Suchergebnisse - "[STAT.ML] Statistics [stat]/Machine Learning [stat.ML]"

Assessing the capability of supervised classifiers to recover the true underlying probabilities

Autoren: Dieye, Ndeye Awa Russolillo, Giorgio Niang, Ndèye

Weitere Verfasser: Dieye, Ndeye Awa Russolillo, Giorgio Niang, Ndèye

Quelle: 2025 International Conference on Advanced Machine Learning and Data Science (AMLDS). :19-24

Schlagwörter: supervised classification, [MATH.MATH-PR] Mathematics [math]/Probability [math.PR], true underlying probability, probabilistic classifier, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], calibration metrics, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], loss scores

Dateibeschreibung: application/pdf

Zugangs-URL: https://hal.science/hal-05099239v1

Rational Retrieval Acts: Leveraging Pragmatic Reasoning to Improve Sparse Retrieval

Autoren: Arthur Satouf Gabriel Ben-Zenou Benjamin Piwowarski et al.

Weitere Verfasser: Arthur Satouf Gabriel Ben-Zenou Benjamin Piwowarski et al.

Quelle: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval. :2674-2678

Schlagwörter: FOS: Computer and information sciences, H.3, Rational Speech Acts, Computer Science - Computation and Language, 68P20, 68T50, [SHS.LANGUE] Humanities and Social Sciences/Linguistics, Neural Information Retrieval, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Computer Science - Information Retrieval, Sparse Retrieval, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], Pragmatic Reasoning, Linguistic Pragmatics for Retrieval, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Computation and Language (cs.CL), Information Retrieval (cs.IR)

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2505.03676
https://hal.science/hal-05074220v1
https://hal.science/hal-05074220v1/document
https://doi.org/10.1145/3726302.3730239

Model-Based Learning for Joint Channel Estimation and Hybrid MIMO Precoding

Autoren: Klaimi, Nay Bedoui, Amira Elvira, Clément et al.

Weitere Verfasser: Klaimi, Nay Bedoui, Amira Elvira, Clément et al.

Quelle: 2025 IEEE 26th International Workshop on Signal Processing and Artificial Intelligence for Wireless Communications (SPAWC). :1-5

Schlagwörter: Signal Processing (eess.SP), FOS: Computer and information sciences, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, channel estimation, Deep learning, Hybrid precoding, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Machine Learning (cs.LG), Machine Learning, MIMO, Deep unfolding, Signal Processing, FOS: Electrical engineering, electronic engineering, information engineering, MIMO Hybrid precoding channel estimation Deep unfolding Deep learning

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2505.04255
https://hal.science/hal-05056429v3
https://hal.science/hal-05056429v3/document
https://doi.org/10.1109/spawc66079.2025.11143375

Asymmetry of the Relative Entropy in the Regularization of Empirical Risk Minimization

Autoren: Daunas, Francisco Esnaola, Iñaki Perlaza, Samir et al.

Weitere Verfasser: Daunas, Francisco Esnaola, Iñaki Perlaza, Samir et al.

Quelle: IEEE Transactions on Information Theory. 71:6198-6226

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, relative entropy regularization, Computer Science - Information Theory, Information Theory (cs.IT), Machine Learning (stat.ML), Empirical risk minimization, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Machine Learning (cs.LG), [MATH.MATH-IT] Mathematics [math]/Information Theory [math.IT], Statistics - Machine Learning, [INFO.INFO-IT] Computer Science [cs]/Information Theory [cs.IT], reference measure, inductive bias

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2410.02833
https://inria.hal.science/hal-04719783v3

Decentralized Reinforcement Learning for Multi-Agent Multi-Resource Allocation via Dynamic Cluster Agreements

Autoren: Marino, Antonio Restrepo, Esteban Pacchierotti, Claudio et al.

Weitere Verfasser: Marino, Antonio Restrepo, Esteban Pacchierotti, Claudio et al.

Quelle: IEEE Robotics and Automation Letters. 10:8123-8130

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, [SPI.AUTO] Engineering Sciences [physics]/Automatic, Statistics - Machine Learning, Resource Assignment, Distributed Control, Machine Learning (stat.ML), Graph Neural Network, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Machine Learning (cs.LG)

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2503.02437

Improving Deep Neural Network Reliability via Transient-Fault-Aware Design and Training

Autoren: Fernando Fernandes dos Santos Niccolò Cavagnero Marco Ciccone et al.

Weitere Verfasser: Fernando Fernandes dos Santos Niccolò Cavagnero Marco Ciccone et al.

