arXiv Machine Learning August 3, 2026 By Hasan Burhan Beytur, Haris Vikalo, Kevin S Chan, Gustavo de Veciana

Optimal Resource Allocation for ML Model Training and Deployment under Concept Drift

Read the original on arXiv Machine Learning →

arXiv:2512. 12816v2 Announce Type: replace Abstract: We study how to allocate resources for training and deployment of machine learning (ML) models under concept drift and limited budgets.

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 8

To Retain or to Adapt? Generalizing Continual Learning

arXiv:2607. 05609v1 Announce Type: cross Abstract: The Continual Learning (CL) literature has long been driven by the goal of mitigating catastrophic forgetting.

By Giulia Lanzillotta, Mandana Samiei, Doina Precup, Razvan Pascanu, Claire Vernade

computer-vision reinforcement-learning benchmarks 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 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.

By Yin Huang, Qingsong Liu, Jie Xu

llms reinforcement-learning benchmarks

arXiv AI

Aug 26

Anytime Pretraining: Horizon-Free Learning-Rate Schedules with Weight Averaging

arXiv:2602.03702v2 Announce Type: replace-cross Abstract: Large language models are increasingly trained in continual or open-ended settings, where the total training horizon is not known in advance....

By Alexandru Meterez, Pranav Ajit Nair, Depen Morwani, Cengiz Pehlevan, Sham Kakade

llms

arXiv Machine Learning

Jul 16

From Novice to Expert: Cost-Aware Bandits for Evolving Worker Performance in Crowdsensing

arXiv:2607. 13546v1 Announce Type: new Abstract: Mobile crowdsensing (MC) recruits mobile users to perform sensing tasks using their smartphones, enabling large-scale applications such as traffic monitoring and environmental sensing.

By Yin Huang, Qingsong Liu, Jie Xu

reinforcement-learning

arXiv AI

Aug 7

Continual Learning in Transition

arXiv:2608. 06216v1 Announce Type: cross Abstract: Classical continual learning (CL) has primarily focused on enabling models to update and retain knowledge through parameter-centric mechanisms, e.

By Zhiyan Hou, Dan Zhang, Tao Feng, Liyuan Wang, Wei Li, Xiangzhao Hao, Hongyan An, Junfeng Fang, Haokai Ma, Zhaohui Xu, Haiyun Guo, Jinqiao Wang, Tat-Seng Chua

One email a morning, machine-written

Related stories

To Retain or to Adapt? Generalizing Continual Learning

Learning to Fine-tune Foundation Models under Resource Limitations

Online LLM Selection via Constrained Bandits with Time-Varying Demand

Anytime Pretraining: Horizon-Free Learning-Rate Schedules with Weight Averaging

From Novice to Expert: Cost-Aware Bandits for Evolving Worker Performance in Crowdsensing

Continual Learning in Transition