Search Results - "Statistics - Machine Learning"

Application of machine learning and convex limiting to subgrid flux modeling in the shallow-water equations

Authors: Ilya Timofeyev Alexey Schwarzmann Dmitri Kuzmin

Source: Mathematics and Computers in Simulation. 238:163-178

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Physics - Atmospheric and Oceanic Physics, Statistics - Machine Learning, 65M99, Atmospheric and Oceanic Physics (physics.ao-ph), Fluid Dynamics (physics.flu-dyn), FOS: Physical sciences, Machine Learning (stat.ML), Physics - Fluid Dynamics, Computational Physics (physics.comp-ph), Physics - Computational Physics, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2407.17214

Scalarisation-based risk concepts for robust multi-objective optimisation

Authors: Ben Tu Nikolas Kantas Robert M. Lee et al.

Source: European Journal of Operational Research. 327:559-576

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Optimization and Control (math.OC), FOS: Mathematics, Machine Learning (stat.ML), Mathematics - Optimization and Control, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2405.10221

Indiscriminate disruption of conditional inference on multivariate Gaussians

Authors: William N. Caballero Matthew LaRosa Alexander A. Fisher et al.

Source: European Journal of Operational Research. 327:191-202

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Cryptography and Security, Statistics - Machine Learning, Machine Learning (stat.ML), Applications (stat.AP), Statistics - Applications, Cryptography and Security (cs.CR), Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2411.14351

Orthogonal nonnegative matrix factorization with the Kullback–Leibler divergence

Authors: Jean Pacifique Nkurunziza Fulgence Nahayo Nicolas Gillis

Source: Pattern Recognition Letters. 197:353-358

Subject Terms: Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, FOS: Electrical engineering, electronic engineering, information engineering, Machine Learning (stat.ML), Electrical Engineering and Systems Science - Signal Processing, Information Retrieval (cs.IR), Computer Science - Information Retrieval, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2410.07786

Deep learning and NLP in cryptocurrency forecasting: Integrating financial, blockchain, and social media data

Authors: Vincent Gurgul Stefan Lessmann Wolfgang Karl Härdle

Source: International Journal of Forecasting. 41:1666-1695

Subject Terms: FOS: Economics and business, FOS: Computer and information sciences, Computer Science - Machine Learning, Statistical Finance (q-fin.ST), Statistics - Machine Learning, Quantitative Finance - Statistical Finance, Machine Learning (stat.ML), Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2311.14759

Generalized Gumbel-Softmax gradient estimator for generic discrete random variables

Authors: Weonyoung Joo Dongjun Kim Seungjae Shin et al.

Source: Pattern Recognition Letters. 196:148-155

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, 0202 electrical engineering, electronic engineering, information engineering, Machine Learning (stat.ML), 02 engineering and technology, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2003.01847

Optimizing treatment allocation in the presence of interference

Authors: Daan Caljon Jente Van Belle Jeroen Berrevoets et al.

Source: European Journal of Operational Research. 328:620-632

Subject Terms: Social and Information Networks (cs.SI), FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Social and Information Networks, Machine Learning (stat.ML), Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2410.00075

Spatiotemporal Besov Priors for Bayesian Inverse Problems

Authors: Shiwei Lan Mirjeta Pasha Shuyi Li et al.

Source: Journal of the American Statistical Association. :1-30

Subject Terms: Methodology (stat.ME), FOS: Computer and information sciences, Statistics - Machine Learning, Machine Learning (stat.ML), Statistics - Methodology

Access URL: http://arxiv.org/abs/2306.16378

A simple algorithm for output range analysis for deep neural networks

Authors: Rojas, Helder Rojas, Nilton B., Espinoza J. et al.

Source: Cybernetics and Physics. :162-173

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Probability (math.PR), FOS: Mathematics, Machine Learning (stat.ML), Mathematics - Probability, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2407.02700

Error-controlled non-additive interaction discovery in machine learning models

Authors: Winston Chen Yifan Jiang William Stafford Noble et al.