Quelle: IEEE Transactions on Emerging Topics in Computing. 13:829-840

Schlagwörter: Neutrons, [INFO.INFO-AR] Computer Science [cs]/Hardware Architecture [cs.AR], Deep Learning, GPUs, Radiation-induced faults ✦, Radiation-induced faults, Reliability, Computer Vision, Robustness, Neutrons, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML]

Dateibeschreibung: application/pdf

Zugangs-URL: https://hal.science/hal-04818068v1

KEGNN: Knowledge-Enhanced Graph Neural Networks for User Engagement Prediction

Autoren: Fan, Ching-Hao Zhou, Hao Sun, Yao et al.

Weitere Verfasser: Fan, Ching-Hao Zhou, Hao Sun, Yao et al.

Quelle: Proceedings of the 2025 International Conference on Multimedia Retrieval. :275-283

Schlagwörter: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], user engagement prediction, Information extraction, Social networks knowledge sharing, graph learning, [INFO.INFO-SI] Computer Science [cs]/Social and Information Networks [cs.SI], [SHS.STAT] Humanities and Social Sciences/Methods and statistics, graph neural networks, natural language processing, Neural networks, Computing methodologies, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML]

Dateibeschreibung: application/pdf

A New Statistical Model of Star Speckles for Learning to Detect and Characterize Exoplanets in Direct Imaging Observations

Autoren: Bodrito, Théo Flasseur, Olivier Mairal, Julien et al.

Weitere Verfasser: Bodrito, Théo Flasseur, Olivier Mairal, Julien et al.

Quelle: 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). :1230-1240

Schlagwörter: High contrast imaging, Earth and Planetary Astrophysics (astro-ph.EP), FOS: Computer and information sciences, Computer Science - Machine Learning, Astronomy, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, [SDU.ASTR.EP] Sciences of the Universe [physics]/Astrophysics [astro-ph]/Earth and Planetary Astrophysics [astro-ph.EP], FOS: Physical sciences, [INFO] Computer Science [cs], Statistics - Applications, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Machine Learning (cs.LG), High angular resolution, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], [SDU.ASTR.IM] Sciences of the Universe [physics]/Astrophysics [astro-ph]/Instrumentation and Methods for Astrophysic [astro-ph.IM], [STAT.AP] Statistics [stat]/Applications [stat.AP], Machine learning, Exoplanet detection methods, Applications (stat.AP), Astrophysics - Instrumentation and Methods for Astrophysics, Instrumentation and Methods for Astrophysics (astro-ph.IM), [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Astrophysics - Earth and Planetary Astrophysics

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2503.17117
https://hal.science/hal-05000360v1

PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance Prediction

Autoren: Poesina, Eduard Costache, Adriana Chifu, Adrian-Gabriel et al.

Weitere Verfasser: Poesina, Eduard Costache, Adriana Chifu, Adrian-Gabriel et al.

Quelle: 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). :28651-28661

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Computer Science - Computation and Language, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Computation and Language (cs.CL), [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Machine Learning (cs.LG)

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2406.04746
https://hal.science/hal-05149275v1

Do You Care About Your Positions? Users Under Liquidation Risk in Decentralized Lending Protocol

Autoren: Mu, Boyang Tovanich, Natkamon Prat, Julien

Weitere Verfasser: Mu, Boyang Tovanich, Natkamon Prat, Julien

Quelle: 2025 IEEE International Conference on Blockchain and Cryptocurrency (ICBC). :1-9

Schlagwörter: decentralized finance, user modeling, [INFO.INFO-CY] Computer Science [cs]/Computers and Society [cs.CY], [INFO.INFO-ET] Computer Science [cs]/Emerging Technologies [cs.ET], financial risks, decision-making, [SHS.ECO] Humanities and Social Sciences/Economics and Finance, lending protocols, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], liquidation

Dateibeschreibung: application/pdf

Zugangs-URL: https://hal.science/hal-05041569v1/document
https://doi.org/10.1109/icbc64466.2025.11114495
https://hal.science/hal-05041569v1

Optimizing Complex Control Systems with Differentiable Simulators: A Hybrid Approach to Reinforcement Learning and Trajectory Planning

Autoren: Parag, Amit Mansard, Nicolas Misimi, Ekrem et al.

Weitere Verfasser: Parag, Amit Mansard, Nicolas Misimi, Ekrem et al.

