Cover Image

Learning After Model Deployment

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kaymak, Derda, Kim, Gyuhak, Kaichi, Tomoya, Konishi, Tatsuya, Liu, Bing
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Open-World Continual Learning: Unifying Novelty Detection and Continual Learning
by: Kim, Gyuhak, et al.
Published: (2023)

CADE: Continual Weakly-supervised Video Anomaly Detection with Ensembles
by: Hashimoto, Satoshi, et al.
Published: (2025)

SFT-GO: Supervised Fine-Tuning with Group Optimization for Large Language Models
by: Kim, Gyuhak, et al.
Published: (2025)

Comprehensive Deployment-Oriented Assessment for Cross-Environment Generalization in Deep Learning-Based mmWave Radar Sensing
by: Tanaka, Tomoya, et al.
Published: (2025)

Multi-objective Reinforcement Learning With Augmented States Requires Rewards After Deployment
by: Vamplew, Peter, et al.
Published: (2026)

Batch Acquisition Function Evaluations and Decouple Optimizer Updates for Faster Bayesian Optimization
by: Irie, Kaichi, et al.
Published: (2025)

Interpretable Fuzzy Systems For Forward Osmosis Desalination
by: Khaled, Qusai, et al.
Published: (2026)

In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning
by: Wakayama, Tomoya, et al.
Published: (2025)

Deploying Large AI Models on Resource-Limited Devices with Split Federated Learning
by: Qiang, Xianke, et al.
Published: (2025)

Learned Neighbor Trust for Collaborative Deployment in Model-Agnostic Decentralized Learning
by: Lanier, Michael, et al.
Published: (2026)

On-Demand Model and Client Deployment in Federated Learning with Deep Reinforcement Learning
by: Chahoud, Mario, et al.
Published: (2024)

Explainable Uncertainty Quantification for Wastewater Treatment Energy Prediction via Interval Type-2 Neuro-Fuzzy System
by: Khaled, Qusai, et al.
Published: (2026)

Explorative Curriculum Learning for Strongly Correlated Electron Systems
by: Yamazaki, Kimihiro, et al.
Published: (2025)

Learning from Similarity/Dissimilarity and Pairwise Comparison
by: Tate, Tomoya, et al.
Published: (2026)

Bayesian Inference for Consistent Predictions in Overparameterized Nonlinear Regression
by: Wakayama, Tomoya
Published: (2024)

Automated Reinforcement Learning: An Overview
by: Afshar, Reza Refaei, et al.
Published: (2022)

Learning Functional Graphs with Nonlinear Sufficient Dimension Reduction
by: Kim, Kyongwon, et al.
Published: (2026)

Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learning
by: Osa, Takayuki, et al.
Published: (2024)

Learning Koopman operators for coupled systems via information on governing equations of subsystems
by: Naoi, Tatsuya, et al.
Published: (2026)

Reinforcement Learning for Machine Learning Model Deployment: Evaluating Multi-Armed Bandits in ML Ops Environments
by: McClendon, S. Aaron, et al.
Published: (2025)

Strategically Deceptive Model Deployment in Performative Prediction
by: Bautiste, Javier Sanguino, et al.
Published: (2025)

Learning the Simplest Neural ODE
by: Okamoto, Yuji, et al.
Published: (2025)

Bayesian Analysis for Over-parameterized Linear Model via Effective Spectra
by: Wakayama, Tomoya, et al.
Published: (2023)

SLMQuant:Benchmarking Small Language Model Quantization for Practical Deployment
by: Wang, Jiacheng, et al.
Published: (2025)

Achieving Upper Bound Accuracy of Joint Training in Continual Learning
by: Momeni, Saleh, et al.
Published: (2025)

Automating the Training and Deployment of Models in MLOps by Integrating Systems with Machine Learning
by: Liang, Penghao, et al.
Published: (2024)

Reliably Detecting Model Failures in Deployment Without Labels
by: Nguyen, Viet, et al.
Published: (2025)

Models Know Their Shortcuts: Deployment-Time Shortcut Mitigation
by: Li, Jiayi, et al.
Published: (2026)

BitRL: Reinforcement Learning with 1-bit Quantized Language Models for Resource-Constrained Edge Deployment
by: Sajid, Md. Ashiq Ul Islam, et al.
Published: (2026)

AutoTailor: Automatic and Efficient Adaptive Model Deployment for Diverse Edge Devices
by: Liu, Mengyang, et al.
Published: (2025)

Comparative Field Deployment of Reinforcement Learning and Model Predictive Control for Residential HVAC
by: Mulayim, Ozan Baris, et al.
Published: (2025)

How to Evaluate Participant Contributions in Decentralized Federated Learning
by: Anada, Honoka, et al.
Published: (2025)

Inferring Data Preconditions from Deep Learning Models for Trustworthy Prediction in Deployment
by: Ahmed, Shibbir, et al.
Published: (2024)

ML2SC: Deploying Machine Learning Models as Smart Contracts on the Blockchain
by: Li, Zhikai, et al.
Published: (2024)

Deployment-complete benchmarking
by: Mansouri, El Mustapha, et al.
Published: (2026)

Ensemble Prediction via Covariate-dependent Stacking
by: Wakayama, Tomoya, et al.
Published: (2024)

Frontiers of Deep Learning: From Novel Application to Real-World Deployment
by: Xie, Rui
Published: (2024)

Bridging the Resource Gap: Deploying Advanced Imitation Learning Models onto Affordable Embedded Platforms
by: Ge, Haizhou, et al.
Published: (2024)

Rescaling-Aware Training for Efficient Deployment of Deep Learning Models on Full-Integer Hardware
by: Mueller, Lion, et al.
Published: (2025)

Quantum Tunneling-Aware Machine Learning: Physics-Derived Noise Models for Robust Deployment
by: Hwang, Uiwon, et al.
Published: (2026)