it also includes several well-known practical methods as special cases, immediately providing them strong convergence guarantees.

Theoretical exploration of foundation model adaption methods
2024-02-12 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Speaker: Kangwook Lee

SILO: Theoretical Exploration of Foundation Model Adaptation Methods
2024-02-14 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Kangwook Lee