Quelle: 2025 IEEE International Conference on Robotics and Automation (ICRA). :9214-9220

Schlagwörter: [INFO.INFO-RB] Computer Science [cs]/Robotics [cs.RO], Value function, Reinforcement Leaning RL, Robotics, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML]

Dateibeschreibung: application/pdf

Zugangs-URL: https://hal.science/hal-05294931v1/document
https://hal.science/hal-05294931v1
https://doi.org/10.1109/icra55743.2025.11127746

In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting

Autoren: Philippenko, Constantin Scaman, Kevin Massoulié, Laurent

Weitere Verfasser: Philippenko, Constantin Scaman, Kevin Massoulié, Laurent

Quelle: Proceedings of the AAAI Conference on Artificial Intelligence. 39:19904-19912

Schlagwörter: Optimization, Matrix factorisation, Machine Learning, FOS: Computer and information sciences, Optimization and Control (math.OC), Optimization and Control, Federated learning, FOS: Mathematics, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Machine Learning (cs.LG)

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2409.08771
https://hal.science/hal-04893622v2

Active Fourier Auditor for Estimating Distributional Properties of ML Models

Autoren: Ajarra, Ayoub Ghosh, Bishwamittra Basu, Debabrota

Weitere Verfasser: Ajarra, Ayoub Ghosh, Bishwamittra Basu, Debabrota

Quelle: Proceedings of the AAAI Conference on Artificial Intelligence. 39:15330-15338

Schlagwörter: [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], FOS: Computer and information sciences, Responsible AI, Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Sample complexity, Manipulation proof, Property estimation, Machine Learning (stat.ML), [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], [STAT.CO] Statistics [stat]/Computation [stat.CO], [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Machine Learning (cs.LG), Computer Science - Computers and Society, [INFO.INFO-CY] Computer Science [cs]/Computers and Society [cs.CY], Artificial Intelligence (cs.AI), Bias estimation, Universal auditor, Statistics - Machine Learning, Computers and Society (cs.CY), Algorithmic audits, Robustness Testing

Zugangs-URL: http://arxiv.org/abs/2410.08111
https://hal.science/hal-04733059v1

Stochastic Online Instrumental Variable Regression: Regrets for Endogeneity and Bandit Feedback

Autoren: Della Vecchia, Riccardo Basu, Debabrota

Weitere Verfasser: Della Vecchia, Riccardo Basu, Debabrota

Quelle: Proceedings of the AAAI Conference on Artificial Intelligence. 39:16190-16198

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Bandit / imperfect feedback, Machine Learning (stat.ML), [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], 02 engineering and technology, 01 natural sciences, Linear bandits, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Machine Learning (cs.LG), Causality, Online linear regression, [INFO.INFO-CY] Computer Science [cs]/Computers and Society [cs.CY], Two-stage regression, Online learning, Statistics - Machine Learning, [SHS.STAT] Humanities and Social Sciences/Methods and statistics, 0202 electrical engineering, electronic engineering, information engineering, Regret Bounds, Instrumental Variables, Econometrics, 0101 mathematics, [SHS.ECO] Humanities and Social Sciences/Economics and Finance, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST]

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2302.09357
https://hal.science/hal-03831210v3
https://hal.science/hal-03831210v3/document

Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms Under Composition

Autoren: Lebeda, Christian Janos Regehr, Matthew Kamath, Gautam et al.

Weitere Verfasser: Lebeda, Christian Janos Regehr, Matthew Kamath, Gautam et al.

Quelle: 2025 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML). :996-1006

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Cryptography and Security, [INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS], Machine Learning (stat.ML), [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Machine Learning (cs.LG), Differential privacy, Statistics - Machine Learning, Computer Science - Data Structures and Algorithms, Data Structures and Algorithms (cs.DS), Cryptography and Security (cs.CR), [INFO.INFO-CR] Computer Science [cs]/Cryptography and Security [cs.CR]

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2405.20769
https://hal.science/hal-05240803v1/document
https://doi.org/10.1109/satml64287.2025.00060
https://hal.science/hal-05240803v1

Deep learning and genomic best linear unbiased prediction integration: An approach to identify potential nonlinear genetic relationships between traits

Autoren: Shokor, F. Croiseau, P. Gangloff, H. et al.

Weitere Verfasser: Shokor, F. Croiseau, P. Gangloff, H. et al.

