Guy Van den Broeck

Celestijnenlaan 200A
3001 Heverlee, Belgium
+32 484 794938
guyvdb@cs.ucla.edu

I am a postdoctoral researcher in the Machine Learning subgroup of the Declarative Languages and Artificial Intelligence lab at KU Leuven, Belgium, where I am supported by the Research Foundation-Flanders. From 2013-2014, I was a postdoctoral researcher in the Automated Reasoning lab at the University of California, Los Angeles, where I was advised by Adnan Darwiche. I obtained my Ph.D. in Computer Science from KU Leuven in 2013, under the supervision of Luc De Raedt.

My research interests are in Machine Learning (Statistical Relational Learning), Knowledge Representation and Reasoning (Graphical Models, Lifted Probabilistic Inference), Applications of Probabilistic Reasoning and Learning (Probabilistic Programming, Probabilistic Databases), and Artifical Intelligence in general.

News

May 2015
In summer, I am joining the computer science department at UCLA as an assistant professor.
May 2015
Two papers were accepted at UAI, one of which won the best paper award
Apr 2015
Four papers were accepted at IJCAI
Nov 2014
Two papers were accepted at AAAI

Talks and Tutorials

2015
Invited Talk: Sentential Decision Diagrams And Their Applications, with Adnan Darwiche and Arthur Choi, INFORMS Annual Meeting, Philadelphia
Talk: Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data [pdf], Conference on Uncertainty in Artificial Intelligence (UAI)
Invited Talk: First-Order Knowledge Compilation for Probabilistic Reasoning [pdf], Symposium on New Frontiers in Knowledge Compilation, Vienna Center for Logic and Algorithms, Austria
Invited Talk: Symmetry in Probabilistic Databases [pdf], Alberto Mendelzon International Workshop on Foundations of Data Management, Lima, Peru
Invited Tutorial: An Overview of Statistical Relational Learning, Alberto Mendelzon Graduate School on Data Management, Lima, Peru
Talk: Approximate Symmetries in Lifted Inference [pdf] [video], Banff Workshop on New Perspectives for Relational Learning
Invited Talk: Scalable Inference and Learning for High-Level Probabilistic Models [pdf], Department of Computer Science, Cornell University
Invited Talk: Scalable Inference and Learning for High-Level Probabilistic Models [pdf] [video], Department of Computer Science & Engineering, University of Washington, Seattle
Invited Talk: Scalable Inference and Learning for High-Level Probabilistic Models [pdf], Department of Computer Science, University of Southern California
Invited Talk: Scalable Inference and Learning for High-Level Probabilistic Models [pdf], Department of Computer Science, University of California, Irvine
Invited Talk: Scalable Inference and Learning for High-Level Probabilistic Models [pdf], Cheriton School of Computer Science, University of Waterloo
Invited Talk: Scalable Inference and Learning for High-Level Probabilistic Models [pdf], Computer Sciences Department, University of Wisconsin-Madison
Invited Talk: Scalable Inference and Learning for High-Level Probabilistic Models [pdf], Department of Computer Science, Tufts University
Invited Talk: Scalable Inference and Learning for High-Level Probabilistic Models [pdf], Department of Computer Science and Informatics, Indiana University, Bloomington
Invited Talk: Scalable Inference and Learning for High-Level Probabilistic Models [pdf], School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne
Invited Talk: Scalable Inference and Learning for High-Level Probabilistic Models [pdf], Computer Science Department, University of California, Los Angeles
Talk: On the Role of Canonicity in Knowledge Compilation [pdf], AAAI Conference on Artificial Intelligence
Talk: Lifted Probabilistic Inference for Asymmetric Graphical Models [pdf], AAAI Conference on Artificial Intelligence
2014
Tutorial: Lifted probabilistic inference in relational models [pdf], Conference on Uncertainty in Artificial Intelligence (UAI), Co-authored with Dan Suciu.
Invited Tutorial: Lifted inference in statistical relational models [pdf], International workshop on Big Uncertain Data (BUDA) at the ACM SIGMOD/PODS conference
Invited Talk: ECCAI Dissertation Award Ceremony at the European Conference on Artificial Intelligence (ECAI), Prague, Czech Republic
Invited Talk: Scientific prize IBM Belgium for Informatics Award Ceremony, IBM, Brussels, Belgium
Invited Talk: Lifted Inference and Learning in Statistical Relational Models, Center for Data Science, University of Washington, Tacoma
(more)
2011
Invited Talk: Monte-Carlo tree search for multi-player, no-limit Texas hold’em poker, SIKS Symposium on Strategic Decision-Making in Complex Games, Maastricht University, Netherlands
Tutorial Presenter: Lifted probabilistic inference by first-order knowledge compilation, IJCAI Tutorial on Lifted Inference in Probabilistic Logical Models

