Nonparametric Learning and Optimization with Covariates

Chen, Ningyuan; Gallego, Guillermo

doi:10.2139/ssrn.3172697

Cited by 5 publications

(4 citation statements)

References 42 publications

(88 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dynamic pricing and learning with demand covariates (or contextual information) has received increasing attention in recent years because of its flexibility and clarity in modeling customers and market environment. Research involving this information include, among others, Chen et al (2015b), Qiang and Bayati (2016), Nambiar et al (2018), Ban and Keskin (2017), Lobel et al (2018), Chen and Gallego (2018), Javanmard and Nazerzadeh (2019). In many online-retailing applications, sellers have access to rich covariate information reflecting the current market situation.…”

Section: Literature Reviewmentioning

confidence: 99%

Context-Based Dynamic Pricing with Online Clustering

Miao¹,

Chen²,

Chao³

et al. 2019

Preprint

View full text Add to dashboard Cite

Authors are encouraged to submit new papers to INFORMS journals by means of a style file template, which includes the journal title. However, use of a template does not certify that the paper has been accepted for publication in the named journal. INFORMS journal templates are for the exclusive purpose of submitting to an INFORMS journal and should not be used to distribute the papers in print or online or to submit the papers to another publication.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Context-Based Dynamic Pricing with Online Clustering

Miao¹,

Chen²,

Chao³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Cohen et al (2016), Lobel et al (2018), and Leme and Schneider (2018) propose learning algorithms based on binary search methods when the context vector is chosen adversarially in each round. Chen and Gallego (2018) consider the problem where a learner observes contextual features and optimizes an objective by experimenting with a fixed set of decisions. They present a tree-based non-parametric learning policy that adaptively splits the feature space into smaller bins (hyper-rectangles), and eventually learns near-optimal decisions in each bin.…”

Section: Related Workmentioning

confidence: 99%

Incentive-aware Contextual Pricing with Non-parametric Market Noise

Golrezaei¹,

Jaillet²,

Liang³

2019

Preprint

View full text Add to dashboard Cite

We consider a dynamic pricing problem for repeated contextual second-price auctions with strategic buyers whose goals are to maximize their long-term time discounted utility. The seller has very limited information about buyers' overall demand curves, which depends on d-dimensional context vectors characterizing auctioned items, and a non-parametric market noise distribution that captures buyers' idiosyncratic tastes.The noise distribution and the relationship between the context vectors and buyers' demand curves are both unknown to the seller. We focus on designing the seller's learning policy to set contextual reserve prices where the seller's goal is to minimize his regret for revenue. We first propose a pricing policy when buyers are truthful and show that it achieves a T -period regret bound of Õ( √ dT ) against a clairvoyant policy that has full information of the buyers' demand. Next, under the setting where buyers bid strategically to maximize their long-term discounted utility, we develop a variant of our first policy that is robust to strategic (corrupted) bids. This policy incorporates randomized "isolation" periods, during which a buyer is randomly chosen to solely participate in the auction. We show that this design allows the seller to control the number of periods in which buyers significantly corrupt their bids. Because of this nice property, our robust policy enjoys a T -period regret of Õ( √ dT ), matching that under the truthful setting up to a constant factor that depends on the utility discount factor.

show abstract

“…That is, treat the contexts generated in the same bin equally. This idea is also adopted by Lu et al [2009], Rigollet and Zeevi [2010], Perchet and Rigollet [2013], Chen and Gallego [2018]. To find the optimal solution y * (x) when the context falls into a particular bin, we use stochastic approximation and the estimated gradient to find the maximum.…”

Section: Our Algorithmmentioning

confidence: 99%

“…In operations research, many papers have focused on dynamic pricing and demand learning [Besbes and Zeevi, 2009, 2015, Broder and Rusmevichientong, 2012, den Boer and Zwart, 2013, den Boer, 2015. Recently Chen and Gallego [2018] consider personalized pricing of a single product to customers. Besides Lipschitz continuity in arms and context space, they further assume smoothness and local concavity at the unique maximizer of the payoff function.…”

Section: Introductionmentioning

confidence: 99%

A Dimension-free Algorithm for Contextual Continuum-armed Bandits

Li¹,

Chen²,

Hong³

2019

Preprint

Self Cite

View full text Add to dashboard Cite

In contextual continuum-armed bandits, the contexts x and the arms y are both continuous and drawn from high-dimensional spaces. The payoff function to learn f (x, y) does not have a particular parametric form. The literature has shown that for Lipschitz-continuous functions, the optimal regret is Õ(T dx +dy +1 dx +dy +2 ), where d x and d y are the dimensions of contexts and arms, and thus suffers from the curse of dimensionality. We develop an algorithm that achieves regret Õ(T dx +1 dx +2 ) when f is globally concave in y. The global concavity is a common assumption in many applications. The algorithm is based on stochastic approximation and estimates the gradient information in an online fashion. Our results generate a valuable insight that the curse of dimensionality of the arms can be overcome with some mild structures of the payoff function.

show abstract

Nonparametric Learning and Optimization with Covariates

Cited by 5 publications

References 42 publications

Context-Based Dynamic Pricing with Online Clustering

Context-Based Dynamic Pricing with Online Clustering

Incentive-aware Contextual Pricing with Non-parametric Market Noise

A Dimension-free Algorithm for Contextual Continuum-armed Bandits

Contact Info

Product

Resources

About