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ORCIDTom Claassen
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2020 – today
- 2024
[j5]Kristian Gonzalez Barman
, Sascha Caron, Tom Claassen, Henk W. de Regt:
Towards a Benchmark for Scientific Understanding in Humans and Machines. Minds Mach. 34(1): 6 (2024)- 2023
- [c20]Mirthe Maria Van Diepen, Ioan Gabriel Bucur, Tom Heskes, Tom Claassen:
Beyond the Markov Equivalence Class: Extending Causal Discovery under Latent Confounding. CLeaR 2023: 707-725 - [c19]Tom Claassen, Joris M. Mooij:
Establishing Markov equivalence in cyclic directed graphs. UAI 2023: 433-442 - [i15]Kristian Gonzalez Barman, Sascha Caron, Tom Claassen, Henk W. de Regt:
Towards a Benchmark for Scientific Understanding in Humans and Machines. CoRR abs/2304.10327 (2023) - [i14]Tom Claassen, Joris M. Mooij:
Establishing Markov Equivalence in Cyclic Directed Graphs. CoRR abs/2309.03092 (2023) - 2022
- [c18]Tom Claassen, Ioan Gabriel Bucur:
Greedy equivalence search in the presence of latent confounders. UAI 2022: 443-452 - 2021
- [j4]Errol Zalmijn, Tom Heskes, Tom Claassen:
Spectral Ranking of Causal Influence in Complex Systems. Entropy 23(3): 369 (2021) - 2020
- [j3]Joris M. Mooij, Sara Magliacane, Tom Claassen:
Joint Causal Inference from Multiple Contexts. J. Mach. Learn. Res. 21: 99:1-99:108 (2020) - [c17]Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, Tom Claassen:
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models. NeurIPS 2020 - [c16]Konrad P. Mielke, Tom Claassen, Mark A. J. Huijbregts, Aafke M. Schipper, Tom M. Heskes:
Discovering cause-effect relationships in spatial systems with a known direction based on observational data. PGM 2020: 305-316 - [c15]Ioan Gabriel Bucur, Tom Claassen, Tom Heskes:
MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models. UAI 2020: 1049-1058 - [c14]Joris M. Mooij, Tom Claassen:
Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles. UAI 2020: 1159-1168 - [i13]Joris M. Mooij, Tom Claassen:
Constraint-Based Causal Discovery In The Presence Of Cycles. CoRR abs/2005.00610 (2020) - [i12]Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, Tom Claassen:
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models. CoRR abs/2011.01625 (2020) - [i11]Ioan Gabriel Bucur, Tom Claassen, Tom Heskes:
MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models. CoRR abs/2012.10141 (2020) - [i10]Errol Zalmijn, Tom Heskes, Tom Claassen:
Spectral Ranking of Causal Influence in Complex Systems. CoRR abs/2012.13195 (2020)
2010 – 2019
- 2019
- [j2]Ioan Gabriel Bucur, Tom Claassen, Tom Heskes:
Large-scale local causal inference of gene regulatory relationships. Int. J. Approx. Reason. 115: 50-68 (2019) - [i9]Ioan Gabriel Bucur, Tom Claassen, Tom Heskes:
Large-Scale Local Causal Inference of Gene Regulatory Relationships. CoRR abs/1909.03818 (2019) - 2018
- [c13]Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij:
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions. NeurIPS 2018: 10869-10879 - [c12]Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen, Tom Heskes:
A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks. PGM 2018: 37-48 - [i8]Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen, Tom Heskes:
A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks. CoRR abs/1809.06827 (2018) - 2017
- [j1]Elena Sokolova, Daniel von Rhein, Jilly Naaijen, Perry Groot, Tom Claassen, Jan K. Buitelaar, Tom Heskes:
