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...

Bibliografske podrobnosti
Glavni avtor:	Law, H
Drugi avtorji:	Sejdinovic, D
Format:	Thesis
Jezik:	English
Izdano:	2019
Teme:	Statistics Machine learning

Podobne knjige/članki

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

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

Towards trustworthy machine learning with kernels
od: Chau, SL
Izdano: (2023)

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

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

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

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

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

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

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

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

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

Machine Learning for Neuroimaging with Scikit-Learn
od: Alexandre eAbraham, et al.
Izdano: (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
od: Du, M
Izdano: (2023)

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

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

Generative models: theory and applications
od: Benton, J
Izdano: (2023)

On the properties of random feature methods
od: Li, Z
Izdano: (2020)

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

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

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

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

Bayesian reasoning and machine learning /
od: Barber, David, 1968-
Izdano: (2012)

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

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

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

Robustness, structure and hierarchy in deep generative models
od: Willetts, MJF
Izdano: (2021)

Inference on Markov random fields: methods and applications
od: Lienart, T
Izdano: (2017)

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

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

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

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

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

Geometry and representation learning in deep generative models
od: Mathieu, E
Izdano: (2021)

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

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

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

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

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

Automated data acquisition via Bayesian experimental design
od: Ivanova, DR
Izdano: (2024)