SILO: Online Learning Guided Quasi-Newton Methods: Improved Global Non-asymptotic Guarantees
2024-03-06 12:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Aryan Mokhtari, UT

Low-Rank Structures in Optimal Transport
2024-03-08 13:30 - 14:30 (America/Chicago) | CSE (Allen) 403
Bio: Meyer Scetbon is currently a Research Scientist at Microsoft Research. He completed his PhD at Institut Polytechnique de Paris, advised by M. Cuturi. He did, as a visiting student, his MS theses UW and Technion, on kernel-based viewpoints on deep neural networks advised by Z. Harchaoui, and end-to-end signal and image denoising advised by M. Elad, respectively.

Abstract: Optimal transport (OT) plays an increasingly important role in machine learning (ML) to compare probability distributions. Yet, it poses, in its original form, several challenges when used for applied problems: (i) computing OT between discrete distributions amounts to solving a large and expensive network flow problem which requires a supercubic complexity in the number of points; (ii) estimating OT using sampled measures is doomed by the curse of dimensionality. These issues can be mitigated using an entropic regularization, solved with the Sinkhorn algorithm, which improves on both statistical and computational aspects. While much faster, entropic OT still requires a quadratic complexity with respect to the number of points and therefore remains prohibitive for large-scale problems. In this talk, I will present new regularization approaches for the OT problem, as well as its quadratic extension, the Gromov-Wasserstein (GW) problem, which impose low-rank structures on the admissible couplings. This results in the development of new algorithms that enjoy a linear complexity both in time and memory with respect to the number of points, enabling their applications in the large-scale setting where millions of points need to be compared. Additionally, I will show that these new regularization schemes have better statistical performances compared to the entropic approach, that they naturally interpolate between the Maximum Mean Discrepancy (MMD) and OT, and that they offer general clustering methods for arbitrary geometry.Website: <https://meyerscetbon.github.io/_pages/publications/>

Towards Plurality: Learning from Diverse Human Preferences
2024-03-11 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Speaker: Ramya Vinayak

Abstract:
Large pre-trained models trained on internet-scale data are often not ready for safe deployment out of the box. They are heavily fine-tuned and aligned using large quantities of human preference data. When we want to align an AI/ML model to human preference or values, it is worthwhile to ask whose preference and values we are aligning it to? Recently, the limitations of current approaches due to their inherent uniformity assumption have been highlighted and the need for plurality – capturing the diversity in human preferences and values – is getting recognized as an important challenge to address. While alignment from human preferences has currently become a very active area of research, it is worthwhile to note that there is rich literature on learning preferences from human judgements using comparison queries. It plays a crucial role in several applications ranging from cognitive and behavioral psychology, crowdsourcing democracy, surveys in social science applications, and recommendation systems. However, the models in the literature often focus on learning average preference over the population due to the limitations on the amount of data available per individual and also assume the knowledge of the metric or way humans judge similarity and dissimilarity.
In this talk, I will discuss some recent results that focus on how we can reliably capture diversity in preferences while pooling together data from individuals. In particular, I will talk about fundamental questions in two directions: (1) Simultaneous metric and preference learning where the goal is to learn an unknown shared metric from preference queries while the preferences are diverse and also unknown. (2) Learning distribution of preferences over a population with a single comparison query per individual.
Bio:
Ramya Korlakai Vinayak is an assistant professor in the Dept. of ECE and affiliated faculty in the Dept. of Computer Science and the Dept. of Statistics at the UW-Madison. Her research interests span the areas of machine learning, statistical inference, and crowdsourcing. Her work focuses on addressing theoretical and practical challenges that arise when learning from heterogeneous data from people. Prior to joining UW-Madison, Ramya was a postdoctoral researcher in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. She received her Ph.D. in Electrical Engineering from Caltech. She obtained her Masters from Caltech and Bachelors from IIT Madras. She is a recipient of the Schlumberger Foundation Faculty of the Future fellowship from 2013-15, and an invited participant at the Rising Stars in EECS workshop in 2019. She is the recipient of NSF CAREER Award 2023-2028.

SILO: Unsupervised Learning: Validation beyond Visualization
2024-03-13 12:30 (America/Chicago) | Researchers’ Link, 330 N Orchard St, Madison, WI, 53715, United States
Marina Meila, UWash

Optimized Decision Making via Active Learning of Stochastic Hamiltonians
2024-03-15 13:30 - 14:30 (America/Los_Angeles) | CSE (Allen) 403
Speaker: Prof. Chandrajit Bajaj , UT Austin
Abstract: A Hamiltonian represents the energy of a dynamical system in phase space with coordinates of position and momentum. The Hamilton’s equations of motion are obtainable as coupled symplectic differential equations. In this talk I shall show how optimized decision making (action sequences) can be obtained via a reinforcement learning problem wherein the agent interacts with the unknown environment to simultaneously learn a Hamiltonian surrogate and the optimal action sequences using Hamilton dynamics, by invoking the Pontryagin Maximum Principle. We use optimal control theory to define an optimal control gradient flow, which guides the reinforcement learning process of the agent to progressively optimize the Hamiltonian while simultaneously converging to the optimal action sequence. Extensions to stochastic Hamiltonians leading to stochastic action sequences and the free-energy principle shall also be discussed. This is joint work with Taemin Heo, Minh Nguyen.

IFDS Ideas Forum Double Header
2024-03-18 12:30 - 13:30 (America/Chicago) | WID 1145, 330 N Orchard Street, Madison, WI, 53715, United States
Karan Srivastava

Harit Vishwakarma: “Confidence functions for auto-labeling”

SILO:
2024-03-20 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Chao Gao, U Chicago

SILO:
2024-04-03 12:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Devavrat Shah, MIT

SILO:
2024-04-10 12:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
CMU, Weina Wang

SILO:
2024-04-17 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Zachary Lubberts, UVA

SILO: Generalized Tensor Decompositions: Algorithms and Applications
2024-04-24 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
David Hong, UDel

SILO:
2024-05-01 12:30 - 13:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Sam Hopkins, MIT

SILO:
2024-05-08 12:30 (America/Chicago) | Orchard View Room, 330 N. Orchard Street, 3rd Floor NE, Madison, Wisconsin, 53715, United States
Max Raginsky, UIUC

2026 IFDS Research Expo
2026-08-10 - 2026-08-12
