idw – Informationsdienst Wissenschaft
Nachrichten, Termine, Experten
Nachrichten, Termine, Experten
Abstract:
Contextual bandit problems model the inherent cost of learning in personalized
decision-making in new environments, whether in marketing, healthcare, or revenue
management. Specifically, the cost is characterized by the optimal growth rate of the
regret in cumulative rewards compared to an optimal policy given full prior knowledge
of the environment. Naturally, the optimal rate should depend on how complex the
underlying supervised learning problem is, namely how much can observing rewards in
one context tell us about mean rewards in another context. Curiously, this
obvious-seeming relationship is obscured in current theory that separately studies the
easy, fully-extrapolatable case and hard, super-local case. To characterize the
relationship more precisely, I study a nonparametric contextual bandit problem where
expected reward functions are β-smooth (roughly meaning β-times differentiable). I
will show how this interpolates between the two extremes previously studied in
isolation: non-differentiable-response bandits (β ≤ 1), where rate-optimal regret is
achieved by decomposing the problem into non-contextual bandits, and
parametric-response bandits (β = ∞), where rate-optimal regret is often achievable
without any exploration at all. We develop a novel algorithm that works for any given
smoothness setting by operating neither fully locally nor fully globally. We prove its
regret is rate-optimal, thereby characterizing the optimal regret rate and revealing a
fuller picture of the crucial interplay between complexity and regret in dynamic
decision-making. Time permitting, I will also discuss how to construct valid
confidence intervals from data collected by contextual bandits, a crucial challenge in
the enterprise to replace randomized trials with adaptive experiments in applied
fields from biostatistics to development economics.
This talk is based on the following papers:
(1) Smooth Contextual Bandits: Bridging the Parametric and Non-differentiable Regret
Regimes (https://pubsonline.informs.org/doi/abs/10.1287/opre.2021.2237)
(2) Post-Contextual-Bandit Inference
(https://papers.nips.cc/paper/2021/hash/eff3058117fd4cf4d4c3af12e273a40f-Abstract...)
Short Bio:
Nathan Kallus is an Associate Professor in the School of Operations Research and
Information Engineering and Cornell Tech at Cornell University. He also holds a
Research Director position for the Product Machine Learning Research at Netflix.
Nathan's research interests include personalization; optimization, especially under
uncertainty; causal inference; sequential decision-making; credible and robust
inference; and algorithmic fairness. He holds a PhD in Operations Research from MIT as
well as a BA in Mathematics and a BS in Computer Science both from UC Berkeley. Before
coming to Cornell, Nathan was a Visiting Scholar at USC's Department of Data Sciences
and Operations and a Postdoctoral Associate at MIT's Operations Research and
Statistics group.
Hinweise zur Teilnahme:
The talk will take place in a hybrid mode with a physical presence in the Bernd Therre lecture hall at CISPA’s main building in Saarbrücken (Stuhlsatzenhaus 5) and via Zoom:
https://cispa-de.zoom.us/j/61118095073?pwd=UmM4bnNuamVjeVQwRy9qTDhIbHNyZz09
ID: 611 1809 5073
Passcode: F8F7%*
Termin:
04.07.2023 10:00 - 12:00
Veranstaltungsort:
CISPA Helmholtz Center for Information Security
Stuhlsatzenhaus 5
Bernd Therre lecture hall (Ground Floor)
66123 Saarbrücken
Saarland
Deutschland
Zielgruppe:
Studierende, Wissenschaftler
Relevanz:
überregional
Sachgebiete:
Informationstechnik
Arten:
Vortrag / Kolloquium / Vorlesung
Eintrag:
29.06.2023
Absender:
Dr. Felix Koltermann
Abteilung:
Unternehmenskommunikation
Veranstaltung ist kostenlos:
ja
Textsprache:
Deutsch
URL dieser Veranstaltung: http://idw-online.de/de/event74735
DLS Talk: Smooth Contextual Bandits (Nathan Kallus) Sie können Suchbegriffe mit und, oder und / oder nicht verknüpfen, z. B. Philo nicht logie.
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