Tom Claassen - RankMe

47 papers A* 3A 9B 2Journal 22Unranked 8

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Local Causal Discovery for Statistically Efficient Causal Inference. CoRR Mátyás Schubert, Tom Claassen, Sara Magliacane
2025	A	`conf`	SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph. AISTATS Mátyás Schubert, Tom Claassen, Sara Magliacane
2025	J	`jnl`	SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph. CoRR Mátyás Schubert, Tom Claassen, Sara Magliacane
2024	—	`conf`	AutoCD: Automated Machine Learning for Causal Discovery Algorithms. PGM Gerlise Chan, Tom Claassen, Holger H. Hoos, Tom Heskes, Mitra Baratchi
2024	J	`jnl`	Towards a Benchmark for Scientific Understanding in Humans and Machines. Minds Mach. Kristian Gonzalez Barman, Sascha Caron, Tom Claassen, Henk W. de Regt
2023	—	`conf`	Beyond the Markov Equivalence Class: Extending Causal Discovery under Latent Confounding. CLeaR Mirthe Maria Van Diepen, Ioan Gabriel Bucur, Tom Heskes, Tom Claassen
2023	J	`jnl`	Establishing Markov Equivalence in Cyclic Directed Graphs. CoRR Tom Claassen, Joris M. Mooij
2023	A	`conf`	Establishing Markov equivalence in cyclic directed graphs. UAI Tom Claassen, Joris M. Mooij
2023	J	`jnl`	Towards a Benchmark for Scientific Understanding in Humans and Machines. CoRR Kristian Gonzalez Barman, Sascha Caron, Tom Claassen, Henk W. de Regt
2022	A	`conf`	Greedy equivalence search in the presence of latent confounders. UAI Tom Claassen, Ioan Gabriel Bucur
2021	J	`jnl`	Spectral Ranking of Causal Influence in Complex Systems. Entropy Errol Zalmijn, Tom Heskes, Tom Claassen
2020	A*	`conf`	Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models. NeurIPS Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, Tom Claassen
2020	J	`jnl`	Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models. CoRR Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, Tom Claassen
2020	J	`jnl`	Constraint-Based Causal Discovery In The Presence Of Cycles. CoRR Joris M. Mooij, Tom Claassen
2020	A	`conf`	Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles. UAI Joris M. Mooij, Tom Claassen
2020	—	`conf`	Discovering cause-effect relationships in spatial systems with a known direction based on observational data. PGM Konrad P. Mielke, Tom Claassen, Mark A. J. Huijbregts, Aafke M. Schipper, Tom M. Heskes
2020	J	`jnl`	Joint Causal Inference from Multiple Contexts. J. Mach. Learn. Res. Joris M. Mooij, Sara Magliacane, Tom Claassen
2020	A	`conf`	MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models. UAI Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
2020	J	`jnl`	MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models. CoRR Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
2020	J	`jnl`	Spectral Ranking of Causal Influence in Complex Systems. CoRR Errol Zalmijn, Tom Heskes, Tom Claassen
2019	J	`jnl`	Large-Scale Local Causal Inference of Gene Regulatory Relationships. CoRR Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
2019	J	`jnl`	Large-scale local causal inference of gene regulatory relationships. Int. J. Approx. Reason. Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
2018	—	`conf`	A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks. PGM Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen, Tom Heskes
2018	J	`jnl`	A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks. CoRR Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen, Tom Heskes
2018	A*	`conf`	Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions. NeurIPS Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij
2017	J	`jnl`	Causal Transfer Learning. CoRR Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij
2017	J	`jnl`	Handling hybrid and missing data in constraint-based causal discovery to study the etiology of ADHD. Int. J. Data Sci. Anal. Elena Sokolova, Daniel von Rhein, Jilly Naaijen, Perry Groot, Tom Claassen, Jan K. Buitelaar, Tom Heskes
2017	A	`conf`	Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness. AISTATS Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
2017	J	`jnl`	Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness. CoRR Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
2016	—	`conf`	Ancestral Causal Inference. NIPS Sara Magliacane, Tom Claassen, Joris M. Mooij
2016	J	`jnl`	Ancestral Causal Inference. CoRR Sara Magliacane, Tom Claassen, Joris M. Mooij
2016	—	`conf`	Computing Lower and Upper Bounds on the Probability of Causal Statements. Probabilistic Graphical Models Elena Sokolova, Martine Hoogman, Perry Groot, Tom Claassen, Tom Heskes
2016	J	`jnl`	Joint Causal Inference on Observational and Experimental Datasets. CoRR Sara Magliacane, Tom Claassen, Joris M. Mooij
2015	B	`conf`	Causal Discovery from Medical Data: Dealing with Missing Values and a Mixture of Discrete and Continuous Data. AIME Elena Sokolova, Perry Groot, Tom Claassen, Daniel von Rhein, Jan K. Buitelaar, Tom Heskes
2015	—	`ed.`	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. ACI@UAI Ricardo Silva, Ilya Shpitser, Robin J. Evans, Jonas Peters, Tom Claassen
2014	—	`conf`	Causal Discovery from Databases with Discrete and Continuous Variables. Probabilistic Graphical Models Elena Sokolova, Perry Groot, Tom Claassen, Tom Heskes
2014	—	`ed.`	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. CI@UAI Joris M. Mooij, Dominik Janzing, Jonas Peters, Tom Claassen, Antti Hyttinen
2013	A*	`conf`	Bayesian Probabilities for Constraint-Based Causal Discovery. IJCAI Tom Claassen, Tom Heskes
2013	—	`—`	Causal discovery and logic. Tom Claassen
2013	A	`conf`	Learning Sparse Causal Models is not NP-hard. UAI Tom Claassen, Joris M. Mooij, Tom Heskes
2013	J	`jnl`	Learning Sparse Causal Models is not NP-hard. CoRR Tom Claassen, Joris M. Mooij, Tom Heskes
2012	J	`jnl`	A Bayesian Approach to Constraint Based Causal Inference CoRR Tom Claassen, Tom Heskes
2012	A	`conf`	A Bayesian Approach to Constraint Based Causal Inference. UAI Tom Claassen, Tom Heskes
2012	J	`jnl`	A Logical Characterization of Constraint-Based Causal Discovery CoRR Tom Claassen, Tom Heskes
2011	A	`conf`	A Logical Characterization of Constraint-Based Causal Discovery. UAI Tom Claassen, Tom Heskes
2011	B	`conf`	A structure independent algorithm for causal discovery. ESANN Tom Claassen, Tom Heskes
2010	—	`conf`	Causal discovery in multiple models from different experiments. NIPS Tom Claassen, Tom Heskes