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1
Optimization with Sparsity-Inducing Penalties
Optimization with Sparsity-Inducing Penalties
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Optimization with Sparsity-Inducing Penalties

Foundations and trends in machine learning, 2012 [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 1935-8237 ;EISSN: 1935-8245 ;DOI: 10.1561/2200000015

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2
Machine Learning in High Energy Physics Community White Paper
Machine Learning in High Energy Physics Community White Paper
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Machine Learning in High Energy Physics Community White Paper

Journal of physics. Conference series, 2018-09, Vol.1085 (2), p.22008 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;2018. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;ISSN: 1742-6596 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/1085/2/022008

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3
Boosting Simple Learners
Boosting Simple Learners
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Boosting Simple Learners

TheoretiCS, 2023-06, Vol.2 [Peer Reviewed Journal]

EISSN: 2751-4838 ;DOI: 10.46298/theoretics.23.8

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4
QCD-aware recursive neural networks for jet physics
QCD-aware recursive neural networks for jet physics
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QCD-aware recursive neural networks for jet physics

The journal of high energy physics, 2019-01, Vol.2019 (1), p.1-23, Article 57 [Peer Reviewed Journal]

The Author(s) 2019 ;Journal of High Energy Physics is a copyright of Springer, (2019). All Rights Reserved. ;ISSN: 1029-8479 ;ISSN: 1126-6708 ;EISSN: 1029-8479 ;DOI: 10.1007/JHEP01(2019)057

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5
Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning
Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning
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Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning

The Astrophysical journal, 2019-11, Vol.886 (1), p.49 [Peer Reviewed Journal]

2019. The American Astronomical Society. All rights reserved. ;Copyright IOP Publishing Nov 20, 2019 ;ISSN: 0004-637X ;ISSN: 1538-4357 ;EISSN: 1538-4357 ;DOI: 10.3847/1538-4357/ab4c41

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6
The derivatives of Sinkhorn-Knopp converge
The derivatives of Sinkhorn-Knopp converge
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Article 		Add to My Research

The derivatives of Sinkhorn-Knopp converge

SIAM journal on optimization, 2023, Vol.33 (3) [Peer Reviewed Journal]

2022. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;http://arxiv.org/licenses/nonexclusive-distrib/1.0 ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 1052-6234 ;EISSN: 2331-8422 ;EISSN: 1095-7189 ;DOI: 10.48550/arxiv.2207.12717

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7
When stakes are high: balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates
When stakes are high: balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates
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When stakes are high: balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates

Expert systems with applications, 2020-12 [Peer Reviewed Journal]

2020. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;http://arxiv.org/licenses/nonexclusive-distrib/1.0 ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0957-4174 ;EISSN: 2331-8422 ;EISSN: 1873-6793 ;DOI: 10.48550/arxiv.2007.06894

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8
Unbalanced Optimal Transport, from Theory to Numerics
Unbalanced Optimal Transport, from Theory to Numerics
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Unbalanced Optimal Transport, from Theory to Numerics

arXiv.org, 2023-01 [Peer Reviewed Journal]

2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;http://creativecommons.org/licenses/by/4.0 ;EISSN: 2331-8422 ;DOI: 10.48550/arxiv.2211.08775

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9
Mining gold from implicit models to improve likelihood-free inference
Mining gold from implicit models to improve likelihood-free inference
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Mining gold from implicit models to improve likelihood-free inference

Proceedings of the National Academy of Sciences - PNAS, 2020-03, Vol.117 (10), p.5242-5249 [Peer Reviewed Journal]

Copyright National Academy of Sciences Mar 10, 2020 ;2020 ;ISSN: 0027-8424 ;ISSN: 1091-6490 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.1915980117 ;PMID: 32079725

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10
Fast Online Changepoint Detection via Functional Pruning CUSUM statistics
Fast Online Changepoint Detection via Functional Pruning CUSUM statistics
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Fast Online Changepoint Detection via Functional Pruning CUSUM statistics

Journal of machine learning research, 2023-03, Vol.24, p.1-36 [Peer Reviewed Journal]

2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;http://creativecommons.org/licenses/by/4.0 ;Attribution ;ISSN: 1532-4435 ;EISSN: 2331-8422 ;EISSN: 1533-7928 ;DOI: 10.48550/arxiv.2110.08205

