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Found 18 papers, 0 papers with code
Allocating Divisible Resources on Arms with Unknown and Random Rewards
We consider a decision maker allocating one unit of renewable and divisible resource in each period on a number of arms.
Algorithmic Decision-Making Safeguarded by Human Knowledge
In view of such a conflict, we provide a general analytical framework to study the augmentation of algorithmic decisions with human knowledge: the analyst uses the knowledge to set a guardrail by which the algorithmic decision is clipped if the algorithmic output is out of bound, and seems unreasonable.
Learning Consumer Preferences from Bundle Sales Data
In this paper, we propose an approach to learn the distribution of consumers' valuations toward the products using bundle sales data.
Bridging Adversarial and Nonstationary Multi-armed Bandit
In the multi-armed bandit framework, there are two formulations that are commonly employed to handle time-varying reward distributions: adversarial bandit and nonstationary bandit.
Debiasing Samples from Online Learning Using Bootstrap
It has been recently shown in the literature that the sample averages from online learning experiments are biased when used to estimate the mean reward.
Sublinear Regret for Learning POMDPs
We study the model-based undiscounted reinforcement learning for partially observable Markov decision processes (POMDPs).
Distributionally Robust Prescriptive Analytics with Wasserstein Distance
In prescriptive analytics, the decision-maker observes historical samples of $(X, Y)$, where $Y$ is the uncertain problem parameter and $X$ is the concurrent covariate, without knowing the joint distribution.
Multi-armed Bandit Requiring Monotone Arm Sequences
We consider the continuum-armed bandit problem when the arm sequence is required to be monotone.
Revenue Maximization and Learning in Products Ranking
When the conditional purchase probabilities are not known and may depend on consumer and product features, we devise an online learning algorithm that achieves $\tilde{\mathcal{O}}(\sqrt{T})$ regret relative to the approximation algorithm, despite the censoring of information: the attention span of a customer who purchases an item is not observable.
Dimension Reduction in Contextual Online Learning via Nonparametric Variable Selection
Our algorithm achieves the regret $\tilde{O}(T^{(d_x^*+d_y+1)/(d_x^*+d_y+2)})$, where $d_x^*$ is the effective covariate dimension.
Learning and Optimization with Seasonal Patterns
We show that our learning policy incurs a regret upper bound $\tilde{O}(\sqrt{T\sum_{k=1}^K T_k})$ where $T_k$ is the period of arm $k$.
Regime Switching Bandits
We study a multi-armed bandit problem where the rewards exhibit regime switching.
The Use of Binary Choice Forests to Model and Estimate Discrete Choices
We also prove that the random forest can recover preference rankings of customers thanks to the splitting criterion such as the Gini index and information gain ratio.
A Dimension-free Algorithm for Contextual Continuum-armed Bandits
The literature has shown that for Lipschitz-continuous functions, the optimal regret is $\tilde{O}(T^{\frac{d_x+d_y+1}{d_x+d_y+2}})$, where $d_x$ and $d_y$ are the dimensions of contexts and arms, and thus suffers from the curse of dimensionality.
A Primal-dual Learning Algorithm for Personalized Dynamic Pricing with an Inventory Constraint
We consider the problem of a firm seeking to use personalized pricing to sell an exogenously given stock of a product over a finite selling horizon to different consumer types.
Nonparametric Pricing Analytics with Customer Covariates
We propose a nonparametric pricing policy to simultaneously learn the preference of customers based on the covariates and maximize the expected revenue over a finite horizon.
Boosted nonparametric hazards with time-dependent covariates
Given functional data from a survival process with time-dependent covariates, we derive a smooth convex representation for its nonparametric log-likelihood functional and obtain its functional gradient.
Super-resolution estimation of cyclic arrival rates
Exploiting the fact that most arrival processes exhibit cyclic behaviour, we propose a simple procedure for estimating the intensity of a nonhomogeneous Poisson process.
