arXiv Machine Learning August 6, 2026 By Mohammadsaeed Haghi, Mahdi Salmani, Nima Kelidari

Differentiating Through Dual Prices: End-to-End Policy Learning Under Capacity Constraints

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arXiv:2608. 04669v1 Announce Type: new Abstract: Many social services assign scarce resources, such as housing assistance or hospital interventions, to people who arrive one at a time: each arrival must receive a decision immediately, and the long-run usage of every resource must stay within its capacity.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 1

Qualified Educational Capacity Planning under Heterogeneous Student Support Needs: A Synthetic Benchmark and Decision-Support Framework

arXiv:2606. 30650v1 Announce Type: cross Abstract: Educational support services often face a qualified-capacity problem: staff time is scarce, qualifications decay, new support needs can appear before anyone is prepared for them, and training consumes the same hours needed by current students.

By Carlos Eduardo Sanoja, Oscar Enrique Moreno Mayz

benchmarks safety

arXiv Machine Learning

Aug 14

Chance-constrained selection of sequential intervention strategies from counterfactual estimates

arXiv:2608. 13209v1 Announce Type: cross Abstract: Many operational decisions are sequences of interventions under a cumulative resource limit, such as a maintenance schedule within a crew-hour budget.

By Minkyoung Kim, Beakcheol Jang

safety

arXiv AI

Jul 14

Learning to Fine-tune Foundation Models under Resource Limitations

arXiv:2607. 10694v1 Announce Type: cross Abstract: We study the problem of optimal continual fine-tuning for a pre-trained Foundation Model deployed at a resource-limited device.

By Thomas Tsouparopoulos, Iordanis Koutsopoulos

nlp reinforcement-learning fine-tuning

arXiv Machine Learning

Aug 10

Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning

arXiv:2608. 07157v1 Announce Type: new Abstract: Sub-model federated learning lets resource-constrained clients train width-reduced versions of a global model, but existing methods allocate capacity by device resources alone.

By Alireza Moayedikia, Alicia Troncoso Lora

benchmarks

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...

By Patrick Wong

llms benchmarks

arXiv Machine Learning

Aug 26

Bellman Calibration for Marginalized Importance Weighting in Offline Reinforcement Learning

arXiv:2608.24858v1 Announce Type: new Abstract: Marginalized importance weighting evaluates a target policy by reweighting offline state-action samples with its discounted occupancy ratio, characteri...

By Lars van der Laan, Nathan Kallus

reinforcement-learning

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