Adaptive Inference

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7 papers in the last four weeks, up 133% on the four weeks before. 0.1% of all new papers.

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Posterior-driven Heuristic Support Adaptation in a Probabilistic Treatment of Real2Sim2Real for Vision-Driven Deformable Linear Object Manipulation
Oct 30, 2025Georgios Kamaras, Craig Innes, Subramanian RamamoorthySimulation-Based Inference Adaptive Inference
School of Informatics, The University of Edinburgh, EH8 9AB, UK. · Heriot-Watt University · UKRI Turing AI World Leading Researcher Fellowship on AI for Person-Centred and Teachable Autonomy
PERTINENCE: Input-based Opportunistic Neural Network Dynamic Execution
Jul 2, 2025Omkar Shende, Gayathri Ananthanarayanan, Marcello TraiolaEfficient Neural Network Inference Adaptive Inference
Department of Computer Science and Engineering, Indian Institute of Technology Dharwad, Karnataka, India · University of Rennes, CNRS, Inria, IRISA, Rennes, France
No Free Lunch: Non-Asymptotic Analysis of Prediction-Powered Inference
May 26, 2025Pranav Mani, Peng Xu, Zachary C. Lipton +1Adaptive Inference Statistical Learning Theory
Abridge AI, San Francisco, CA, USA · Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA · Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA

Oct 30, 2025, cs.ROJ/K move · Enter open · S save

Posterior-driven Heuristic Support Adaptation in a Probabilistic Treatment of Real2Sim2Real for Vision-Driven Deformable Linear Object Manipulation

Georgios Kamaras, Craig Innes, Subramanian Ramamoorthy

School of Informatics, The University of Edinburgh, EH8 9AB, UK. · Heriot-Watt University · UKRI Turing AI World Leading Researcher Fellowship on AI for Person-Centred and Teachable Autonomy

Simulation-Based Inference Adaptive Inference Deformable Object Manipulation Visuomotor Policy Learning

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Likelihood-free inference (LFI) enables system identification in complex tasks via black-box modelling, abstracting nonlinearity and stochasticity, and infers a domain distribution for adapting agents to parametric deployment conditions. LFI assumes an arbitrary support for sampling, which remains fixed as the initial generic prior is refined to increasingly descriptive posteriors. Misspecified support can therefore yield suboptimal yet overconfident posteriors. We address this issue by using the posterior of an inference step to guide the adaptation of the support using three illustrative heuristics: EDGE, MODE, and CENTRE. Each heuristic interprets the updated belief and enables support adaptation alongside posterior inference. For illustrative purposes, we first study misspecified support in LFI and evaluate the utility of our heuristics using stochastic dynamical benchmarks. We then evaluate posterior-driven heuristic support adaptation for parameter inference and policy learning in a dynamic deformable linear object (DLO) manipulation task. Inference results in a finer length and stiffness classification for a parametric set of DLOs. When the resulting posteriors are used as domain distributions for sim-based policy learning, they lead to more robust object-centric agent performance.

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Latest papers 63

Posterior-driven Heuristic Support Adaptation in a Probabilistic Treatment of Real2Sim2Real for Vision-Driven Deformable Linear Object Manipulation

PERTINENCE: Input-based Opportunistic Neural Network Dynamic Execution

No Free Lunch: Non-Asymptotic Analysis of Prediction-Powered Inference