← Back to all news
arXiv Machine Learning

Source-Aware Dynamic Pricing with Strategic Buyers

Related stories

arXiv Machine Learning
Jun 3

Human-in-the-Loop Contextual Bandits for Short-Term Rental Dynamic Pricing: Structural Equivalence of Historical Warm-Up and Approval-Gated Live Learning

arXiv:2606. 02595v1 Announce Type: new Abstract: Dynamic pricing in short-term rental (STR) markets presents a distinctive challenge for online learning algorithms: pricing decisions carry significant financial risk, operators require explainability, and market feedback is sparse (one booking outcome per listed night).

By Oleg Miroshnichenko
agentsreinforcement-learning
More like this →
arXiv Machine Learning
2d ago

Doubly Robust Distributionally Robust Offline Contextual Pricing

The paper introduces a doubly robust framework for distributionally robust offline contextual pricing, addressing challenges of distributional shifts and continuous actions. It proposes a localized DR estimator that is computationally efficient and semiparametrically optimal, and a learning algorithm that exploits demand noise smoothness to achieve finite‑sample regret bounds of τ^{-σ/(2σ+1)} for smoothness orders s=1,2. Experiments across varying distribution shifts demonstrate the effectiveness of the proposed methods.

By Min Xu, Xinyi Yin, Yunfan Zhang, Yuxuan Han, Houcai Shen, Caihua Chen
More like this →
arXiv AI
Oct 2

Learning to Sell: Reinforcement Learning for Strategic Large Language Model Agents in Multi-Product Markets

The paper presents a reinforcement learning framework for training autonomous large language model agents to act as sellers in multi-product bargaining markets. It models the problem as a Partially Observable Markov Decision Process and introduces a structured message protocol to map natural language into a regulated decision space. Using Reinforcement Learning from Verifiable Rewards, the trained agents match limited inventory to buyers more effectively, achieving seller surplus extraction and allocation quality comparable to or better than trillion‑parameter models, and generalize to unseen market conditions.

By Shuze Daniel Liu, Claire Chen, Jiuqi Wang, David Simchi-Levi, Thorsten Joachims
llmsagentsreinforcement-learning
More like this →
arXiv Machine Learning
Aug 18

Learning to Price with Persuasion

arXiv:2608. 16699v1 Announce Type: cross Abstract: Motivated by modern marketplaces, where the platform or the seller routinely gathers detailed user profiles, we study a novel learning theoretic model that simultaneously involves information and mechanism design.

By Maria-Florina Balcan, Tejas Pagare, Karan Singh
More like this →
arXiv Machine Learning
Sep 10

High-dimensional Linear Bandits with Knapsacks

The paper studies high‑dimensional linear contextual bandits with knapsack constraints (CBwK), aiming to exploit sparsity for tighter regret bounds. It introduces an online hard‑thresholding estimator integrated into a primal‑dual framework, achieving sub‑linear regret that grows only logarithmically with the feature dimension. Under either a diverse‑covariate or margin condition, the regret improves to τ‑dependent rates, and when both hold simultaneously, a dual resolving scheme yields an even tighter bound. The approach also recovers optimal rates for high‑dimensional contextual bandits without knapsacks, and experiments demonstrate its practical effectiveness.

By Wanteng Ma, Dong Xia, Jiashuo Jiang
efficiency
More like this →