1 code implementation • 4 Jun 2024 • Laurens Sluijterman, Frank Kreuwel, Eric Cator, Tom Heskes

In this paper, we present a smooth approximation of the pinball loss, the arctan pinball loss, that is tailored to the needs of XGBoost.

1 code implementation • 25 May 2024 • Roel Bouman, Linda Schmeitz, Luco Buise, Jacco Heres, Yuliya Shapovalova, Tom Heskes

In this paper we present novel methodology for automatic anomaly and switch event filtering to improve load estimation in power grid systems.

no code implementations • 25 Feb 2024 • Binyam Gebre, Karoliina Ranta, Stef van den Elzen, Ernst Kuiper, Thijs Baars, Tom Heskes

In personalized recommender systems, embeddings are often used to encode customer actions and items, and retrieval is then performed in the embedding space using approximate nearest neighbor search.

1 code implementation • 2 Oct 2023 • Charlotte Cambier van Nooten, Tom van de Poll, Sonja Füllhase, Jacco Heres, Tom Heskes, Yuliya Shapovalova

Ensuring electricity grid reliability becomes increasingly challenging with the shift towards renewable energy and declining conventional capacities.

1 code implementation • 4 Aug 2023 • Laurens Sluijterman, Eric Cator, Tom Heskes

This paper introduces a first implementation of a novel likelihood-ratio-based approach for constructing confidence intervals for neural networks.

1 code implementation • 1 May 2023 • Roel Bouman, Zaharah Bukhsh, Tom Heskes

In this study we evaluate 32 unsupervised anomaly detection algorithms on 52 real-world multivariate tabular datasets, performing the largest comparison of unsupervised anomaly detection algorithms to date.

1 code implementation • 17 Feb 2023 • Laurens Sluijterman, Eric Cator, Tom Heskes

We demonstrate, both on toy examples and on a number of benchmark UCI regression data sets, that following the original recommendations and the novel separate regularization can lead to significant improvements.

1 code implementation • 7 Oct 2022 • Jelle Piepenbrock, Josef Urban, Konstantin Korovin, Miroslav Olšák, Tom Heskes, Mikolaš Janota

In particular, we develop a GNN2RNN architecture based on an invariant graph neural network (GNN) that learns from problems and their solutions independently of symbol names (addressing the abundance of skolems), combined with a recurrent neural network (RNN) that proposes for each clause its instantiations.

no code implementations • 6 Apr 2022 • Lisandro A. Jimenez-Roa, Tom Heskes, Tiedo Tinga, Marielle Stoelinga

Fault tree analysis is a well-known technique in reliability engineering and risk assessment, which supports decision-making processes and the management of complex systems.

1 code implementation • 22 Feb 2022 • Laurens Sluijterman, Eric Cator, Tom Heskes

A classical parametric model has uncertainty in the parameters due to the fact that the data on which the model is build is a random sample.

1 code implementation • 25 Nov 2021 • Zhuoran Liu, Zhengyu Zhao, Alex Kolmus, Tijn Berns, Twan van Laarhoven, Tom Heskes, Martha Larson

Recent work has shown that imperceptible perturbations can be applied to craft unlearnable examples (ULEs), i. e. images whose content cannot be used to improve a classifier during training.

1 code implementation • 1 Nov 2021 • Alex Kolmus, Grégory Baltus, Justin Janquart, Twan van Laarhoven, Sarah Caudill, Tom Heskes

Fast, highly accurate, and reliable inference of the sky origin of gravitational waves would enable real-time multi-messenger astronomy.

no code implementations • 7 Jun 2021 • Laurens Sluijterman, Eric Cator, Tom Heskes

We show why it is fundamentally flawed to test a prediction or confidence interval on a single test set.

no code implementations • 10 Feb 2021 • Jelle Piepenbrock, Tom Heskes, Mikoláš Janota, Josef Urban

On these tasks, 3SIL is shown to significantly outperform several established RL and imitation learning methods.

no code implementations • 24 Dec 2020 • Errol Zalmijn, Tom Heskes, Tom Claassen

Like natural complex systems such as the Earth's climate or a living cell, semiconductor lithography systems are characterized by nonlinear dynamics across more than a dozen orders of magnitude in space and time.

Time Series Analysis Information Theory Information Theory Adaptation and Self-Organizing Systems Physics and Society

1 code implementation • 18 Dec 2020 • Ioan Gabriel Bucur, Tom Claassen, Tom Heskes

In this paper, we propose a Bayesian approach called BayesMR that is a generalization of the Mendelian randomization technique in which we allow for pleiotropic effects and, crucially, for the possibility of reverse causation.

no code implementations • 18 Dec 2020 • Ioan Gabriel Bucur, Tom Claassen, Tom Heskes

Unfortunately, searching for proper instruments in a many-dimensional set of candidates is a daunting task due to the intractable model space and the fact that we cannot directly test which of these candidates are valid, so most existing search methods either rely on overly stringent modeling assumptions or fail to capture the inherent model uncertainty in the selection process.

1 code implementation • NeurIPS 2020 • Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, Tom Claassen

Shapley values underlie one of the most popular model-agnostic methods within explainable artificial intelligence.

1 code implementation • 3 Sep 2019 • Ioan Gabriel Bucur, Tom Claassen, Tom Heskes

Many of these computational methods are designed to infer individual regulatory relationships among genes from data on gene expression.

