Sara Magliacane

62 papers A* 13A 5B 1Journal 29Unranked 13

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Improving Local Explainability By Learning Causal Graphs From Data. Trans. Mach. Learn. Res. Daan Roos, Sebastian Gerwinn, Jan-Willem van de Meent, Sara Magliacane
2025	—	`conf`	Combining Causal Models for More Accurate Abstractions of Neural Networks. CLeaR Theodora-Mara Pîslar, Sara Magliacane, Atticus Geiger
2025	J	`jnl`	Combining Causal Models for More Accurate Abstractions of Neural Networks. CoRR Theodora-Mara Pîslar, Sara Magliacane, Atticus Geiger
2025	A	`ed.`	Conference on Uncertainty in Artificial Intelligence, Rio Othon Palace, Rio de Janeiro, Brazil, 21-25 July 2025. UAI Silvia Chiappa, Sara Magliacane
2025	J	`jnl`	Learning Interactive World Model for Object-Centric Reinforcement Learning. CoRR Fan Feng, Phillip Lippe, Sara Magliacane
2025	J	`jnl`	Learning to Defer for Causal Discovery with Imperfect Experts. CoRR Oscar Clivio, Divyat Mahajan, Perouz Taslakian, Sara Magliacane, Ioannis Mitliagkas, Valentina Zantedeschi, Alexandre Drouin
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
2025	J	`jnl`	Sample-efficient Learning of Concepts with Theoretical Guarantees: from Data to Concepts without Interventions. CoRR Hidde Fokkema, Tim van Erven, Sara Magliacane
2024	A*	`conf`	A Sparsity Principle for Partially Observable Causal Representation Learning. ICML Danru Xu, Dingling Yao, Sébastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, Sara Magliacane
2024	J	`jnl`	A Sparsity Principle for Partially Observable Causal Representation Learning. CoRR Danru Xu, Dingling Yao, Sébastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, Sara Magliacane
2024	A*	`conf`	Amortized Equation Discovery in Hybrid Dynamical Systems. ICML Yongtuo Liu, Sara Magliacane, Miltiadis Kofinas, Stratis Gavves
2024	J	`jnl`	Amortized Equation Discovery in Hybrid Dynamical Systems. CoRR Yongtuo Liu, Sara Magliacane, Miltiadis Kofinas, Efstratios Gavves
2024	J	`jnl`	IEEE Transactions on Neural Networks and Learning Systems Special Issue on Causal Discovery and Causality-Inspired Machine Learning. IEEE Trans. Neural Networks Learn. Syst. Kun Zhang, Ilya Shpitser, Sara Magliacane, Davide Bacciu, Fei Wu, Changshui Zhang, Peter Spirtes
2024	A	`conf`	Learning Causal Abstractions of Linear Structural Causal Models. UAI Riccardo Massidda, Sara Magliacane, Davide Bacciu
2024	J	`jnl`	Learning Causal Abstractions of Linear Structural Causal Models. CoRR Riccardo Massidda, Sara Magliacane, Davide Bacciu
2024	A*	`conf`	Multi-View Causal Representation Learning with Partial Observability. ICLR Dingling Yao, Danru Xu, Sébastien Lachapelle, Sara Magliacane, Perouz Taslakian, Georg Martius, Julius von Kügelgen, Francesco Locatello
2024	—	`conf`	Towards the Reusability and Compositionality of Causal Representations. CLeaR Davide Talon, Phillip Lippe, Stuart James, Alessio Del Bue, Sara Magliacane
2024	J	`jnl`	Towards the Reusability and Compositionality of Causal Representations. CoRR Davide Talon, Phillip Lippe, Stuart James, Alessio Del Bue, Sara Magliacane
2023	A	`conf`	BISCUIT: Causal Representation Learning from Binary Interactions. UAI Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves
2023	J	`jnl`	BISCUIT: Causal Representation Learning from Binary Interactions. CoRR Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves
2023	A*	`conf`	Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems. ICLR Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves
2023	A*	`conf`	Graph Switching Dynamical Systems. ICML Yongtuo Liu, Sara Magliacane, Miltiadis Kofinas, Efstratios Gavves
2023	J	`jnl`	Graph Switching Dynamical Systems. CoRR Yongtuo Liu, Sara Magliacane, Miltiadis Kofinas, Efstratios Gavves
2023	J	`jnl`	Invariant Neural Ordinary Differential Equations. CoRR Ilze Amanda Auzina, Çagatay Yildiz, Sara Magliacane, Matthias Bethge, Efstratios Gavves
2023	A*	`conf`	Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning. NeurIPS Fan Feng, Sara Magliacane
2023	J	`jnl`	Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning. CoRR Fan Feng, Sara Magliacane
2023	A*	`conf`	Modulated Neural ODEs. NeurIPS Ilze Amanda Auzina, Çagatay Yildiz, Sara Magliacane, Matthias Bethge, Efstratios Gavves
2023	J	`jnl`	Multi-View Causal Representation Learning with Partial Observability. CoRR Dingling Yao, Danru Xu, Sébastien Lachapelle, Sara Magliacane, Perouz Taslakian, Georg Martius, Julius von Kügelgen, Francesco Locatello