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11
A Concentration of Measure and Random Matrix Approach to Large Dimensional Robust Statistics
A Concentration of Measure and Random Matrix Approach to Large Dimensional Robust Statistics
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A Concentration of Measure and Random Matrix Approach to Large Dimensional Robust Statistics

arXiv.org, 2022-12 [Peer Reviewed Journal]

2022. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;http://arxiv.org/licenses/nonexclusive-distrib/1.0 ;EISSN: 2331-8422 ;DOI: 10.48550/arxiv.2006.09728

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12
Neural Networks beyond explainability: Selective inference for sequence motifs
Neural Networks beyond explainability: Selective inference for sequence motifs
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Neural Networks beyond explainability: Selective inference for sequence motifs

Transactions on Machine Learning Research Journal, 2022-12 [Peer Reviewed Journal]

2022. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;http://creativecommons.org/licenses/by-nc-nd/4.0 ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 2835-8856 ;EISSN: 2331-8422 ;DOI: 10.48550/arxiv.2212.12542

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13
Preconditioned Gradient Descent for Overparameterized Nonconvex Burer--Monteiro Factorization with Global Optimality Certification
Preconditioned Gradient Descent for Overparameterized Nonconvex Burer--Monteiro Factorization with Global Optimality Certification
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Preconditioned Gradient Descent for Overparameterized Nonconvex Burer--Monteiro Factorization with Global Optimality Certification

arXiv.org, 2022-06 [Peer Reviewed Journal]

2022. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;http://arxiv.org/licenses/nonexclusive-distrib/1.0 ;EISSN: 2331-8422 ;DOI: 10.48550/arxiv.2206.03345

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14
Model Averaging and Double Machine Learning
Model Averaging and Double Machine Learning
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Model Averaging and Double Machine Learning

arXiv.org, 2024-01

2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;http://creativecommons.org/licenses/by/4.0 ;EISSN: 2331-8422 ;DOI: 10.48550/arxiv.2401.01645

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15
The boosted HP filter is more general than you might think
The boosted HP filter is more general than you might think
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The boosted HP filter is more general than you might think

arXiv.org, 2022-09

2022. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;http://arxiv.org/licenses/nonexclusive-distrib/1.0 ;EISSN: 2331-8422 ;DOI: 10.48550/arxiv.2209.09810

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16
Calibrating doubly-robust estimators with unbalanced treatment assignment
Calibrating doubly-robust estimators with unbalanced treatment assignment
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Calibrating doubly-robust estimators with unbalanced treatment assignment

arXiv.org, 2024-03

2024. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;http://arxiv.org/licenses/nonexclusive-distrib/1.0 ;EISSN: 2331-8422 ;DOI: 10.48550/arxiv.2403.01585

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17
Learning to Reconstruct Signals From Binary Measurements
Learning to Reconstruct Signals From Binary Measurements
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Article 		Add to My Research

Learning to Reconstruct Signals From Binary Measurements

arXiv.org, 2023-11 [Peer Reviewed Journal]

2023. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;http://arxiv.org/licenses/nonexclusive-distrib/1.0 ;EISSN: 2331-8422 ;DOI: 10.48550/arxiv.2303.08691

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18
Conformal Prediction for Trustworthy Detection of Railway Signals
Conformal Prediction for Trustworthy Detection of Railway Signals
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Conformal Prediction for Trustworthy Detection of Railway Signals

arXiv.org, 2023-01

2023. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;http://arxiv.org/licenses/nonexclusive-distrib/1.0 ;EISSN: 2331-8422 ;DOI: 10.48550/arxiv.2301.11136

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19
Robust matrix completion via Novel M-estimator Functions
Robust matrix completion via Novel M-estimator Functions
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Robust matrix completion via Novel M-estimator Functions

http://creativecommons.org/licenses/by/4.0 ;DOI: 10.48550/arxiv.2310.04953

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20
Learning with Fitzpatrick Losses
Learning with Fitzpatrick Losses
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Learning with Fitzpatrick Losses

arXiv.org, 2024-05

2024. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;http://arxiv.org/licenses/nonexclusive-distrib/1.0 ;EISSN: 2331-8422 ;DOI: 10.48550/arxiv.2405.14574

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