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.
Optimal Resource Allocation for ML Model Training and Deployment under Concept Drift
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.
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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.
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
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.
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.
Hugging Face Trending Papers
Jul 15
From Novice to Expert: Cost-Aware Bandits for Evolving Worker Performance in Crowdsensing
Mobile crowdsensing (MC) recruits mobile users to perform sensing tasks using their smartphones, enabling large-scale applications such as traffic monitoring and environmental sensing. A fundamental challenge is online worker recruitment under uncertainty, where the platform must learn workers' sensing performance while operating with a limited budget.
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 Machine Learning
Jun 30
DRIFT: A Benchmark for Task-Free Continual Graph Learning with Continuous Distribution Shifts
arXiv:2605. 12998v3 Announce Type: replace Abstract: Continual graph learning (CGL) aims to learn from dynamically evolving graphs while mitigating catastrophic forgetting.
arXiv Machine Learning
Aug 4
T-TAMER: Provably Taming Trade-offs in ML Serving
arXiv:2509. 22992v2 Announce Type: replace Abstract: As machine learning models continue to grow in size and complexity, efficient serving faces increasingly broad trade-offs spanning accuracy, latency, resource usage, and other objectives.
arXiv Machine Learning
Jun 9
Partially Performative Prediction
arXiv:2606. 07890v1 Announce Type: new Abstract: Performative prediction studies feedback loops that arise when predictive models are deployed in consequential domains.
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.
arXiv AI
Jun 8
Just-In-Time Reinforcement Learning: Continual Learning in LLM Agents Without Gradient Updates
arXiv:2601. 18510v2 Announce Type: replace-cross Abstract: While Large Language Model (LLM) agents excel at general tasks, they inherently struggle with continual adaptation due to the frozen weights after deployment.