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...

Bibliografiset tiedot
Päätekijä:	Law, H
Muut tekijät:	Sejdinovic, D
Aineistotyyppi:	Opinnäyte
Kieli:	English
Julkaistu:	2019
Aiheet:	Statistics Machine learning

Samankaltaisia teoksia

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

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

Towards trustworthy machine learning with kernels
Tekijä: Chau, SL
Julkaistu: (2023)

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

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

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

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

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

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

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

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

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

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

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

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

Generative models: theory and applications
Tekijä: Benton, J
Julkaistu: (2023)

On the properties of random feature methods
Tekijä: Li, Z
Julkaistu: (2020)

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

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

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

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

Bayesian reasoning and machine learning /
Tekijä: Barber, David, 1968-
Julkaistu: (2012)

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

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

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

Robustness, structure and hierarchy in deep generative models
Tekijä: Willetts, MJF
Julkaistu: (2021)

Inference on Markov random fields: methods and applications
Tekijä: Lienart, T
Julkaistu: (2017)

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

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

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

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

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

Geometry and representation learning in deep generative models
Tekijä: Mathieu, E
Julkaistu: (2021)

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

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

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

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

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

Automated data acquisition via Bayesian experimental design
Tekijä: Ivanova, DR
Julkaistu: (2024)