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Kyungbok Lee

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3 papers

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AAAI Conference 2024 Conference Paper

Mixed-Effects Contextual Bandits

Kyungbok Lee
Myunghee Cho Paik
Min-hwan Oh
Gi-Soo Kim

We study a novel variant of a contextual bandit problem with multi-dimensional reward feedback formulated as a mixed-effects model, where the correlations between multiple feedback are induced by sharing stochastic coefficients called random effects. We propose a novel algorithm, Mixed-Effects Contextual UCB (ME-CUCB), achieving tildeO(d sqrt(mT)) regret bound after T rounds where d is the dimension of contexts and m is the dimension of outcomes, with either known or unknown covariance structure. This is a tighter regret bound than that of the naive canonical linear bandit algorithm ignoring the correlations among rewards. We prove a lower bound of Omega(d sqrt(mT)) matching the upper bound up to logarithmic factors. To our knowledge, this is the first work providing a regret analysis for mixed-effects models and algorithms involving weighted least-squares estimators. Our theoretical analysis faces a significant technical challenge in that the error terms do not constitute martingales since the weights depend on the rewards. We overcome this challenge by using covering numbers, of theoretical interest in its own right. We provide numerical experiments demonstrating the advantage of our proposed algorithm, supporting the theoretical claims.

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NeurIPS Conference 2024 Conference Paper

What Variables Affect Out-of-Distribution Generalization in Pretrained Models?

Yousuf Harun
Kyungbok Lee
Jhair Gallardo
Giri Krishnan
Christopher Kanan

Embeddings produced by pre-trained deep neural networks (DNNs) are widely used; however, their efficacy for downstream tasks can vary widely. We study the factors influencing transferability and out-of-distribution (OOD) generalization of pre-trained DNN embeddings through the lens of the tunnel effect hypothesis, which is closely related to intermediate neural collapse. This hypothesis suggests that deeper DNN layers compress representations and hinder OOD generalization. Contrary to earlier work, our experiments show this is not a universal phenomenon. We comprehensively investigate the impact of DNN architecture, training data, image resolution, and augmentations on transferability. We identify that training with high-resolution datasets containing many classes greatly reduces representation compression and improves transferability. Our results emphasize the danger of generalizing findings from toy datasets to broader contexts.

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AAAI Conference 2023 Conference Paper

Double Doubly Robust Thompson Sampling for Generalized Linear Contextual Bandits

Wonyoung Kim
Kyungbok Lee
Myunghee Cho Paik

We propose a novel algorithm for generalized linear contextual bandits (GLBs) with a regret bound sublinear to the time horizon, the minimum eigenvalue of the covariance of contexts and a lower bound of the variance of rewards. In several identified cases, our result is the first regret bound for generalized linear bandits (GLBs) achieving the regret bound sublinear to the dimension of contexts without discarding the observed rewards. Previous approaches achieve the regret bound sublinear to the dimension of contexts by discarding the observed rewards, whereas our algorithm achieves the bound incorporating contexts from all arms in our double doubly robust (DDR) estimator. The DDR estimator is a subclass of doubly robust estimator but with a tighter error bound. We also provide a logarithmic cumulative regret bound under a probabilistic margin condition. This is the first regret bound under the margin condition for linear models or GLMs when contexts are different for all arms but coefficients are common. We conduct empirical studies using synthetic data and real examples, demonstrating the effectiveness of our algorithm.

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