mlsanityData to 14 Oct 2026

Learning new physics efficiently with nonparametric methods

arXiv 2204.02317 ↗First posted 5 Apr 2022hep-ph cs.LG hep-excited by 21

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Abstract (arXiv)

We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any continuous function given enough data. Based on the original proposal by D'Agnolo and Wulzer (arXiv:1806.02350), the model evaluates the compatibility between experimental data and a reference model, by implementing a hypothesis testing procedure based on the likelihood ratio. Model-independence is enforced by avoiding any prior assumption about the presence or shape of new physics components in the measurements. We show that our approach has dramatic advantages compared to neural network implementations in terms of training times and computational resources, while maintaining comparable performances. In particular, we conduct our tests on higher dimensional datasets, a step forward with respect to previous studies.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 17 of its 65 references that are in the map. First posted on arXiv 5 Apr 2022.

Builds on

17 of 65 references.

References · 17

  1. 2016Generalization Properties of Learning with Random Featurescited by 164
  2. 2015Less is More: Nystr"om Computational Regularizationcited by 91
  3. 2020Kernel methods through the roof: handling billions of points efficientlycited by 89
  4. 2017FALKON: An Optimal Large Scale Kernel Methodcited by 84
  5. 2020Anomaly Detection with Density Estimationcited by 72
  6. 2018Variational Autoencoders for New Physics Mining at the Large Hadron Collidercited by 41
  7. 2020Variational Autoencoders for Anomalous Jet Taggingcited by 33
  8. 2021Autoencoders for unsupervised anomaly detection in high energy physicscited by 32
  9. 2021The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collidercited by 29
  10. 2018But How Does It Work in Theory? Linear SVM with Random Featurescited by 28
  11. 2019Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordancecited by 28
  12. 2020Simulation Assisted Likelihood-free Anomaly Detectioncited by 27
5 more
  1. 2018Novelty Detection Meets Collider Physicscited by 20
  2. 2021Bump Hunting in Latent Spacecited by 19
  3. 2019Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Lossescited by 17
  4. 2020Anomaly Awarenesscited by 10
  5. 2019Statistical and Computational Trade-Offs in Kernel K-Meanscited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 6; 2024: 2; 2025: 7; 2026: 3.

21 papers.

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  1. 2023CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimationcited by 10
  2. 2023Goodness of fit by Neyman-Pearson testingcited by 10
  3. 2023Improving new physics searches with diffusion models for event observables and jet constituentscited by 10
  4. 2023Robust, randomized preconditioning for kernel ridge regressioncited by 9
  5. 2024Refereeing the Referees: Evaluating Two-Sample Tests for Validating Generators in Precision Sciencescited by 9
  6. 2023Fast kernel methods for Data Quality Monitoring as a goodness-of-fit testcited by 8
  7. 2022Null Hypothesis Test for Anomaly Detectioncited by 7
  8. 2024Multiple testing for signal-agnostic searches of new physics with machine learningcited by 7
  9. 2025A Scalable Nystrom-Based Kernel Two-Sample Test with Permutationscited by 6
  10. 2023Comparison of Affine and Rational Quadratic Spline Coupling and Autoregressive Flows through Robust Statistical Testscited by 5
  11. 2022Resonant Anomaly Detection with Multiple Reference Datasetscited by 4
  12. 2025Comparing Generative Models with the New Physics Learning Machinecited by 4
9 more
  1. 2025Learning to Validate Generative Models: a Goodness-of-Fit Approachcited by 4
  2. 2022Machine-Learned Exclusion Limits without Binningcited by 3
  3. 2026Model-Agnostic Signal Discovery with Machine Learning: Bridging the Gap Between Theory and Practicecited by 3
  4. 2025Learning Theory for Kernel Bilevel Optimizationcited by 2
  5. 2025AutoSciDACT: Automated Scientific Discovery through Contrastive Embedding and Hypothesis Testingcited by 2
  6. 2026Regularized f-Divergence Kernel Testscited by 2
  7. 2025Sparse, self-organizing ensembles of local kernels detect rare statistical anomaliescited by 1
  8. 2025TRANSIT your events into a new mass: Fast background interpolation for weakly-supervised anomaly searchescited by 0
  9. 2026BLOKS: Scaling Kernel Solvers with Sketched Block Preconditioningcited by 0