Awards

Selected Publications

Books, journal papers, highly selective conference papers, and selected reports are listed here.
For all publications, including all reports, workshop papers and abstracts, see the lists   By Year,  By Type,  By Google Scholar,  RSS feed (subscribe),  BibTex

2015

[45]Guy Van den Broeck, Kristian Kersting, Sriraam Natarajan, David Poole. An Introduction to Lifted Probabilistic Inference (working title), MIT Press, 2015. (in preparation)
[44]Daan Fierens, Guy Van den Broeck, Joris Renkens, Dimitar Shterionov, Bernd Gutmann, Ingo Thon, Gerda Janssens, Luc De Raedt. Inference and Learning in Probabilistic Logic Programs using Weighted Boolean Formulas, In Theory and Practice of Logic Programming, volume 15, 2015. [pdf]
[43]Jonas Vlasselaer, Guy Van den Broeck, Angelika Kimmig, Wannes Meert, Luc De Raedt. Anytime Inference in Probabilistic Logic Programs with Tp-compilation, In Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), 2015. [pdf]
[42]Jonas Vlasselaer, Wannes Meert, Guy Van den Broeck, Luc De Raedt. Exploiting Local and Repeated Structure in Dynamic Bayesian Networks, In Artificial Intelligence, 2015. (under review)
[41]Luc De Raedt, Anton Dries, Ingo Thon, Guy Van den Broeck, Mathias Verbeke. Inducing Probabilistic Relational Rules from Probabilistic Examples, In Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), 2015. [pdf]
[40]Guy Van den Broeck, Karthika Mohan, Arthur Choi, Adnan Darwiche, Judea Pearl. Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data, In Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI), 2015. [pdf]
Oral full presentation, acceptance rate 28/292 = 9%
[39]Guy Van den Broeck. Lifted Inference and Learning in Statistical Relational Models, AI Access, 2015. (in preparation)
[38]Guy Van den Broeck. Towards High-Level Probabilistic Reasoning with Lifted Inference, In Proceedings of the AAAI Spring Symposium on KRR, 2015. (to appear) [pdf]
[37]Guy Van den Broeck, Adnan Darwiche. On the Role of Canonicity in Knowledge Compilation, In Proceedings of the 29th Conference on Artificial Intelligence (AAAI), 2015. (to appear) [pdf]
[36]Guy Van den Broeck, Mathias Niepert. Lifted Probabilistic Inference for Asymmetric Graphical Models, In Proceedings of the 29th Conference on Artificial Intelligence (AAAI), 2015. (to appear) [pdf]
[35]Jan Van Haaren, Guy Van den Broeck, Wannes Meert, Jesse Davis. Lifted Generative Learning of Markov Logic Networks, In Machine Learning, 2015. (under review)
[34]Arthur Choi, Guy Van den Broeck, Adnan Darwiche. Tractable Learning for Structured Probability Spaces: A Case Study in Learning Preference Distributions, In Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), 2015. [pdf]
[33]Bart Bogaerts, Guy Van den Broeck. Knowledge Compilation of Logic Programs Using Approximation Fixpoint Theory, In Proceedings of the 31st International Conference on Logic Programming (ICLP), 2015.
[32]Vaishak Belle, Guy Van den Broeck, Andrea Passerini. Hashing-Based Approximate Probabilistic Inference in Hybrid Domains, In Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI), 2015. [pdf]
UAI best paper award
[31]Vaishak Belle, Andrea Passerini, Guy Van den Broeck. Probabilistic Inference in Hybrid Domains by Weighted Model Integration, In Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), 2015. [pdf]
[30]Paul Beame, Guy Van den Broeck, Eric Gribkoff, Dan Suciu. Symmetric Weighted First-Order Model Counting, In Proceedings of the 34th ACM Symposium on Principles of Database Systems (PODS), 2015. [pdf]

