arXiv Machine Learning
Jul 14
From Confounding to Learning: Dynamic Service Fee Pricing on Third-Party Platforms
arXiv:2512. 22749v2 Announce Type: replace Abstract: We study the pricing behavior of third-party platforms facing strategic agents.
Prediction-Assisted Pricing and Admission for LLM APIs with Stochastic Token Consumption
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arXiv Machine Learning
Jul 28
On Non-Stationary Dynamic Pricing: Adaptivity and Optimality
arXiv:2607. 24115v1 Announce Type: cross Abstract: We study the contextual dynamic pricing problem under non-stationarity, where a firm sells products to $T$ sequentially arriving consumers that behave according to an unknown demand model that can change over time.
arXiv AI
Jun 17
Online LLM Selection via Constrained Bandits with Time-Varying Demand
arXiv:2606. 17489v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed in edge-cloud inference systems to handle diverse user tasks with heterogeneous accuracy, latency, and cost profiles.
arXiv Machine Learning
Aug 24
Adaptive Inference for Resource-Constrained Dynamic Pricing
arXiv:2606. 03736v2 Announce Type: replace-cross Abstract: We study resource-constrained dynamic pricing when the seller seeks revenue and valid inference about demand at a price fixed before the selling season.
arXiv AI
Sep 2
Drift-Aware LLM Routing with Sparse Contexts and Shared Budgets
The paper introduces Drift‑Aware Sparse Routing (DRS), a method for routing requests in a multi‑model language service while respecting compute, latency, memory, or cost budgets. DRS estimates reward and resource use from a rolling audit window, routes using pessimistic reward and optimistic cost estimates, updates resource shadow prices online, and applies a hard meter before commitment. The authors provide theoretical regret bounds that separate control from statistics, showing how the method adapts to non‑stationary prompt distributions and model changes.
arXiv Machine Learning
Sep 29
Dynamic Regret in Online Convex Optimization with Indicator Switching Costs
arXiv:2609. 30556v1 Announce Type: new Abstract: We study dynamic regret in online convex optimization with an \emph{indicator switching cost}: a fixed penalty incurred whenever two consecutive decisions differ.
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).
arXiv Statistics ML
Sep 16
Learning Choice Model Trees for Feature-Based Multi-Product Pricing: Exact Optimization and Field Evidence
arXiv:2609.16952v1 Announce Type: cross Abstract: Feature-based multi-product pricing uses customer characteristics to identify demand heterogeneity and tailor prices across products. Choice model tr...
arXiv Computation and Language
Sep 1
LLP: LLM-Based Product Pricing in E-commerce
The paper introduces LLP, a Large Language Model–based generative framework for pricing second‑hand products on consumer‑to‑consumer platforms. LLP retrieves similar items to capture market dynamics, then uses LLMs to generate price suggestions, refined through supervised fine‑tuning and group relative policy optimization. A confidence‑based filter rejects unreliable predictions, and experiments show LLP outperforms prior methods, achieving higher static adoption rates when deployed on Xianyu.
arXiv Machine Learning
Jul 17
The Challenger: When Do New Data Sources Justify Switching Machine Learning Models?
arXiv:2512. 18390v2 Announce Type: replace Abstract: Organizations often have an incumbent predictive model in production when new data sources become available.
arXiv AI
Jun 10
A Theory of Training Profit-Optimal LLMs
arXiv:2605. 16430v2 Announce Type: replace-cross Abstract: Scaling LLMs requires tremendous computational resources, and recent advances in AI have gone hand in hand with massive amounts of capital expenditure.
arXiv Machine Learning
Aug 13
Reoptimization Algorithms for Contextual Bandits with Knapsack Constraints
arXiv:2608. 11383v1 Announce Type: new Abstract: We study new algorithms for Contextual Bandits with Knapsack.