Adaptive Model Routing

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7 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Learning-to-Defer in Non-Stationary Time Series via Switching State-Space Models
Jan 30, 2026Yannis Montreuil, Letian Yu, Axel Carlier +2Cost-Sensitive Learning Adaptive Model Routing
School of Computing National University of Singapore · Université de Toulouse Fédération ENAC ISAE-SUPAERO ONERA · Institute for Infocomm Research A*STAR, Singapore
FeRA: Frequency-Energy Constrained Routing for Effective Diffusion Adaptation Fine-Tuning
Nov 22, 2025Bo Yin, Xiaobin Hu, Xingyu Zhou +7Fine-Tuning Adaptive Model Routing
National University of Singapore · University of Electronic Science and Technology of China · Nanyang Technological University +4
UniShield: An Adaptive Multi-Agent Framework for Unified Forgery Image Detection and Localization
Oct 3, 2025Qing Huang, Zhipei Xu, Xuanyu Zhang +2Image Forgery Detection Adaptive Model Routing
School of Future Technology, South China University of Technology · School of Electronic and Computer Engineering, Peking University · School of Electronic and Information Engineering, South China University of Technology +1

Jan 30, 2026, cs.LGJ/K move · Enter open · S save

Learning-to-Defer in Non-Stationary Time Series via Switching State-Space Models

Yannis Montreuil, Letian Yu, Axel Carlier, Lai Xing Ng+1

School of Computing National University of Singapore · Université de Toulouse Fédération ENAC ISAE-SUPAERO ONERA · Institute for Infocomm Research A*STAR, Singapore

Cost-Sensitive Learning Adaptive Model Routing Learning to Defer Decision-Focused Learning Non-Stationary Time Series Forecasting State Space Models

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Learning-to-defer (L2D) lets a predictor decide, at each round, whether to issue its own forecast or pay for an expert's. In non-stationary time series this decision must keep adapting, although deployment reveals only the consulted expert's forecast while a historical archive records every expert with the target. L2D-SLDS learns from this archive a switching state-space model of the target and all expert forecasts, whose shared and expert-specific states describe how experts move together and apart. Its predictive law supplies the internal forecast and the expected cost of every consultation, which a greedy router minimizes, and one consultation also updates the beliefs about unconsulted and unavailable experts. We prove sublinear regret against a changing conditional-risk oracle without exploration, when the candidate models are accurate and either the archive separates them or live feedback reveals cost differences. On three real datasets, L2D-SLDS has the lowest cost among eight bandit routers and adapts its consultation rate to the fee.

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Latest papers 63

Learning-to-Defer in Non-Stationary Time Series via Switching State-Space Models

FeRA: Frequency-Energy Constrained Routing for Effective Diffusion Adaptation Fine-Tuning

UniShield: An Adaptive Multi-Agent Framework for Unified Forgery Image Detection and Localization