Julian Zimmert
Julian is passionate about deriving principled solutions for challenging applications. He completed his PhD at the University of Copenhagen about adversarially robust multi-armed bandits problems and has hence worked on a range of problems in online learning and reinforcement learning.
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We initiate the study of contextual dynamic pricing with a heterogeneous population of buyers, where a seller repeatedly (over T rounds) posts prices that depend on the observable
dimensional context and receives binary purchase feedback. Unlike prior work assuming homogeneous buyer types, in our setting the buyer's valuation type is drawn from an unknown distribution with finite support K*. We develop a contextual pricing algorithm based on Optimistic Posterior Sampling with regret K* sqrt(dT), which we prove to be tight in d, T up to logarithmic terms. Finally, we refine our analysis for the non-contextual pricing case, proposing a variance-aware Zooming algorithm that achieves the optimal dependence on K*.
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Bandit Convex Optimization is a fundamental class of sequential decision-making problems, where the learner selects actions from a continuous domain and observes a loss (but not its gradient) at only one point per round. We study this problem in non-stationary environments, and aim to minimize the regret under three standard measures of non-stationarity: the number of switches S in the comparator sequence, the total variation Delta of the loss functions, and the path-length P of the comparator sequence. We propose a polynomial-time algorithm, Tilted Exponentially Weighted Average with Sleeping Experts (TEWA-SE), which adapts the sleeping experts framework from online convex optimization to the bandit setting. For strongly convex losses, we prove that TEWA-SE is minimax-optimal with respect to known S and Delta by establishing matching upper and lower bounds. By equipping TEWA-SE with the Bandit-over-Bandit framework, we extend our analysis to environments with unknown non-stationarity measures. For general convex losses, we introduce a second algorithm, clipped Exploration by Optimization (cExO), based on exponential weights over a discretized action space. While not polynomial-time computable, this method achieves minimax-optimal regret with respect to known S and Delta, and improves on the best existing bounds with respect to P.
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Preview abstract
We initiate the study of contextual dynamic pricing with a heterogeneous population of buyers, where a seller repeatedly (over T rounds) posts prices that depend on the observable
dimensional context and receives binary purchase feedback. Unlike prior work assuming homogeneous buyer types, in our setting the buyer's valuation type is drawn from an unknown distribution with finite support K*. We develop a contextual pricing algorithm based on Optimistic Posterior Sampling with regret K* sqrt(dT), which we prove to be tight in d, T up to logarithmic terms. Finally, we refine our analysis for the non-contextual pricing case, proposing a variance-aware Zooming algorithm that achieves the optimal dependence on K*.
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We propose a new best-of-both-worlds algorithm for bandits with variably delayed feedback. The algorithm improves on prior work by \citep{masoudian2022} by eliminating the need in prior knowledge of the maximal delay $d_{\mathrm{max}}$ and providing tighter regret bounds in both regimes. The algorithm and its regret bounds are based on counts of outstanding observations (a quantity that is observed at action time) rather than delays or the maximal delay (quantities that are only observed when feedback arrives). One major contribution is a novel control of distribution drift, which is based on biased loss estimators and skipping of observations with excessively large delays. Another major contribution is demonstrating that the complexity of best-of-both-worlds bandits with delayed feedback is characterized by the cumulative count of outstanding observations after skipping of observations with excessively large delays, rather than the delays or the maximal delay.
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A Model Selection Approach for Corruption Robust Reinforcement Learning
Chen-Yu Wei
33rd International Conference on Algorithmic Learning Theory (ALT 2022) (2022)
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We develop a model selection approach to tackle reinforcement learning with adversarial corruption in both transition and reward. For finite-horizon tabular MDPs, without prior knowledge on the total amount of corruption, our algorithm achieves a regret bound of O(min{1/∆,√T}+C)where T is the number of episodes, C is the total amount of corruption, and ∆ is the reward gap between the best and the second-best policies. This is the first worst-case optimal bound achieved without knowledge of C, improving previous results of Lykouris et al. (2021); Chen et al. (2021); Wu et al.(2021). For finite-horizon linear MDPs, we develop a computationally efficient algorithm with a regret bound of ̃O(√(1 +C)T), and another computationally inefficient one with O(√T+C),improving the result of Lykouris et al. (2021) and answering an open question by Zhang et al.(2021b). Finally, our model selection framework can be easily applied to other settings including linear bandits, linear contextual bandits, and MDPs with general function approximation, leading to several improved or new results.
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Multiclass logistic regression is a fundamental task in machine learning with applications in classification and boosting. Previous work (Foster et a. 2018) has highlighted the importance of improper predictors for achieving ``fast rates'' in the online multiclass logistic regression problem without suffering exponentially from secondary problem parameters, such as the norm of the predictors in the comparison class. While Foster et al. introduced a statistically optimal algorithm, it is in practice computationally intractable due to its run-time complexity being a large polynomial in the time horizon and dimension of input feature vectors. It has remained an open problem if algorithms exist that are both statistically and computationally efficient. Jezequel et al. answered this question for binary logistic regression by introducing AIOLI, an algorithm based on online newton step.
However, their technique fails to generalize to more than two classes and it remains open whether their algorithm can be applied to practical applications of logistic regression, such as bandit multiclass learning or online boosting.
In this paper, we develop a new algorithm, FOLKLORE, for the problem which runs significantly faster than the algorithm of Foster et al. -- the running time per iteration scales quadratically in the dimension -- at the cost of a linear dependence on the norm of the predictors in the regret bound. This yields the first practical algorithm for online multiclass logistic regression, resolving an open problem of Jezequel et al. We resolve the open questions by deriving an extension of AIOLI to the multi-class setting.
Furthermore, we show that our algorithm can be applied to online bandit multiclass prediction and online multiclass boosting, yielding more practical algorithms for both problems compared to the ones in Foster et al. with similar performance guarantees. Finally, we also provide an online-to-batch conversion result for our algorithm.
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