Anton Schwaighofer

发表

Observations on the Nyström Method for Gaussian Processes

Christopher K. I. Williams, Volker Tresp, C. Rasmussen, 2002 .

Dataset Shift in Machine Learning

Neil D. Lawrence, Masashi Sugiyama, Anton Schwaighofer, 2009 .

Predicting Lipophilicity of Drug‐Discovery Molecules using Gaussian Process Models

K. Müller, S. Mika, Anton Schwaighofer, 2007, ChemMedChem.

Covariate Shift by Kernel Mean Matching

Karsten M. Borgwardt, B. Schölkopf, Alex Smola, 2009, NIPS 2009.

Learning Gaussian processes from multiple tasks

Anton Schwaighofer, Volker Tresp, Kai Yu, 2005, ICML.

Machine learning models for lipophilicity and their domain of applicability.

Klaus-Robert Müller, Sebastian Mika, Anton Schwaighofer, 2007, Molecular pharmaceutics.

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules

Klaus-Robert Müller, Sebastian Mika, Anton Schwaighofer, 2007, J. Comput. Aided Mol. Des..

Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach

Klaus-Robert Müller, Sebastian Mika, Anton Schwaighofer, 2007, J. Chem. Inf. Model..

A Probabilistic Approach to Classifying Metabolic Stability

Klaus-Robert Müller, Sebastian Mika, Anton Schwaighofer, 2008, J. Chem. Inf. Model..

Learning Gaussian Process Kernels via Hierarchical Bayes

Anton Schwaighofer, Volker Tresp, Kai Yu, 2004, NIPS.

Transductive and Inductive Methods for Approximate Gaussian Process Regression

Anton Schwaighofer, Volker Tresp, Volker Tresp, 2002, NIPS.

An Adversarial View of Covariate Shift and a Minimax Approach

Masashi Sugiyama, Joaquin Quiñonero-Candela, Anton Schwaighofer, 2009 .

When Training and Test Sets Are Different: Characterizing Learning Transfer

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

Ieee Transactions on Knowledge and Data Engineering Probabilistic Memory-based Collaborative Filtering

Hans-Peter Kriegel, Anton Schwaighofer, Volker Tresp, 2022 .

A Conditional Expectation Approach to Model Selection and Active Learning under Covariate Shift

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

Gaussian Processes in Practice

Neil D. Lawrence, Anton Schwaighofer, Joaquin Quinonero Candela, 2007 .

Algorithms for Covariate Shift

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

On the Training/Test Distributions Gap: A Data Representation Learning Framework

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

When Training and Test Sets Are Different

Neil D. Lawrence, Masashi Sugiyama, Amos Storkey, 2008 .

Evaluation of Deep Learning to Augment Image-Guided Radiotherapy for Head and Neck and Prostate Cancers

Anton Schwaighofer, B. Glocker, O. Oktay, 2020, JAMA network open.

Making the Most of Text Semantics to Improve Biomedical Vision-Language Processing

Stephanie L. Hyland, Daniel Coelho de Castro, Anton Schwaighofer, 2022, ECCV.

Probabilistic Memory Based Collaborative Filtering: Learning Individual and Social Preferences

Hans-Peter Kriegel, Anton Schwaighofer, Kai Yu, 2004 .

Laser Imaging Techniques for Follow-up Analysis of Joint Inflammation in Patients with Rheumatoid Arthritis

Anton Schwaighofer, Volker Tresp, Alexander D. Klose, 2003 .

How wrong can we get? A review of machine learning approaches and error bars.

Gilles Blanchard, Sebastian Mika, Anton Schwaighofer, 2009, Combinatorial chemistry & high throughput screening.

The Bayesian Committee Support Vector Machine

Anton Schwaighofer, Volker Tresp, Volker Tresp, 2001, ICANN.

Introduction to Dataset Shift

Neil D. Lawrence, Masashi Sugiyama, Joaquin Quiñonero-Candela, 2009 .

Active label cleaning for improved dataset quality under resource constraints

Daniel Coelho de Castro, Anton Schwaighofer, M. Lungren, 2021, Nature Communications.

Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes

Wei-Ying Ma, Anton Schwaighofer, Volker Tresp, 2002, UAI.

Removing redundancy and inconsistency in memory-based collaborative filtering

Hans-Peter Kriegel, Anton Schwaighofer, Xiaowei Xu, 2002, CIKM '02.

Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing

Stephanie L. Hyland, Daniel Coelho de Castro, Anton Schwaighofer, 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Hierarchical Bayesian modelling with Gaus-sian processes

Anton Schwaighofer, Volker Tresp, Kevin C. Yu, 2005, NIPS 2005.