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UID:426@fds.yale.edu
DTSTART;TZID=America/New_York:20221219T123000
DTEND;TZID=America/New_York:20221219T133000
DTSTAMP:20240226T190410Z
URL:https://fds.yale.edu/events/fds-seminar-alkis-kalavasis-national-techn
ical-university-of-athens/
SUMMARY:FDS Seminar: Alkis Kalavasis (National Technical University of Athe
ns)
DESCRIPTION:"Efficient Algorithms and Computational Barriers in Reliable Ma
chine Learning"Speaker: Alkis Kalavasis\nNational Technical University of
Athens\nAbstract: In this talk\, we will discuss the computational challen
ges arising in various problems in Reliable Machine Learning. Reliable ML
aims at the design of computationally efficient algorithms that provide gu
arantees such as robustness to biased data\, reproducibility and privacy.
We firstly focus on the design of algorithms robust to biased and corrupte
d observations. We begin with the problem of learning from coarse data. Th
e motivation behind this problem is that in many learning tasks one may no
t have access to fine grained label information\; e.g.\, an image can be l
abeled as husky\, dog\, or even animal depending on the expertise of the a
nnotator. We formalize these settings from the viewpoint of computational
learning theory and provide efficient algorithms and computational hardnes
s results. We then continue with the task of learning noisy linear label r
ankings. Label ranking is the supervised task of learning a sorting functi
on that maps feature vectors to rankings over a finite set of labels. We p
rovide the first efficient algorithms for learning linear sorting function
s in the presence of bounded noise (an extension of the Massart noise cond
ition to label rankings) under Gaussian marginals. Next\, we consider ques
tions regarding responsibility aspects of ML systems. We study the importa
nt problem of reproducibility as an algorithmic property in decision-makin
g settings. We introduce the notion of reproducible policies in the contex
t of stochastic bandits\, one of the canonical problems in interactive lea
rning. A policy in the bandit environment is called reproducible if it pul
ls\, with high probability\, the exact same sequence of arms in two differ
ent and independent executions (under independent reward realizations and
shared internal randomness). We show that not only do reproducible policie
s exist\, but also they achieve almost the same optimal (non-reproducible)
regret bounds in terms of the time horizon. In the end of the talk\, we w
ill shortly discuss some on-going work on the complexity of min-max optimi
zation\, a fundamental problem in the area of equilibrium computation in m
ulti-agent environments.
CATEGORIES:Postdoctoral Applicants
LOCATION:Webcast\, \,
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