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.
QueryMarket: Cost-Aware Online Active Learning in Data Markets
arXiv:2606. 17805v1 Announce Type: new Abstract: Data acquisition is a major bottleneck for learning in real-time streams: analysts must decide on the fly which labels to purchase while respecting a rolling budget.
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arXiv Machine Learning
Sep 2
Prediction-Assisted Pricing and Admission for LLM APIs with Stochastic Token Consumption
arXiv:2609.00710v1 Announce Type: cross Abstract: An LLM application often sells or internally allocates several service products: a small or premium model, a short or long token cap, and possibly mu...
arXiv Machine Learning
Jul 14
How much Data do We Need? Sequential Data Collection for Stochastic Programming
arXiv:2607. 10207v1 Announce Type: cross Abstract: Data-driven optimization often requires collecting data to estimate uncertain model parameters before solving the underlying decision problem.
arXiv Machine Learning
Jul 22
JD-BP: A Joint-Decision Generative Framework for Auto-Bidding and Pricing
arXiv:2604. 05845v2 Announce Type: replace-cross Abstract: Auto-bidding services optimize real-time bidding strategies for advertisers under key performance indicator (KPI) constraints such as target return on investment and budget.
arXiv AI
Aug 5
Minimax-Optimal Semiparametric Contextual Dynamic Pricing with Multimodal Revenue
arXiv:2608. 03142v1 Announce Type: cross Abstract: We study contextual dynamic pricing with arbitrary covariate sequences and bounded, possibly nonbinary purchase quantities.
arXiv AI
Jul 28
Adaptive Data Admission and Retention for Streaming Federated Learning
arXiv:2607. 23987v1 Announce Type: cross Abstract: We study streaming federated learning with limited client memory, where newly generated training data incur time-varying sampling costs and must be selectively admitted and retained over time.
arXiv AI
Jul 21
TypiCore: A Hybrid Active Query Strategy for Class-Incremental Learning on Time Series
arXiv:2607. 17632v1 Announce Type: cross Abstract: Time series data play a pivotal role across numerous domains, including healthcare and manufacturing.
Hugging Face Trending Papers
Jul 27
Adaptive Data Admission and Retention for Streaming Federated Learning
We study streaming federated learning with limited client memory, where newly generated training data incur time-varying sampling costs and must be selectively admitted and retained over time. We consider a joint server-side admission and client-side memory-management framework with the objective of minimizing the cumulative excess population risk under a sampling-cost budget and buffer constraints.
arXiv AI
3d ago
Active Learning with Imperfect Labels: Optimal Labeler Assignment and Sample Selection
arXiv:2512.12870v2 Announce Type: replace-cross Abstract: Active Learning (AL) is commonly used in applications where labeling data is expensive or time-consuming. In practice, however, labels are of...
arXiv Machine Learning
Jul 2
Decision-Aware Training for Sample-Based Generative Models
arXiv:2607. 01171v1 Announce Type: new Abstract: Sample-based generative models are increasingly used for probabilistic forecasting in high-stakes decision settings, yet their training objectives are blind to the decision maker's cost structure.
arXiv Machine Learning
Jul 27
Online Pricing and Allocation with Demand Learning and Fulfillment Cost
arXiv:2501. 18049v3 Announce Type: replace Abstract: We study online learning for a seller that jointly chooses per-period inventory positions and a uniform price, then fulfills realized demand through a downstream allocation.
arXiv Machine Learning
Jun 25
Towards Continuous Power Forecasting: Practical Continual Learning for Real-World Energy Systems in Nonstationary Time Series
arXiv:2606. 24955v1 Announce Type: new Abstract: Power forecasting models deployed in real-world energy markets must operate under nonstationary conditions, where data distributions continually evolve due to weather variability, infrastructure upgrades, and changing consumption behaviors.