Testing and learning on distributional and set inputs

Testing and learning on distributional and set inputs

<p>As machine learning gains significant attention in many disciplines and research communities, the variety of data structures has increased, with examples including distributions and sets of observations. In this thesis, we consider sets and distributions as inputs for machine learning pr...

Fuld beskrivelse

Bibliografiske detaljer
Hovedforfatter:	Law, H
Andre forfattere:	Sejdinovic, D
Format:	Thesis
Sprog:	English
Udgivet:	2019
Fag:	Statistics Machine learning

Lignende værker

Utilizing Statistical Tests for Comparing Machine Learning Algorithms
af: Hozan Khalid Hamarashid
Udgivet: (2021-07-01)

The information of attribute uncertainties: what convolutional neural networks can learn about errors in input data
af: Natália V N Rodrigues, et al.
Udgivet: (2023-01-01)

Towards trustworthy machine learning with kernels
af: Chau, SL
Udgivet: (2023)

Towards data-efficient deep learning with meta-learning and symmetries
af: Xu, J
Udgivet: (2023)

Kernel-based hypothesis tests: large-scale approximations and Bayesian perspectives
af: Zhang, Q
Udgivet: (2019)

Enabling feature-level interpretability in non-linear latent variable models: a synthesis of statistical and machine learning techniques
af: Martens, K
Udgivet: (2019)

Empirical performance of simple statistical inference-type models with feature extraction in comparison to modern machine learning methods concerning regression and classification problems
af: Dittgen, M
Udgivet: (2019)

Expectancy learning from probabilistic input by infants
af: Alexa R Romberg, et al.
Udgivet: (2013-01-01)

Evaluation metrics and statistical tests for machine learning
af: Oona Rainio, et al.
Udgivet: (2024-03-01)

Boosting any learning algorithm with Statistically Enhanced Learning
af: Florian Felice, et al.
Udgivet: (2025-01-01)

Finite Sample Analysis of Minmax Variant of Offline Reinforcement Learning for General MDPs
af: Jayanth Reddy Regatti, et al.
Udgivet: (2022-01-01)

Input Complexity Affects Long-Term Retention of Statistically Learned Regularities in an Artificial Language Learning Task
af: Ethan Jost, et al.
Udgivet: (2019-10-01)

Machine Learning for Neuroimaging with Scikit-Learn
af: Alexandre eAbraham, et al.
Udgivet: (2014-02-01)

Machine learning and statistical models to address cluster-level confounding for the study of medical devices and surgical epidemiology using observational data
af: Du, M
Udgivet: (2023)

Sample Size Analysis for Machine Learning Clinical Validation Studies
af: Daniel M. Goldenholz, et al.
Udgivet: (2023-02-01)

Statistical Analysis and Machine Learning Used in the Case of Two Behavioral Tests Applied in Zebrafish Exposed to Mycotoxins
af: Tigran-Lucian Mandalian, et al.
Udgivet: (2022-03-01)

Generative models: theory and applications
af: Benton, J
Udgivet: (2023)

On the properties of random feature methods
af: Li, Z
Udgivet: (2020)

Probabilistic models and Monte Carlo objectives in generative modelling
af: Shi, Y
Udgivet: (2023)

Large scale methods for kernels, causal inference and survival modelling
af: Hu, R
Udgivet: (2022)

Bayesian Experimental Design for Finding Reliable Level Set Under Input Uncertainty
af: Shogo Iwazaki, et al.
Udgivet: (2020-01-01)

Intelligently learning from data
af: Edward Palmer, et al.
Udgivet: (2019-04-01)

Bayesian reasoning and machine learning /
af: Barber, David, 1968-
Udgivet: (2012)

A Computational Approach to Statistical Learning /
af: Arnold, Taylor, author, et al.
Udgivet: (2019)

Height and Weight Estimation From Anthropometric Measurements Using Machine Learning Regressions
af: Diego Rativa, et al.
Udgivet: (2018-01-01)

Diversity of Remote Sensing-Based Variable Inputs Improves the Estimation of Seasonal Maximum Freezing Depth
af: Bingquan Wang, et al.
Udgivet: (2021-11-01)

Robustness, structure and hierarchy in deep generative models
af: Willetts, MJF
Udgivet: (2021)

Inference on Markov random fields: methods and applications
af: Lienart, T
Udgivet: (2017)

Optimizing Learning: Predicting Research Competency via Statistical Proficiency
af: Tarid Wongvorachan, et al.
Udgivet: (2024-07-01)

Special Issue on Statistical and Learning-Theoretic Challenges in Data Privacy
af: Aleksandra B. Slavkovic, et al.
Udgivet: (2012-07-01)

Machine learning methods for commonsense reasoning processes : interactive models /
af: Naidenova, Xenia, 1940-
Udgivet: (2010)

Interference effects from machine learning matched confusable object pairs in memory assessment tasks
af: Batra, D
Udgivet: (2022)

The future is coming: promising perspectives regarding the use of machine learning in renal transplantation
af: Pedro Guilherme Coelho Hannun, et al.
Udgivet: (2018-10-01)

Geometry and representation learning in deep generative models
af: Mathieu, E
Udgivet: (2021)

A nonparametric Bayesian method of translating machine learning scores to probabilities in clinical decision support
af: Brian Connolly, et al.
Udgivet: (2017-08-01)

Neural networks and statistical learning /
af: Du, Ke-Lin, author, et al.
Udgivet: (2014)

Influence of the input data on learning deep representations
af: Sylvestre-Alvise Rebuffi
Udgivet: (2020)

Testing the Wind Energy Data Based on Environmental Factors Predicted by Machine Learning with Analysis of Variance
af: Yasemin Ayaz Atalan, et al.
Udgivet: (2024-12-01)

Comparing conventional statistical models and machine learning in a small cohort of South African cardiac patients
af: Preesha Premsagar, et al.
Udgivet: (2022-01-01)

Automated data acquisition via Bayesian experimental design
af: Ivanova, DR
Udgivet: (2024)