Quelle: Journal of Dairy Science, Vol 108, Iss 6, Pp 6174-6189 (2025)

Schlagwörter: Dairying, machine learning, SF221-250, machine learning GBLUP multitrait models genetic evaluation genetic relationship, GBLUP, [SDV.GEN.GA] Life Sciences [q-bio]/Genetics/Animal genetics, SF250.5-275, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], multitrait models, genetic evaluation, genetic relationship, Dairy processing. Dairy products

Dateibeschreibung: application/pdf

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/40252763
https://doaj.org/article/a66208a6d6f44d029f976a761b6f4eaa
https://hal.inrae.fr/hal-05218863v1
https://doi.org/10.3168/jds.2024-26057
https://hal.inrae.fr/hal-05218863v1/document

The double-edged sword of user reviews: how customer feedback can build and break hotel brands

Autoren: Oshriyeh, Omid Egger, Roman

Weitere Verfasser: Oshriyeh, Omid Egger, Roman

Quelle: Journal of Hospitality Marketing & Management. 34:925-963

Schlagwörter: customer engagement, Value co-destruction, topic modeling, attribution theory, justice theory, online reviews, [SHS.GESTION] Humanities and Social Sciences/Business administration, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML]

Zugangs-URL: https://hal.science/hal-05196289v1
https://doi.org/10.1080/19368623.2025.2494567

Federal Reserve Communication and the COVID‐19 Pandemic

Autoren: Benchimol, Jonathan Kazinnik, Sophia Saadon, Yossi

Weitere Verfasser: Benchimol, Jonathan Kazinnik, Sophia Saadon, Yossi

Quelle: The Manchester School. 93:464-484

Schlagwörter: FOS: Computer and information sciences, General Economics (econ.GN), Financial stability, Text mining, Economics, Information Theory, [INFO.INFO-TT] Computer Science [cs]/Document and Text Processing, Text analytics, Machine Learning (stat.ML), Financial crises, Data science, Machine leaning, Crisis, Machine Learning, FOS: Economics and business, E50, [SHS.STAT] Humanities and Social Sciences/Methods and statistics, Machine learning, Applications (stat.AP), E58, Computation and Language, [SHS.ECO] Humanities and Social Sciences/Economics and Finance, E52, C55, ddc:330, Information Theory (cs.IT), COVID-19, Text analysis, Unconventional monetary policy, General Economics, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Central bank communication, C82, Applications, C81, E44, [PHYS.PHYS.PHYS-DATA-AN] Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an], E63, Computation and Language (cs.CL)

Dateibeschreibung: application/pdf

Zugangs-URL: http://arxiv.org/abs/2508.04830
https://hdl.handle.net/10419/323832
https://hal.science/hal-05203069v1/document
https://doi.org/10.1111/manc.12520
https://hal.science/hal-05203069v1

Identification and validation of robust hospital-acquired pneumonia subphenotypes associated with all-cause mortality: a multi-cohort derivation and validation

Autoren: Martin, Florian Pierre Poulain, Cécile Mulier, Jelle Haitsma et al.

Weitere Verfasser: Martin, Florian Pierre Poulain, Cécile Mulier, Jelle Haitsma et al.

Quelle: Intensive Care Medicine. 51:692-707

Schlagwörter: Male, Critical Illness, Subphenotype, Healthcare-Associated Pneumonia, Middle Aged, Prognosis, Lung microbiome, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Antimicrobial therapy, Anti-Bacterial Agents, Cohort Studies, Machine Learning, Phenotype, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Machine learning, [SDV.MHEP.MI] Life Sciences [q-bio]/Human health and pathology/Infectious diseases, [SDV.MHEP.PSR] Life Sciences [q-bio]/Human health and pathology/Pulmonology and respiratory tract, Humans, Hospital-acquired pneumonia, Female, France, Mortality, Aged, Netherlands

Dateibeschreibung: application/pdf

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/40261385
https://hal.science/hal-05248288v1/document
https://hal.science/hal-05248288v1
https://doi.org/10.1007/s00134-025-07884-3

Targeted nano-energetic material exploration through active learning algorithm implementation

Autoren: Carreira, Leandro Pillemont, Lea Sami, Yasser et al.

Weitere Verfasser: Carreira, Leandro Pillemont, Lea Sami, Yasser et al.

Quelle: Energetic Materials Frontiers, Vol 6, Iss 1, Pp 3-13 (2025)

Schlagwörter: Active learning, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Materials design, Chemical technology, Thermite, Intelligent sampling, TP1-1185, [SPI.MAT] Engineering Sciences [physics]/Materials, Gaussian process, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Performance optimization, [SPI.MAT]Engineering Sciences [physics]/Materials

Dateibeschreibung: application/pdf

Zugangs-URL: https://doaj.org/article/10eed994e79549d4a66f10deb68a4f90
https://laas.hal.science/hal-04937289v1
https://doi.org/10.1016/j.enmf.2024.08.004
https://laas.hal.science/hal-04937289v1/document