arXiv research

New algorithms minimize risk in MNL bandits, achieving near-optimal performance.

Study MNL-Bandit in non-stationary settings with optimal regret bound.

The paper achieves nearly optimal regret bounds for contextual multinomial logit bandits.

Two algorithms optimize assortment selection for user choices in unknown MNL models.

Improved online confidence bounds for multinomial logistic models in bandits.

Optimal design for multinomial logit models improves assortment selection efficiency.

New algorithm tackles non-linear utility in MNL bandits with $ilde{O}(\sqrt{T})$ regret.

In this short note we consider a dynamic assortment planning problem under the capacitated multinomial logit (MNL) bandit model. We prove a tight lower bound on the accumulated regret that matches existing regret upper bounds for all parameters (time horizon $T$, number of items $N$ and maximum assortment capacity $K$)…

DMNL bandits optimize assortment choices balancing relevance and diversity.

Two algorithms achieve optimal regret with limited adaptivity in multinomial logistic bandits.

Multinomial logit bandit is a sequential subset selection problem which arises in many applications. In each round, the player selects a $K$-cardinality subset from $N$ candidate items, and receives a reward which is governed by a {\it multinomial logit} (MNL) choice model considering both item utility and substitution…

New algorithm for maximizing revenue in multinomial logistic bandits.

Improved regret bound for MNL MDPs with variance-aware approach.

Paper tackles combinatorial reinforcement learning with preference feedback.

New model optimizes assortment and pricing with dynamic customer arrivals.

Paper presents a privacy-preserving method for dynamic assortment selection.

New algorithm reduces regret in dynamic assortment selection.

The paper tackles learning mixtures of two multinomial logits, showing identifiability and presenting an algorithm.

We study the dynamic assortment planning problem, where for each arriving customer, the seller offers an assortment of substitutable products and customer makes the purchase among offered products according to an uncapacitated multinomial logit (MNL) model. Since all the utility parameters of MNL are unknown, the selle…

New algorithms reduce regret in reinforcement learning with MNL approximations.

New algorithms reduce matching regret by limiting frequent updates.

We present a mixed multinomial logit (MNL) model, which leverages the truncated stick-breaking process representation of the Dirichlet process as a flexible nonparametric mixing distribution. The proposed model is a Dirichlet process mixture model and accommodates discrete representations of heterogeneity, like a laten…

This paper refines the weighted strategy for non-stationary parametric bandits and MDPs, improving regret bounds.

New algorithms learn MNL weights efficiently for any slate size.

Motivated by generating personalized recommendations using ordinal (or preference) data, we study the question of learning a mixture of MultiNomial Logit (MNL) model, a parameterized class of distributions over permutations, from partial ordinal or preference data (e.g. pair-wise comparisons). Despite its long standing…

The geometric non-linear Schrodinger equation (GNLS) on the complex Grassmannian manifold M is the Hamiltonian equation for the energy functional on C(R,M) with respect to the symplectic form induced from the Kahler form on M. It has a Lax pair that is gauge equivalent to the Lax pair of the matrix non-linear Schroding…

Study optimal product assortment using historical data, proving item coverage suffices.

The paper models network formation using mixed logit models.

New algorithm reduces reinforcement learning regret by adapting to interaction variability.

Travel providers such as airlines and on-line travel agents are becoming more and more interested in understanding how passengers choose among alternative itineraries when searching for flights. This knowledge helps them better display and adapt their offer, taking into account market conditions and customer needs. Som…

When tracking user-specific online activities, each user's preference is revealed in the form of choices and comparisons. For example, a user's purchase history is a record of her choices, i.e. which item was chosen among a subset of offerings. A user's preferences can be observed either explicitly as in movie ratings …

Algorithm stabilizes queues in asymmetric systems with unknown service rates.

In discrete choice modeling (DCM), model misspecifications may lead to limited predictability and biased parameter estimates. In this paper, we propose a new approach for estimating choice models in which we divide the systematic part of the utility specification into (i) a knowledge-driven part, and (ii) a data-driven…

CRS model improves ranking data modeling with theoretical guarantees.

New algorithm tackles dynamic assortment optimization with knapsack constraints.

This paper explores the adaptive (active) PAC (probably approximately correct) top-$k$ ranking (i.e., top-$k$ item selection) and total ranking problems from $l$-wise ($l\geq 2$) comparisons under the multinomial logit (MNL) model. By adaptively choosing sets to query and observing the noisy output of the most favored …

Study dynamic assortment and positioning of products with varying display effects.

New model improves website ranking by considering user choices as a whole.

Optimizes assortment decisions with a new OFU scheme for online choice problems.

In this paper, we study the dynamic assortment optimization problem under a finite selling season of length $T$. At each time period, the seller offers an arriving customer an assortment of substitutable products under a cardinality constraint, and the customer makes the purchase among offered products according to a d…

The question of aggregating pair-wise comparisons to obtain a global ranking over a collection of objects has been of interest for a very long time: be it ranking of online gamers (e.g. MSR's TrueSkill system) and chess players, aggregating social opinions, or deciding which product to sell based on transactions. In mo…

The Multinomial Logit (MNL) model and the axiom it satisfies, the Independence of Irrelevant Alternatives (IIA), are together the most widely used tools of discrete choice. The MNL model serves as the workhorse model for a variety of fields, but is also widely criticized, with a large body of experimental literature cl…

New algorithms tackle RKHS bandits with reduced complexity and improved performance.

Unified formulation bridges adversarial and nonstationary bandits.

As datasets capturing human choices grow in richness and scale -- particularly in online domains -- there is an increasing need for choice models that escape traditional choice-theoretic axioms such as regularity, stochastic transitivity, and Luce's choice axiom. In this work we introduce the Pairwise Choice Markov Cha…

Paper tackles LDP bandits learning with improved results and sub-linear regret.

Paper solves stochastic contextual linear bandits using linear bandit algorithms.

Graph-Triggered Bandits unify rested and restless bandits with graph-defined arm interactions.