A. Schwaighofer

发表

Dataset Shift in Machine Learning

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

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.

Active label cleaning: Improving dataset quality under resource constraints pdf

Daniel C. Castro, Ben Glocker, Anton Schwaighofer, 2021, ArXiv.

Transductive and Inductive Methods for Approximate Gaussian Process Regression

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

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

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

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).

Mining functional modules in genetic networks with decomposable graphical models.

Anton Schwaighofer, Volker Tresp, Mathäus Dejori, 2004, Omics : a journal of integrative biology.

When Training and Test Sets Are Different: Characterizing Learning Transfer

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

Covariate Shift by Kernel Mean Matching

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

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

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

Active label cleaning for improved dataset quality under resource constraints

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

Enabling large-scale screening of Barrett's esophagus using weakly supervised deep learning in histopathology

Daniel Coelho de Castro, S. Killcoyne, O. Oktay, 2023, medRxiv.

Learning Gaussian processes from multiple tasks

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

GPPS: A Gaussian Process Positioning System for Cellular Networks

Anton Schwaighofer, Volker Tresp, Marian Grigoras, 2003, NIPS.

Scalable clustering and keyword suggestion for online advertisements

Joaquin Quiñonero Candela, Anton Schwaighofer, Thore Graepel, 2009, KDD Workshop on Data Mining and Audience Intelligence for Advertising.

Hierarchical Bayesian modelling with Gaus-sian processes

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

Budget Optimization for Sponsored Search: Censored Learning in MDPs pdf

Anton Schwaighofer, Michael Kearns, Kareem Amin, 2012, UAI.

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

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

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

Stephanie L. Hyland, Daniel Coelho de Castro, Hoifung Poon, 2022, ECCV.

A Probabilistic Approach to Classifying Metabolic Stability

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

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..

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

K. Müller, S. Mika, T. Schroeter, 2007, Journal of computer-aided molecular design.

Probabilistic memory-based collaborative filtering

Volker Tresp, H. Kriegel, Kai Yu, 2004, IEEE Transactions on Knowledge and Data Engineering.

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

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

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.

Classification of rheumatoid joint inflammation based on laser imaging

Peter Mayer, Anton Schwaighofer, Volker Tresp, 2003, IEEE Transactions on Biomedical Engineering.

Learning Gaussian Process Kernels via Hierarchical Bayes

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

Geometry of Covariate Shift with Applications to Active Learning

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