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Found 10 papers, 0 papers with code
Last Switch Dependent Bandits with Monotone Payoff Functions
In a recent work, Laforgue et al. introduce the model of last switch dependent (LSD) bandits, in an attempt to capture nonstationary phenomena induced by the interaction between the player and the environment.
MNL-Bandit in non-stationary environments
In this paper, we study the MNL-Bandit problem in a non-stationary environment and present an algorithm with a worst-case expected regret of $\tilde{O}\left( \min \left\{ \sqrt{NTL}\;,\; N^{\frac{1}{3}}(\Delta_{\infty}^{K})^{\frac{1}{3}} T^{\frac{2}{3}} + \sqrt{NT}\right\}\right)$.
Interpretable Machine Learning for Resource Allocation with Application to Ventilator Triage
We propose a novel data-driven model to compute interpretable triage guidelines based on policies for Markov Decision Process that can be represented as simple sequences of decision trees ("tree policies").
Dynamic pricing and assortment under a contextual MNL demand
We consider dynamic multi-product pricing and assortment problems under an unknown demand over T periods, where in each period, the seller decides on the price for each product or the assortment of products to offer to a customer who chooses according to an unknown Multinomial Logit Model (MNL).
MNL-Bandit with Knapsacks: a near-optimal algorithm
The goal of the seller is to maximize the total expected revenue from the $T$ customers given the fixed initial inventory of $N$ products.
Robust Policies For Proactive ICU Transfers
In this work, we study the problem of finding \emph{robust} patient transfer policies which account for uncertainty in statistical estimates due to data limitations when optimizing to improve overall patient care.
A Generalized Markov Chain Model to Capture Dynamic Preferences and Choice Overload
Assortment optimization is an important problem that arises in many industries such as retailing and online advertising where the goal is to find a subset of products from a universe of substitutable products which maximize seller's expected revenue.
MNL-Bandit: A Dynamic Learning Approach to Assortment Selection
The retailer observes this choice and the objective is to dynamically learn the model parameters, while optimizing cumulative revenues over a selling horizon of length $T$.
Thompson Sampling for the MNL-Bandit
We consider a sequential subset selection problem under parameter uncertainty, where at each time step, the decision maker selects a subset of cardinality $K$ from $N$ possible items (arms), and observes a (bandit) feedback in the form of the index of one of the items in said subset, or none.
Assortment Optimization Under the Mallows model
We consider the assortment optimization problem when customer preferences follow a mixture of Mallows distributions.