Handling hybrid and missing data in constraint-based causal discovery to study the etiology of ADHD. Int. J. Data Sci. Anal. 3(2): 105-119 (2017) - [c11]Ioan Gabriel Bucur, Tom Claassen, Tom Heskes:
Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness. AISTATS 2017: 1523-1531 - [i7]Ioan Gabriel Bucur, Tom Claassen, Tom Heskes:
Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness. CoRR abs/1704.01864 (2017) - [i6]Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij:
Causal Transfer Learning. CoRR abs/1707.06422 (2017) - 2016
- [c10]Sara Magliacane, Tom Claassen, Joris M. Mooij:
Ancestral Causal Inference. NIPS 2016: 4466-4474 - [c9]Elena Sokolova, Martine Hoogman, Perry Groot, Tom Claassen, Tom Heskes:
Computing Lower and Upper Bounds on the Probability of Causal Statements. Probabilistic Graphical Models 2016: 487-498 - [i5]Sara Magliacane, Tom Claassen, Joris M. Mooij:
Ancestral Causal Inference. CoRR abs/1606.07035 (2016) - [i4]Sara Magliacane, Tom Claassen, Joris M. Mooij:
Joint Causal Inference on Observational and Experimental Datasets. CoRR abs/1611.10351 (2016) - 2015
- [c8]Elena Sokolova, Perry Groot, Tom Claassen, Daniel von Rhein, Jan K. Buitelaar, Tom Heskes:
Causal Discovery from Medical Data: Dealing with Missing Values and a Mixture of Discrete and Continuous Data. AIME 2015: 177-181 - [e2]Ricardo Silva, Ilya Shpitser, Robin J. Evans, Jonas Peters, Tom Claassen:
Proceedings of the UAI 2015 Workshop on Advances in Causal Inference co-located with the 31st Conference on Uncertainty in Artificial Intelligence (UAI 2015), Amsterdam, The Netherlands, July 16, 2015. CEUR Workshop Proceedings 1504, CEUR-WS.org 2015 [contents] - 2014
- [c7]Elena Sokolova, Perry Groot, Tom Claassen, Tom Heskes:
Causal Discovery from Databases with Discrete and Continuous Variables. Probabilistic Graphical Models 2014: 442-457 - [e1]Joris M. Mooij, Dominik Janzing, Jonas Peters, Tom Claassen, Antti Hyttinen:
Proceedings of the UAI 2014 Workshop Causal Inference: Learning and Prediction co-located with 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014), Quebec City, Canada, July 27, 2014. CEUR Workshop Proceedings 1274, CEUR-WS.org 2014 [contents] - 2013
- [c6]Tom Claassen, Tom Heskes:
Bayesian Probabilities for Constraint-Based Causal Discovery. IJCAI 2013: 2992-2996 - [c5]Tom Claassen, Joris M. Mooij, Tom Heskes:
Learning Sparse Causal Models is not NP-hard. UAI 2013 - [i3]Tom Claassen, Joris M. Mooij, Tom Heskes:
Learning Sparse Causal Models is not NP-hard. CoRR abs/1309.6824 (2013) - 2012
- [c4]Tom Claassen, Tom Heskes:
A Bayesian Approach to Constraint Based Causal Inference. UAI 2012: 207-216 - [i2]Tom Claassen, Tom Heskes:
A Logical Characterization of Constraint-Based Causal Discovery. CoRR abs/1202.3711 (2012) - [i1]Tom Claassen, Tom Heskes:
A Bayesian Approach to Constraint Based Causal Inference. CoRR abs/1210.4866 (2012) - 2011
- [c3]Tom Claassen, Tom Heskes:
A structure independent algorithm for causal discovery. ESANN 2011 - [c2]Tom Claassen, Tom Heskes:
A Logical Characterization of Constraint-Based Causal Discovery. UAI 2011: 135-144 - 2010
- [c1]Tom Claassen, Tom Heskes:
Causal discovery in multiple models from different experiments. NIPS 2010: 415-423
Coauthor Index
aka: Tom M. Heskes
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last updated on 2024-09-13 00:44 CEST by the dblp team
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