2014

[29]Eric Gribkoff, Dan Suciu, Guy Van den Broeck. Lifted probabilistic inference: A guide for the database researcher, In Bulletin of the Technical Committee on Data Engineering, volume 37, 2014. [pdf]
[28]Eric Gribkoff, Guy Van den Broeck, Dan Suciu. Understanding the complexity of lifted inference and asymmetric weighted model counting, In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI), 2014. [pdf]
[27]Doga Kisa, Guy Van den Broeck, Arthur Choi, Adnan Darwiche. Probabilistic sentential decision diagrams, In Proceedings of the 14th International Conference on Principles of Knowledge Representation and Reasoning (KR), 2014. [pdf]
[26]Mathias Niepert, Guy Van den Broeck. Tractability through exchangeability: A new perspective on efficient probabilistic inference, In Proceedings of the 28th AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, 2014. [pdf]
AAAI best paper award honorable mention
[25]Guy Van den Broeck, Wannes Meert, Adnan Darwiche. Skolemization for weighted first-order model counting, In Proceedings of the 14th International Conference on Principles of Knowledge Representation and Reasoning (KR), 2014. [pdf]
[24]Jonas Vlasselaer, Joris Renkens, Guy Van den Broeck, Luc De Raedt. Compiling probabilistic logic programs into sentential decision diagrams, In Workshop on Probabilistic Logic Programming (PLP), 2014. [pdf]
[23]Jan Van Haaren, Guy Van den Broeck, Wannes Meert, Jesse Davis. Tractable learning of liftable Markov logic networks, In Proceedings of the ICML-14 Workshop on Learning Tractable Probabilistic Models (LTPM), 2014. [pdf]
[22]Doga Kisa, Guy Van den Broeck, Arthur Choi, Adnan Darwiche. Probabilistic sentential decision diagrams: Learning with massive logical constraints, In ICML Workshop on Learning Tractable Probabilistic Models (LTPM), 2014. [pdf]
[21]Eric Gribkoff, Guy Van den Broeck, Dan Suciu. The most probable database problem, In Proceedings of the First International Workshop on Big Uncertain Data (BUDA), 2014. [pdf]
[20]Joris Renkens, Angelika Kimmig, Guy Van den Broeck, Luc De Raedt. Explanation-based approximate weighted model counting for probabilistic logics, In Proceedings of the 28th AAAI Conference on Artificial Intelligence, AAAI, 2014. [pdf]

2013

[19]Guy Van den Broeck, Adnan Darwiche. On the complexity and approximation of binary evidence in lifted inference, In Advances in Neural Information Processing Systems 26 (NIPS), 2013. [pdf]
Oral spotlight presentation, acceptance rate 72/1420 = 5%
[18]Guy Van den Broeck, Wannes Meert, Jesse Davis. Lifted generative parameter learning, In Statistical Relational AI (StaRAI) workshop, Bellevue, Washington, USA, 15 July 2013, 2013. [pdf]
[17]Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis, Hendrik Blockeel. Completeness results for lifted variable elimination, In Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics (AISTATS), JMLR Workshop and Conference Proceedings (Carlos M. Carvalho, Pradeep Ravikumar, eds.), 2013. [pdf]
[16]Guy Van den Broeck. Lifted Inference and Learning in Statistical Relational Models, PhD thesis, KU Leuven, 2013. [pdf]
ECCAI Artificial Intelligence Dissertation Award
Scientific prize IBM Belgium for Informatics

2012

[15]Joris Renkens, Dimitar Shterionov, Guy Van den Broeck, Jonas Vlasselaer, Daan Fierens, Wannes Meert, Gerda Janssens, Luc De Raedt. ProbLog2: From probabilistic programming to statistical relational learning, In Proceedings of the NIPS Probabilistic Programming Workshop, (Daniel Roy, Vikash Mansinghka, Noah Goodman, eds.), 2012. [pdf]
[14]Daan Fierens, Guy Van den Broeck, Maurice Bruynooghe, Luc De Raedt. Constraints for probabilistic logic programming, In Proceedings of the NIPS Probabilistic Programming Workshop, (Daniel Roy, Vikash Mansinghka, Noah Goodman, eds.), 2012. [pdf]
[13]Joris Renkens, Guy Van den Broeck, Siegfried Nijssen. k-optimal: A novel approximate inference algorithm for ProbLog, In Machine Learning, volume 89, 2012. [pdf]
ILP best student paper award
[12]Guy Van den Broeck, Arthur Choi, Adnan Darwiche. Lifted relax, compensate and then recover: From approximate to exact lifted probabilistic inference, In Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI) (Nando de Freitas, Kevin Murphy, eds.), 2012. [pdf]
[11]Guy Van den Broeck, Jesse Davis. Conditioning in first-order knowledge compilation and lifted probabilistic inference, In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, (Joerg Hoffmann, Bart Selman, eds.), AAAI Press, 2012. [pdf]
[10]Angelika Kimmig, Guy Van den Broeck, Luc De Raedt. Algebraic Model Counting, In CoRR, volume abs/1211.4475, 2012. [pdf]
[9]Manfred Jaeger, Guy Van den Broeck. Liftability of probabilistic inference: Upper and lower bounds, In Proceedings of the 2nd International Workshop on Statistical Relational AI,, 2012. [pdf]

