Publications by Year

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2019
[98]	Yitao Liang and Guy Van den Broeck. Learning Logistic Circuits, In Proceedings of the 33rd Conference on Artificial Intelligence (AAAI), 2019.
2018
[97]	Tal Friedman and Guy Van den Broeck. Approximate Knowledge Compilation by Online Collapsed Importance Sampling, In Advances in Neural Information Processing Systems 31 (NIPS), 2018. Oral full presentation, acceptance rate 30/4856 = 0.6%
[96]	Yitao Liang and Guy Van den Broeck. Learning Logistic Circuits, In Proceedings of the UAI 2018 Workshop: Uncertainty in Deep Learning, 2018.
[95]	Tal Friedman and Guy Van den Broeck. Approximate Knowledge Compilation by Online Collapsed Importance Sampling, In Proceedings of the ICML Workshop on Tractable Probabilistic Models (TPM), 2018.
[94]	Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang and Guy Van den Broeck. A Semantic Loss Function for Deep Learning with Symbolic Knowledge, In Proceedings of the ICML Workshop on Tractable Probabilistic Models (TPM), 2018.
[93]	YooJung Choi and Guy Van den Broeck. On Robust Trimming of Bayesian Network Classifiers, In Proceedings of the ICML Workshop on Tractable Probabilistic Models (TPM), 2018.
[92]	Steven Holtzen, Guy Van den Broeck and Todd Millstein. Sound Abstraction and Decomposition of Probabilistic Programs, In Proceedings of the 35th International Conference on Machine Learning (ICML), 2018.
[91]	Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang and Guy Van den Broeck. A Semantic Loss Function for Deep Learning with Symbolic Knowledge, In Proceedings of the 35th International Conference on Machine Learning (ICML), 2018.
[90]	YooJung Choi and Guy Van den Broeck. On Robust Trimming of Bayesian Network Classifiers, In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), 2018.
[89]	Steven Holtzen, Guy Van den Broeck and Todd Millstein. Probabilistic Program Inference With Abstractions, In POPL 2018 Probabilistic Programming Languages, Semantics, and Systems Workshop, 2018.
2017
[88]	Jingyi Xu, Zilu Zhang, Tal Friedman, Yitao Liang and Guy Van den Broeck. A Semantic Loss Function for Deep Learning Under Weak Supervision, In NIPS 2017 Workshop on Learning with Limited Labeled Data: Weak Supervision and Beyond, 2017. LLD best paper award runner up
[87]	Shahroze Kabir, Frederic Sala, Guy Van den Broeck and Lara Dolecek. Coded Machine Learning: Joint Informed Replication and Learning for Linear Regression, In Proceedings of the 55th Annual Allerton Conference on Communication, Control, and Computing, 2017.
[86]	Frederic Sala, Shahroze Kabir, Lara Dolecek and Guy Van den Broeck. Don’t Fear the Bit Flips: Robust Linear Prediction Through Informed Channel Coding, In ICML 2017 Workshop on Reliable Machine Learning in the Wild, 2017.
[85]	Yitao Liang and Guy Van den Broeck. Towards Compact Interpretable Models: Shrinking of Learned Probabilistic Sentential Decision Diagrams, In IJCAI 2017 Workshop on Explainable Artificial Intelligence (XAI), 2017.
[84]	Seyed Mehran Kazemi, Angelika Kimmig, Guy Van den Broeck and David Poole. Domain Recursion for Lifted Inference with Existential Quantifiers, In Seventh International Workshop on Statistical Relational AI (StarAI), 2017.
[83]	Steven Holtzen, Todd Millstein and Guy Van den Broeck. Probabilistic Program Abstractions, In Seventh International Workshop on Statistical Relational AI (StarAI), 2017.
[82]	YooJung Choi, Adnan Darwiche and Guy Van den Broeck. Optimal Feature Selection for Decision Robustness in Bayesian Networks, In IJCAI 2017 Workshop on Logical Foundations for Uncertainty and Machine Learning, 2017.
[81]	Ismail Ilkan Ceylan, Adnan Darwiche and Guy Van den Broeck. Open-World Probabilistic Databases: An Abridged Report, In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), Sister Conference Best Paper Track, 2017.
[80]	Anna Latour, Behrouz Babaki, Anton Dries, Angelika Kimmig, Guy Van den Broeck and Siegfried Nijssen. Combining Stochastic Constraint Optimization and Probabilistic Programming: From Knowledge Compilation to Constraint Solving, In Proceedings of the 23rd International Conference on Principles and Practice of Constraint Programming (CP), 2017.
