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Jesse H. Krijthe
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2020 – today
- 2023
- [j6]Marco Loog, Jesse H. Krijthe, Manuele Bicego:
Also for k-means: more data does not imply better performance. Mach. Learn. 112(8): 3033-3050 (2023) - [j5]J. M. Smit, Jesse H. Krijthe, W. M. R. Kant, J. A. Labrecque, M. Komorowski, D. A. M. P. J. Gommers, Jasper van Bommel, Marcel J. T. Reinders, Michel E. van Genderen:
Causal inference using observational intensive care unit data: a scoping review and recommendations for future practice. npj Digit. Medicine 6 (2023) - [c12]Rickard Karlsson, Jesse H. Krijthe:
Detecting hidden confounding in observational data using multiple environments. NeurIPS 2023 - [i16]Wouter A. C. van Amsterdam, Nan van Geloven, Jesse H. Krijthe, Rajesh Ranganath, Giovanni Ciná:
When accurate prediction models yield harmful self-fulfilling prophecies. CoRR abs/2312.01210 (2023) - 2022
- [c11]Zhiyi Chen, Marco Loog, Jesse H. Krijthe:
Explaining Two Strange Learning Curves. BNAIC/BENELEARN 2022: 16-30 - [i15]Rickard K. A. Karlsson, Jesse H. Krijthe:
Combining observational datasets from multiple environments to detect hidden confounding. CoRR abs/2205.13935 (2022) - 2021
- [c10]Burak Yildiz, Hayley Hung, Jesse H. Krijthe, Cynthia C. S. Liem, Marco Loog, Gosia Migut, Frans A. Oliehoek, Annibale Panichella, Przemyslaw Pawelczak, Stjepan Picek, Mathijs de Weerdt, Jan van Gemert:
ReproducedPapers.org: Openly Teaching and Structuring Machine Learning Reproducibility. RRPR 2021: 3-11 - 2020
- [i14]Marco Loog, Tom J. Viering, Alexander Mey, Jesse H. Krijthe, David M. J. Tax:
A Brief Prehistory of Double Descent. CoRR abs/2004.04328 (2020) - [i13]Burak Yildiz, Hayley Hung, Jesse H. Krijthe, Cynthia C. S. Liem, Marco Loog, Gosia Migut, Frans A. Oliehoek, Annibale Panichella, Przemyslaw Pawelczak, Stjepan Picek, Mathijs de Weerdt, Jan van Gemert:
ReproducedPapers.org: Openly teaching and structuring machine learning reproducibility. CoRR abs/2012.01172 (2020)
2010 – 2019
- 2019
- [j4]Tom J. Viering, Jesse H. Krijthe, Marco Loog:
Nuclear discrepancy for single-shot batch active learning. Mach. Learn. 108(8-9): 1561-1599 (2019) - [c9]Wouter M. Kouw, Jesse H. Krijthe, Marco Loog:
Robust Importance-Weighted Cross-Validation Under Sample Selection Bias. MLSP 2019: 1-6 - 2018
- [c8]Jesse H. Krijthe, Marco Loog:
The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning. NeurIPS 2018: 1795-1804 - 2017
- [j3]Jesse H. Krijthe, Marco Loog:
Projected estimators for robust semi-supervised classification. Mach. Learn. 106(7): 993-1008 (2017) - [j2]Jesse H. Krijthe, Marco Loog:
Robust semi-supervised least squares classification by implicit constraints. Pattern Recognit. 63: 115-126 (2017) - [i12]Tom J. Viering, Jesse H. Krijthe, Marco Loog:
Nuclear Discrepancy for Active Learning. CoRR abs/1706.02645 (2017) - [i11]Marco Loog, Jesse H. Krijthe, Are Charles Jensen:
On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL. CoRR abs/1707.04025 (2017) - 2016
- [j1]Wouter M. Kouw, Laurens J. P. van der Maaten, Jesse H. Krijthe, Marco Loog:
Feature-Level Domain Adaptation. J. Mach. Learn. Res. 17: 171:1-171:32 (2016) - [c7]Jesse H. Krijthe, Marco Loog:
Reproducible Pattern Recognition Research: The Case of Optimistic SSL. RRPR@ICPR 2016: 48-59 - [c6]Jesse H. Krijthe:
RSSL: Semi-supervised Learning in R. RRPR@ICPR 2016: 104-115 - [c5]Jesse H. Krijthe, Marco Loog:
Optimistic semi-supervised least squares classification. ICPR 2016: 1677-1682 - [c4]Jesse H. Krijthe, Marco Loog:
The Peaking Phenomenon in Semi-supervised Learning. S+SSPR 2016: 299-309 - [p1]Marco Loog, Jesse H. Krijthe, Are Charles Jensen:
On Measuring and Quantifying Performance: error rates, surrogate Loss, and an Example in Semi-Supervised Learning. Handbook of Pattern Recognition and Computer Vision 2016: 53-68 - [i10]Jesse H. Krijthe, Marco Loog:
Projected Estimators for Robust Semi-supervised Classification. CoRR abs/1602.07865 (2016) - [i9]Jesse H. Krijthe, Marco Loog:
Optimistic Semi-supervised Least Squares Classification. CoRR abs/1610.03713 (2016) - [i8]Jesse H. Krijthe, Marco Loog:
The Peaking Phenomenon in Semi-supervised Learning. CoRR abs/1610.05160 (2016) - [i7]Jesse H. Krijthe:
RSSL: Semi-supervised Learning in R. CoRR abs/1612.07993 (2016) - [i6]Jesse H. Krijthe, Marco Loog:
Reproducible Pattern Recognition Research: The Case of Optimistic SSL. CoRR abs/1612.08650 (2016) - [i5]Jesse H. Krijthe, Marco Loog:
The Pessimistic Limits of Margin-based Losses in Semi-supervised Learning. CoRR abs/1612.08875 (2016) - 2015
- [c3]Jesse H. Krijthe, Marco Loog:
Implicitly Constrained Semi-supervised Least Squares Classification. IDA 2015: 158-169 - [i4]Jesse H. Krijthe, Marco Loog:
Implicitly Constrained Semi-Supervised Least Squares Classification. CoRR abs/1507.06802 (2015) - [i3]Wouter M. Kouw, Jesse H. Krijthe, Marco Loog, Laurens J. P. van der Maaten:
Feature-Level Domain Adaptation. CoRR abs/1512.04829 (2015) - [i2]Jesse H. Krijthe, Marco Loog:
Robust Semi-supervised Least Squares Classification by Implicit Constraints. CoRR abs/1512.08240 (2015) - 2014
- [c2]Jesse H. Krijthe, Marco Loog:
Implicitly Constrained Semi-supervised Linear Discriminant Analysis. ICPR 2014: 3762-3767 - [i1]Jesse H. Krijthe, Marco Loog:
Implicitly Constrained Semi-Supervised Linear Discriminant Analysis. CoRR abs/1411.4521 (2014) - 2012
- [c1]Jesse H. Krijthe, Tin Kam Ho, Marco Loog:
Improving cross-validation based classifier selection using meta-learning. ICPR 2012: 2873-2876
Coauthor Index
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