2011

[8]Guy Van den Broeck. On the completeness of first-order knowledge compilation for lifted probabilistic inference, In Advances in Neural Information Processing Systems 24 (NIPS),, 2011. [pdf]
Oral full presentation, acceptance rate 20/1400 = 1.4%
[7]Guy Van den Broeck, Kurt Driessens. Automatic discretization of actions and states in Monte-Carlo tree search, In Proceedings of the ECML/PKDD 2011 Workshop on Machine Learning and Data Mining in and around Games, (Tom Croonenborghs, Kurt Driessens, Olana Missura, eds.), 2011. [pdf]
[6]Guy Van den Broeck, Nima Taghipour, Wannes Meert, Jesse Davis, Luc De Raedt. Lifted probabilistic inference by first-order knowledge compilation, In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI) (Toby Walsh, ed.), AAAI Press/International Joint Conferences on Artificial Intelligence, 2011. [pdf]
[5]Angelika Kimmig, Guy Van den Broeck, Luc De Raedt. An algebraic Prolog for reasoning about possible worlds, In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, (Wolfram Burgard, Dan Roth, eds.), AAAI Press, 2011. [pdf]
[4]Daan Fierens, Guy Van den Broeck, Ingo Thon, Bernd Gutmann, Luc De Raedt. Inference in probabilistic logic programs using weighted CNF's, In Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI), (Fabio Gagliardi Cozman, Avi Pfeffer, eds.), 2011. [pdf]
Oral full presentation, acceptance rate 24/285 = 8%

2010

[3]Guy Van den Broeck, Ingo Thon, Martijn van Otterlo, Luc De Raedt. DTProbLog: A decision-theoretic probabilistic Prolog, In Proceedings of the Twenty-fourth AAAI Conference on Artificial Intelligence, (Maria Fox, David Poole, eds.), AAAI Press, 2010. [pdf]

2009

[2]Guy Van den Broeck, Kurt Driessens, Jan Ramon. Monte-Carlo tree search in poker using expected reward distributions, In Proceedings of the 1st Asian Conference on Machine Learning (ACML), Lecture Notes in Computer Science, Springer, 2009. [pdf]
[1]Guy Van den Broeck. Algorithms and assessment in no-limit computer poker, Master's thesis, KU Leuven, 2009.
Alcatel-Lucent Innovation Award

Software

I have (co-)authored the following software, which is all available under an open source license.

Professional Service

Committees

  • Senior Program Committee member for IJCAI 2013, 2015.
  • Program Committee member for AAAI 2014, 2015, 2016; UAI 2015; ECML/PKDD 2013, 2014, 2015; KR 2014, 2016; ECAI 2014, ILP 2014, 2015; StarAI 2013, 2014; BUDA 2014; LTPM 2014; LML 2013; MLSA 2013.
  • Reviewer for NIPS 2014, 2015; AAAI 2010, 2012; POPL 2016; AISTATS 2016; SAT 2015; ECML 2009; Artificial Intelligence Journal (AIJ); Machine Learning Journal (MLJ); Journal of Machine Learning Research (JMLR); ACM Transactions on Database Systems (TODS); Encyclopedia of Social Network Analysis and Mining; Benelearn 2010.

Organization

2015
Co-organizer of the 5th International Workshop on Statistical Relational AI (StarAI).
2014
Co-organizer of the 4th International Workshop on Statistical Relational AI (StarAI) at the AAAI Conference on Artificial Intelligence, Québec City, Québec, Canada.

Teaching

2014
Co-Instructor: Selected Topics in Computer Science: Artificial Intelligence, H05N0A.
Guest Lecturer: Relational Probabilistic Models, I590, Indiana University, Bloomington
Guest Lecturer: Readings in Databases, CSE590Q, University of Washington, Seattle
2012-2013
Guest Lecturer: Automated Reasoning, CS264, University of California, Los Angeles
2009-2013
Teaching Assistant:
  • Uncertainty in Artificial Intelligence, H02D2A
  • Declarative Languages (Prolog, Haskell and Mercury), G0Q45, H04H5
  • Fundamentals of Computer Science, H01T3B
  • Problem Solving and Design, H01B9A
  • Innovation Lab (teaching visual programming to high school students)
2009
Guest Lecturer: Selected Topics in Computer Science: Artificial Intelligence, H05N0A, KU Leuven