1 code implementation • 19 May 2019 • Sascha Caron, Tom Heskes, Sydney Otten, Bob Stienen

Constraining the parameters of physical models with $>5-10$ parameters is a widespread problem in fields like particle physics and astronomy.

1 code implementation • 18 Sep 2018 • Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen, Tom Heskes

Gene regulatory networks play a crucial role in controlling an organism's biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data.

2 code implementations • 12 Jun 2018 • Ruifei Cui, Ioan Gabriel Bucur, Perry Groot, Tom Heskes

We consider the problem of learning parameters of latent variable models from mixed (continuous and ordinal) data with missing values.

no code implementations • 24 May 2018 • Ridho Rahmadi, Perry Groot, Tom Heskes

In our previous study, we introduced stable specification search for cross-sectional data (S3C).

1 code implementation • 6 Apr 2017 • Ioan Gabriel Bucur, Tom Claassen, Tom Heskes

Causal effect estimation from observational data is an important and much studied research topic.

no code implementations • 24 Oct 2016 • Mohsen Ghafoorian, Nico Karssemeijer, Tom Heskes, Mayra Bergkamp, Joost Wissink, Jiri Obels, Karlijn Keizer, Frank-Erik de Leeuw, Bram van Ginneken, Elena Marchiori, Bram Platel

In this paper, we propose an automated two-stage method using deep convolutional neural networks (CNN).

no code implementations • 16 Oct 2016 • Mohsen Ghafoorian, Nico Karssemeijer, Tom Heskes, Inge van Uden, Clara Sanchez, Geert Litjens, Frank-Erik de Leeuw, Bram van Ginneken, Elena Marchiori, Bram Platel

The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks.

no code implementations • 22 May 2016 • Ridho Rahmadi, Perry Groot, Marieke HC van Rijn, Jan AJG van den Brand, Marianne Heins, Hans Knoop, Tom Heskes

The present work introduces a novel causal modeling algorithm for longitudinal data, that is robust for finite samples based on recent advances in stability selection using subsampling and selection algorithms.

no code implementations • 17 Apr 2016 • Arno Solin, Pasi Jylänki, Jaakko Kauramäki, Tom Heskes, Marcel A. J. van Gerven, Simo Särkkä

We apply the method to both simulated and empirical data, and demonstrate the efficiency and generality of our Bayesian source reconstruction approach which subsumes various classical approaches in the literature.

1 code implementation • 15 Apr 2016 • Harm Berntsen, Wouter Kuijper, Tom Heskes

We introduce a novel artificial neural network architecture that integrates robustness to adversarial input in the network structure.

1 code implementation • 9 Dec 2015 • Fabian Gieseke, Cosmin Eugen Oancea, Ashish Mahabal, Christian Igel, Tom Heskes

A buffer k-d tree is a k-d tree variant for massively-parallel nearest neighbor search.

no code implementations • 18 Jun 2015 • Ridho Rahmadi, Perry Groot, Marianne Heins, Hans Knoop, Tom Heskes

Structure search is performed over Structural Equation Models.

no code implementations • 6 Nov 2014 • Tom Claassen, Joris M. Mooij, Tom Heskes

The algorithm is an adaptation of the well-known FCI algorithm by (Spirtes et al., 2000) that is also sound and complete, but has worst case complexity exponential in $N$.

no code implementations • 16 Jan 2014 • Botond Cseke, Tom Heskes

We define the Gaussian fractional Bethe free energy in terms of the moment parameters of the approximate marginals, derive a lower and an upper bound on the fractional Bethe free energy and establish a necessary condition for the lower bound to be bounded from below.

no code implementations • 26 Sep 2013 • Joris Mooij, Tom Heskes

We propose a method for learning cyclic causal models from a combination of observational and interventional equilibrium data.

no code implementations • 26 Sep 2013 • Tom Claassen, Joris Mooij, Tom Heskes

This paper shows that causal model discovery is not an NP-hard problem, in the sense that for sparse graphs bounded by node degree k the sound and complete causal model can be obtained in worst case order N^{2(k+2)} independence tests, even when latent variables and selection bias may be present.

no code implementations • 2 Jul 2013 • Evgeni Tsivtsivadze, Tom Heskes

We propose a novel sparse preference learning/ranking algorithm.

no code implementations • 17 May 2013 • Botond Cseke, Andrew Zammit Mangion, Tom Heskes, Guido Sanguinetti

Spatio-temporal point process models play a central role in the analysis of spatially distributed systems in several disciplines.

no code implementations • NeurIPS 2011 • Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf

We study a particular class of cyclic causal models, where each variable is a (possibly nonlinear) function of its parents and additive noise.

no code implementations • 17 Aug 2011 • Jesse Alama, Tom Heskes, Daniel Kühlwein, Evgeni Tsivtsivadze, Josef Urban

A good method for premise selection in complex mathematical libraries is the application of machine learning to large corpora of proofs.

no code implementations • NeurIPS 2010 • Tom Claassen, Tom Heskes

We present the MCI-algorithm as the first method that can infer provably valid causal relations in the large sample limit from different experiments.

no code implementations • NeurIPS 2009 • Marcel V. Gerven, Botond Cseke, Robert Oostenveld, Tom Heskes

We introduce a novel multivariate Laplace (MVL) distribution as a sparsity promoting prior for Bayesian source localization that allows the specification of constraints between and within sources.

no code implementations • NeurIPS 2007 • José M. Hernández-Lobato, Tjeerd Dijkstra, Tom Heskes

We introduce a hierarchical Bayesian model for the discovery of putative regulators from gene expression data only.

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