Improvise, Adapt, Overcome: An On-The-Fly Multifidelity Algorithm for Efficient Machine Learning

• URL: http://arxiv.org/abs/2606.02662v1
• Date: Mon, 01 Jun 2026 08:26:24 GMT
• Title: Improvise, Adapt, Overcome: An On-The-Fly Multifidelity Algorithm for Efficient Machine Learning
• Abstract summary: Multifidelity machine learning (MFML) combines abundant low fidelity data with sparse high fidelity data.<n>Standard MFML schemes rely on pre-defined scaling factors to determine sparse data ratio across fidelities.<n>Here, we introduce an adaptive on-the-fly multifidelity framework for machine learning that autonomously determines training dataset composition.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning has accelerated quantum chemistry but is hindered by the prohibitive cost of generating high fidelity training data. Multifidelity machine learning (MFML) mitigates this overhead by systematically combining abundant low fidelity data with sparse high fidelity data. In spite of its success, standard MFML schemes rely on pre-defined scaling factors to determine sparse data ratio across fidelities, often generating redundant multifidelity data resulting in a loss of efficiency. Here, we introduce an adaptive on-the-fly multifidelity framework for machine learning that autonomously determines training dataset composition. By dynamically querying training samples at each fidelity, the algorithm saturates model accuracy at lower fidelities before moving up to more expensive reference calculations. We benchmark the novel adaptive-MFML across diverse chemical properties including the computational chemistry gold standard coupled cluster energies, and the more chemically challenging excitation energies. In our numerical experiments we show that our adaptive algorithm reduces data generation costs by up to a factor of 30 compared to single fidelity methods and improves upon standard MFML by up to a factor of 5. The mitigation of data redundancy establishes a high-accuracy low-cost pathway for sustainable cost-aware machine learning in quantum chemistry.

Related papers

• Consolidation or Adaptation? PRISM: Disentangling SFT and RL Data via Gradient Concentration [56.074760766965085]
  PRISM achieves a dynamics-aware framework that arbitrates data based on its degree of cognitive conflict with the model's existing knowledge.<n>Our findings suggest that disentangling data based on internal optimization regimes is crucial for scalable and robust agent alignment.
  arXiv  Detail & Related papers  (2026-01-12T05:43:20Z)
• Optimizing Pretraining Data Mixtures with LLM-Estimated Utility [52.08428597962423]
  Large Language Models improve with increasing amounts of high-quality training data.<n>We find token-counts outperform manual and learned mixes, indicating that simple approaches for dataset size and diversity are surprisingly effective.<n>We propose two complementary approaches: UtiliMax, which extends token-based $200s by incorporating utility estimates from reduced-scale ablations, achieving up to a 10.6x speedup over manual baselines; and Model Estimated Data Utility (MEDU), which leverages LLMs to estimate data utility from small samples, matching ablation-based performance while reducing computational requirements by $simx.
  arXiv  Detail & Related papers  (2025-01-20T21:10:22Z)
• Learning for Cross-Layer Resource Allocation in MEC-Aided Cell-Free Networks [71.30914500714262]
  Cross-layer resource allocation over mobile edge computing (MEC)-aided cell-free networks can sufficiently exploit the transmitting and computing resources to promote the data rate.<n>Joint subcarrier allocation and beamforming optimization are investigated for the MEC-aided cell-free network from the perspective of deep learning.
  arXiv  Detail & Related papers  (2024-12-21T10:18:55Z)
• Implicit Delta Learning of High Fidelity Neural Network Potentials [1.135672229709142]
  Implicit Delta Learning (IDLe) method reduces the need for high-fidelity Quantum Mechanics (QM) data.<n>IDLe achieves the same accuracy as single high-fidelity baselines while using up to 50x less high-fidelity data.
  arXiv  Detail & Related papers  (2024-12-08T20:35:45Z)
• Investigating Data Hierarchies in Multifidelity Machine Learning for Excitation Energies [0.0]
  This study investigates the impact of modifying $gamma$ on model efficiency and accuracy for the prediction of vertical excitation energies using the QeMFi benchmark dataset.<n>A novel error metric, error contours of MFML, is proposed to provide a comprehensive view of model error contributions from each fidelity.<n>The results indicate that high model accuracy can be achieved with just 2 training samples at the target fidelity when a larger number of samples from lower fidelities are used.
  arXiv  Detail & Related papers  (2024-10-15T08:35:00Z)
• Benchmarking Data Efficiency in $Δ$-ML and Multifidelity Models for Quantum Chemistry [0.0]
  This work compares the data costs associated with $Delta$-ML, multifidelity machine learning (MFML), and optimized MFML (o-MFML)<n>The results indicate that the use of multifidelity methods surpasses the standard $Delta$-ML approaches in cases of a large number of predictions.
  arXiv  Detail & Related papers  (2024-10-15T08:34:32Z)
• Assessing Non-Nested Configurations of Multifidelity Machine Learning for Quantum-Chemical Properties [0.0]
  Multifidelity machine learning (MFML) for quantum chemical (QC) properties has seen strong development in the recent years. This work assesses the use of non-nested training data for two of these multifidelity methods, namely MFML and optimized MFML.
  arXiv  Detail & Related papers  (2024-07-24T08:34:08Z)
• Multifidelity linear regression for scientific machine learning from scarce data [0.0]
  We propose a new multifidelity training approach for scientific machine learning via linear regression. We provide bias and variance analysis of our new estimators that guarantee the approach's accuracy and improved robustness to scarce high-fidelity data.
  arXiv  Detail & Related papers  (2024-03-13T15:40:17Z)
• Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices [72.61177465035031]
  We propose a generative AI-empowered federated learning to address these challenges by leveraging the idea of FIlling the MIssing (FIMI) portion of local data. Experiment results demonstrate that FIMI can save up to 50% of the device-side energy to achieve the target global test accuracy.
  arXiv  Detail & Related papers  (2023-10-21T12:07:04Z)
• Multi-fidelity Hierarchical Neural Processes [79.0284780825048]
  Multi-fidelity surrogate modeling reduces the computational cost by fusing different simulation outputs. We propose Multi-fidelity Hierarchical Neural Processes (MF-HNP), a unified neural latent variable model for multi-fidelity surrogate modeling. We evaluate MF-HNP on epidemiology and climate modeling tasks, achieving competitive performance in terms of accuracy and uncertainty estimation.
  arXiv  Detail & Related papers  (2022-06-10T04:54:13Z)
• Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning [89.31889875864599]
  We propose an efficient model-based reinforcement learning algorithm for learning in multi-agent systems. Our main theoretical contributions are the first general regret bounds for model-based reinforcement learning for MFC. We provide a practical parametrization of the core optimization problem.
  arXiv  Detail & Related papers  (2021-07-08T18:01:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.