2022	A*	`conf`	AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning. ICLR Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang
2022	A*	`conf`	CITRIS: Causal Identifiability from Temporal Intervened Sequences. ICML Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Stratis Gavves
2022	J	`jnl`	CITRIS: Causal Identifiability from Temporal Intervened Sequences. CoRR Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves
2022	A*	`conf`	Factored Adaptation for Non-Stationary Reinforcement Learning. NeurIPS Fan Feng, Biwei Huang, Kun Zhang, Sara Magliacane
2022	J	`jnl`	Factored Adaptation for Non-Stationary Reinforcement Learning. CoRR Fan Feng, Biwei Huang, Kun Zhang, Sara Magliacane
2022	J	`jnl`	iCITRIS: Causal Representation Learning for Instantaneous Temporal Effects. CoRR Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves
2021	J	`jnl`	AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning. CoRR Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang
2021	B	`conf`	The Challenges of Cross-Document Coreference Resolution for Email. K-CAP Xue Li, Sara Magliacane, Paul Groth
2021	—	`conf`	Verifiably safe exploration for end-to-end reinforcement learning. HSCC Nathan Hunt, Nathan Fulton, Sara Magliacane, Trong Nghia Hoang, Subhro Das, Armando Solar-Lezama
2020	J	`jnl`	Active Structure Learning of Causal DAGs via Directed Clique Tree. CoRR Chandler Squires, Sara Magliacane, Kristjan H. Greenewald, Dmitriy Katz, Murat Kocaoglu, Karthikeyan Shanmugam
2020	A*	`conf`	Active Structure Learning of Causal DAGs via Directed Clique Trees. NeurIPS Chandler Squires, Sara Magliacane, Kristjan H. Greenewald, Dmitriy Katz, Murat Kocaoglu, Karthikeyan Shanmugam
2020	J	`jnl`	Joint Causal Inference from Multiple Contexts. J. Mach. Learn. Res. Joris M. Mooij, Sara Magliacane, Tom Claassen
2020	J	`jnl`	Verifiably Safe Exploration for End-to-End Reinforcement Learning. CoRR Nathan Hunt, Nathan Fulton, Sara Magliacane, Nghia Hoang, Subhro Das, Armando Solar-Lezama
2019	A*	`conf`	Sample Efficient Active Learning of Causal Trees. NeurIPS Kristjan H. Greenewald, Dmitriy Katz, Karthikeyan Shanmugam, Sara Magliacane, Murat Kocaoglu, Enric Boix Adserà, Guy Bresler
2018	J	`jnl`	An Upper Bound for Random Measurement Error in Causal Discovery. CoRR Tineke Blom, Anna Klimovskaia, Sara Magliacane, Joris M. Mooij
2018	A	`conf`	Causal Discovery in the Presence of Measurement Error. UAI Tineke Blom, Anna Klimovskaia, Sara Magliacane, Joris M. Mooij
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
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	J	`jnl`	Joint Causal Inference on Observational and Experimental Datasets. CoRR Sara Magliacane, Tom Claassen, Joris M. Mooij
2015	—	`conf`	FoxPSL: An Extended and Scalable PSL Implementation. AAAI Spring Symposia Sara Magliacane, Philip Stutz, Paul Groth, Abraham Bernstein
2015	J	`jnl`	foxPSL: A Fast, Optimized and eXtended PSL implementation. Int. J. Approx. Reason. Sara Magliacane, Philip Stutz, Paul Groth, Abraham Bernstein
2013	—	`conf`	Git2PROV: Exposing Version Control System Content as W3C PROV. ISWC (Posters & Demos) Tom De Nies, Sara Magliacane, Ruben Verborgh, Sam Coppens, Paul Groth, Erik Mannens, Rik Van de Walle
2013	—	`conf`	Repurposing Benchmark Corpora for Reconstructing Provenance. SePublica Sara Magliacane, Paul Groth
2012	—	`conf`	Automatic Metadata Annotation through Reconstructing Provenance. SWPM@ESWC Paul Groth, Yolanda Gil, Sara Magliacane
2012	—	`conf`	Efficient Execution of Top-K SPARQL Queries. ISWC (1) Sara Magliacane, Alessandro Bozzon, Emanuele Della Valle
2012	—	`ch.`	Extending SPARQL Algebra to Support Efficient Evaluation of Top-K SPARQL Queries. SeCO Book Alessandro Bozzon, Emanuele Della Valle, Sara Magliacane
2012	—	`conf`	Hubble: Linked Data Hub for Clinical Decision Support. ESWC (Satellite Events) Rinke Hoekstra, Sara Magliacane, Laurens Rietveld, Gerben de Vries, Adianto Wibisono, Stefan Schlobach
2012	—	`conf`	Reconstructing Provenance. ISWC (2) Sara Magliacane
2012	—	`conf`	Towards Reconstructing the Provenance of Clinical Guidelines. SWAT4LS Sara Magliacane, Paul Groth
2012	—	`conf`	Who Are We Talking About? Identifying Scientific Populations Online. CSWS Julie M. Birkholz, Shenghui Wang, Paul Groth, Sara Magliacane