Jesse H. Krijthe

42 papers A* 3B 5Journal 26Unranked 7

Year	Rank	Type	Title / Venue / Authors
2025	—	`conf`	Are Interactive Visualizations in Machine Learning Education Helping Students? ITiCSE (1) Ilinca Rentea, Gosia Migut, Jesse H. Krijthe
2025	B	`conf`	Counterintuitive Behavior of Clustering Quality: Findings for K-Means on Synthetic and Real Data. IDA Marco Loog, Jesse H. Krijthe, Manuele Bicego
2025	A*	`conf`	Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms. ICML Rickard Karlsson, Jesse H. Krijthe
2025	J	`jnl`	Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms. CoRR Rickard K. A. Karlsson, Jesse H. Krijthe
2025	J	`jnl`	Qini curve estimation under clustered network interference. CoRR Rickard K. A. Karlsson, Bram van den Akker, Felipe Moraes, Hugo Manuel Proença, Jesse H. Krijthe
2025	J	`jnl`	Robust estimation of heterogeneous treatment effects in randomized trials leveraging external data. CoRR Rickard Karlsson, Piersilvio De Bartolomeis, Issa J. Dahabreh, Jesse H. Krijthe
2025	J	`jnl`	When accurate prediction models yield harmful self-fulfilling prophecies. Patterns Wouter A. C. van Amsterdam, Nan van Geloven, Jesse H. Krijthe, Rajesh Ranganath, Giovanni Cinà
2023	J	`jnl`	Also for k-means: more data does not imply better performance. Mach. Learn. Marco Loog, Jesse H. Krijthe, Manuele Bicego
2023	J	`jnl`	Causal inference using observational intensive care unit data: a scoping review and recommendations for future practice. npj Digit. Medicine 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
2023	A*	`conf`	Detecting hidden confounding in observational data using multiple environments. NeurIPS Rickard Karlsson, Jesse H. Krijthe
2023	J	`jnl`	When accurate prediction models yield harmful self-fulfilling prophecies. CoRR Wouter A. C. van Amsterdam, Nan van Geloven, Jesse H. Krijthe, Rajesh Ranganath, Giovanni Ciná
2022	J	`jnl`	Combining observational datasets from multiple environments to detect hidden confounding. CoRR Rickard K. A. Karlsson, Jesse H. Krijthe
2022	—	`conf`	Explaining Two Strange Learning Curves. BNAIC/BENELEARN Zhiyi Chen, Marco Loog, Jesse H. Krijthe
2021	—	`conf`	ReproducedPapers.org: Openly Teaching and Structuring Machine Learning Reproducibility. RRPR 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
2020	J	`jnl`	A Brief Prehistory of Double Descent. CoRR Marco Loog, Tom J. Viering, Alexander Mey, Jesse H. Krijthe, David M. J. Tax
2020	J	`jnl`	ReproducedPapers.org: Openly teaching and structuring machine learning reproducibility. CoRR 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
2019	J	`jnl`	Nuclear discrepancy for single-shot batch active learning. Mach. Learn. Tom J. Viering, Jesse H. Krijthe, Marco Loog
2019	—	`conf`	Robust Importance-Weighted Cross-Validation Under Sample Selection Bias. MLSP Wouter M. Kouw, Jesse H. Krijthe, Marco Loog
2018	A*	`conf`	The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning. NeurIPS Jesse H. Krijthe, Marco Loog
2017	J	`jnl`	Nuclear Discrepancy for Active Learning. CoRR Tom J. Viering, Jesse H. Krijthe, Marco Loog
2017	J	`jnl`	On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL. CoRR Marco Loog, Jesse H. Krijthe, Are Charles Jensen
2017	J	`jnl`	Projected estimators for robust semi-supervised classification. Mach. Learn. Jesse H. Krijthe, Marco Loog
2017	J	`jnl`	Robust semi-supervised least squares classification by implicit constraints. Pattern Recognit. Jesse H. Krijthe, Marco Loog
2016	J	`jnl`	Feature-Level Domain Adaptation. J. Mach. Learn. Res. Wouter M. Kouw, Laurens J. P. van der Maaten, Jesse H. Krijthe, Marco Loog
2016	—	`ch.`	On Measuring and Quantifying Performance: error rates, surrogate Loss, and an Example in Semi-Supervised Learning. Handbook of Pattern Recognition and Computer Vision Marco Loog, Jesse H. Krijthe, Are Charles Jensen
2016	J	`jnl`	Optimistic Semi-supervised Least Squares Classification. CoRR Jesse H. Krijthe, Marco Loog
2016	B	`conf`	Optimistic semi-supervised least squares classification. ICPR Jesse H. Krijthe, Marco Loog
2016	J	`jnl`	Projected Estimators for Robust Semi-supervised Classification. CoRR Jesse H. Krijthe, Marco Loog
2016	—	`conf`	RSSL: Semi-supervised Learning in R. RRPR@ICPR Jesse H. Krijthe
2016	J	`jnl`	RSSL: Semi-supervised Learning in R. CoRR Jesse H. Krijthe
2016	—	`conf`	Reproducible Pattern Recognition Research: The Case of Optimistic SSL. RRPR@ICPR Jesse H. Krijthe, Marco Loog
2016	J	`jnl`	Reproducible Pattern Recognition Research: The Case of Optimistic SSL. CoRR Jesse H. Krijthe, Marco Loog
2016	—	`conf`	The Peaking Phenomenon in Semi-supervised Learning. S+SSPR Jesse H. Krijthe, Marco Loog
2016	J	`jnl`	The Peaking Phenomenon in Semi-supervised Learning. CoRR Jesse H. Krijthe, Marco Loog
2016	J	`jnl`	The Pessimistic Limits of Margin-based Losses in Semi-supervised Learning. CoRR Jesse H. Krijthe, Marco Loog
2015	J	`jnl`	Feature-Level Domain Adaptation. CoRR Wouter M. Kouw, Jesse H. Krijthe, Marco Loog, Laurens J. P. van der Maaten
2015	J	`jnl`	Implicitly Constrained Semi-Supervised Least Squares Classification. CoRR Jesse H. Krijthe, Marco Loog
2015	B	`conf`	Implicitly Constrained Semi-supervised Least Squares Classification. IDA Jesse H. Krijthe, Marco Loog
2015	J	`jnl`	Robust Semi-supervised Least Squares Classification by Implicit Constraints. CoRR Jesse H. Krijthe, Marco Loog
2014	J	`jnl`	Implicitly Constrained Semi-Supervised Linear Discriminant Analysis. CoRR Jesse H. Krijthe, Marco Loog
2014	B	`conf`	Implicitly Constrained Semi-supervised Linear Discriminant Analysis. ICPR Jesse H. Krijthe, Marco Loog
2012	B	`conf`	Improving cross-validation based classifier selection using meta-learning. ICPR Jesse H. Krijthe, Tin Kam Ho, Marco Loog