Source: Nature Machine Intelligence. 7:1541-1554

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Applications (stat.AP), Machine Learning (stat.ML), Statistics - Applications, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2408.17016

On the Robustness of the Successive Projection Algorithm

Authors: Giovanni Barbarino Nicolas Gillis

Source: SIAM Journal on Matrix Analysis and Applications. 46:2140-2170

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Data Structures and Algorithms, FOS: Mathematics, Data Structures and Algorithms (cs.DS), Machine Learning (stat.ML), Mathematics - Numerical Analysis, Numerical Analysis (math.NA), Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2411.16195

Sharp Global Guarantees for Nonconvex Low-Rank Recovery in the Noisy Overparameterized Regime

Authors: Richard Y. Zhang

Source: SIAM Journal on Optimization. 35:2128-2154

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Optimization and Control (math.OC), 0202 electrical engineering, electronic engineering, information engineering, FOS: Mathematics, Machine Learning (stat.ML), 02 engineering and technology, Mathematics - Optimization and Control, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2104.10790

TS-RSR: A Provably Efficient Approach for Batch Bayesian Optimization

Authors: Zhaolin Ren Na Li

Source: SIAM Journal on Optimization. 35:2155-2181

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Optimization and Control (math.OC), FOS: Mathematics, Machine Learning (stat.ML), Mathematics - Optimization and Control, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2403.04764

Stream-Based Active Learning for Process Monitoring

Authors: Capezza, Christian Lepore, Antonio Paynabar, Kamran

Source: Technometrics. :1-21

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Machine Learning (stat.ML), Statistical Process Monitoring, Hidden Markov Model, Sequential Data Analysis, Imbalanced Classification, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2411.12563
https://hdl.handle.net/11588/1012783
https://doi.org/10.1080/00401706.2025.2561744

Kernel Multigrid: Accelerate Backfitting via Sparse Gaussian Processes Regression

Authors: Zou, Lu Ding, Liang

Source: IISE Transactions. :1-28

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Machine Learning (stat.ML), Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2403.13300

Sparsifying Dimensionality Reduction of PDE Solution Data with Bregman Learning

Authors: Tjeerd Jan Heeringa Christoph Brune Mengwu Guo

Source: SIAM Journal on Scientific Computing. 47:C1033-C1058

Subject Terms: FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Statistics - Machine Learning, I.2.6, G.1.6, FOS: Mathematics, 65K10 (Primary) 68T07, 65D99, 41A63 (Secondary), Machine Learning (stat.ML), Mathematics - Numerical Analysis, Numerical Analysis (math.NA)

Access URL: http://arxiv.org/abs/2406.12672

A simple and improved algorithm for noisy, convex, zeroth-order optimisation

Authors: Alexandra Carpentier

Source: Mathematical Statistics and Learning. 8:165-192

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Optimization and Control (math.OC), FOS: Mathematics, Machine Learning (stat.ML), Mathematics - Optimization and Control, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2406.18672

Differentially Private Sliced Inverse Regression: Minimax Optimality and Algorithm

Authors: Xintao Xia Linjun Zhang Zhanrui Cai

Source: Journal of the American Statistical Association. :1-22

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Cryptography and Security, 05 social sciences, Mathematics - Statistics Theory, Machine Learning (stat.ML), Statistics Theory (math.ST), 01 natural sciences, Machine Learning (cs.LG), Statistics - Machine Learning, 0502 economics and business, FOS: Mathematics, 0101 mathematics, Cryptography and Security (cs.CR)

Access URL: http://arxiv.org/abs/2401.08150

A Generalized Mean Approach for Distributed-PCA

Authors: Jou, Zhi-Yu Huang, Su-Yun Hung, Hung et al.

Source: Journal of Computational and Graphical Statistics. :1-15

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Machine Learning (stat.ML), Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2410.00397

Covariance Alignment: From Maximum Likelihood Estimation to Gromov–Wasserstein

Authors: Yanjun Han Philippe Rigollet George Stepaniants

Source: SIAM Journal on Mathematics of Data Science. 7:1491-1513

Subject Terms: Methodology (stat.ME), FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, FOS: Mathematics, G.3, Mathematics - Statistics Theory, Machine Learning (stat.ML), Statistics Theory (math.ST), Statistics - Methodology, Primary 62C20, 90B80, 49Q22, secondary 62R07, 05C60, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2311.13595