Speaker |
Samory Kpotufe |
|---|---|
Affiliation |
Columbia University |
Date |
Friday, 21 October 2022 |
Time |
12:00-13:00 |
Location |
Function Space, UCL Centre for Artificial Intelligence, 1st Floor, 90 High Holborn, London WC1V 6BH |
Link |
https://ucl.zoom.us/j/97245943682 |
Event series |
Jump Trading/ELLIS CSML Seminar Series |
Abstract |
Domain adaptation, transfer, multitask, meta, few-shots, representation, or lifelong learning … these are all important recent directions in ML that all touch at the core of what we might mean by ‘AI’. As these directions all concern learning in heterogeneous and ever-changing environments, they all share a central question; what information a data distribution may have about another, critically, in the context of a given estimation problem, e.g., classification, regression, bandits, etc. Our understanding of these problems is still rather fledgeling. We plan to present both some recent positive results and also some negative ones. On one hand, recent measures of discrepancy between distributions, fine-tuned to given estimation problems (classification, bandits, etc) offer a more optimistic picture than existing probability metrics (e.g. Wasserstein, TV) or divergences (KL, Renyi, etc) in terms of achievable rates. On the other hand, when considering seemingly simple extensions to choices between multiple datasets (as in multitask), or multiple prediction models (as in Structural Risk Minimization), it turns out that minimax oracle rates are not always adaptively achievable, i.e., using just the available data without side information. These negative results suggest that domain adaptation is more structured in practice than captured by common invariants considered in the literature. The talk will be based on joint work with collaborators over the last few years, namely, G. Martinet, S. Hanneke, J. Suk. |
Biography |
Samory Kpotufe is an Associate Professor in the Department of Statistics of Columbia University. He graduated (Sept 2010) in Computer Science at the University of California, San Diego, advised by Sanjoy Dasgupta. He then was a researcher at the Max Planck Institute for Intelligent Systems. At the MPI he worked in the department of Bernhard Schoelkopf, in the learning theory group of Ulrike von Luxburg. Following this, he spent a couple years as an Assistant Research Professor at the Toyota Technological Institute at Chicago. He then spent 4 years at ORFE, Princeton University as an Assistant Professor. He was a visiting member at the Institute of Advanced Study from January to July 2020. |