[79]	Steven Holtzen, Todd Millstein and Guy Van den Broeck. Probabilistic Program Abstractions, In Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI), 2017.
[78]	Guy Van den Broeck and Dan Suciu. Query Processing on Probabilistic Data: A Survey, Foundations and Trends in Databases, Now Publishers, 2017.
[77]	Yitao Liang, Jessa Bekker and Guy Van den Broeck. Learning the Structure of Probabilistic Sentential Decision Diagrams, In Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI), 2017. Oral full presentation, acceptance rate 29/289 = 10%
[76]	YooJung Choi, Adnan Darwiche and Guy Van den Broeck. Optimal Feature Selection for Decision Robustness in Bayesian Networks, In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017.
[75]	Frederic Sala, Shahroze Kabir, Guy Van den Broeck and Lara Dolecek. Don't Fear the Bit Flips: Optimized Coding Strategies for Binary Classification, In CoRR, volume abs/1703.02641, 2017.
2016
[74]	Seyed Mehran Kazemi, Angelika Kimmig, Guy Van den Broeck and David Poole. New Liftable Classes for First-Order Probabilistic Inference, In Advances in Neural Information Processing Systems 29 (NIPS), 2016.
[73]	Angelika Kimmig, Guy Van den Broeck and Luc De Raedt. Algebraic Model Counting, In International Journal of Applied Logic, 2016.
[72]	Kayvon Mazooji, Frederic Sala, Guy Van den Broeck and Lara Dolecek. Robust Channel Coding Strategies for Machine Learning Data, In Proceedings of the 54th Annual Allerton Conference on Communication, Control, and Computing, 2016.
[71]	Vaishak Belle, Guy Van den Broeck and Andrea Passerini. Hashing-Based Approximate Probabilistic Inference in Hybrid Domains: An Abridged Report, In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI), Sister Conference Best Paper Track, 2016.
[70]	Guy Van den Broeck. First-Order Model Counting in a Nutshell, In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI), Early Career Spotlight Track, 2016.
[69]	Jonas Vlasselaer, Guy Van den Broeck, Angelika Kimmig, Wannes Meert and Luc De Raedt. Tp-Compilation for Inference in Probabilistic Logic Programs, In International Journal of Approximate Reasoning, 2016.
[68]	Ismail Ilkan Ceylan, Adnan Darwiche and Guy Van den Broeck. Open-World Probabilistic Databases, In Proceedings of the 15th International Conference on Principles of Knowledge Representation and Reasoning (KR), 2016. KR best student paper award
[67]	Ismail Ilkan Ceylan, Adnan Darwiche and Guy Van den Broeck. Open World Probabilistic Databases (Extended Abstract), In Proceedings of the 29th International Workshop on Description Logics (DL), 2016.
[66]	Jonas Vlasselaer, Wannes Meert, Guy Van den Broeck and Luc De Raedt. Exploiting Local and Repeated Structure in Dynamic Bayesian Networks, In Artificial Intelligence, volume 232, 2016.
[65]	Vaishak Belle, Guy Van den Broeck and Andrea Passerini. Component Caching in Hybrid Domains with Piecewise Polynomial Densities, In Proceedings of the 30th Conference on Artificial Intelligence (AAAI), 2016.
[64]	Wannes Meert, Jonas Vlasselaer and Guy Van den Broeck. A Relaxed Tseitin Transformation for Weighted Model Counting, In International Workshop on Statistical Relational AI, 2016.
[63]	Babak Salimi, Leopoldo Bertossi, Dan Suciu and Guy Van den Broeck. Quantifying Causal Effects on Query Answering in Databases, In 8th USENIX Workshop on the Theory and Practice of Provenance (TaPP), USENIX Association, 2016.
2015
[62]	Jessa Bekker, Jesse Davis, Arthur Choi, Adnan Darwiche and Guy Van den Broeck. Tractable Learning for Complex Probability Queries, In Advances in Neural Information Processing Systems 28 (NIPS), 2015.
[61]	Daan Fierens, Guy Van den Broeck, Joris Renkens, Dimitar Shterionov, Bernd Gutmann, Ingo Thon, Gerda Janssens and Luc De Raedt. Inference and Learning in Probabilistic Logic Programs using Weighted Boolean Formulas, In Theory and Practice of Logic Programming, volume 15, 2015.
[60]	Bart Bogaerts and Guy Van den Broeck. Knowledge Compilation of Logic Programs Using Approximation Fixpoint Theory, In Theory and Practice of Logic Programming, volume 15, 2015.
[59]	Jonas Vlasselaer, Guy Van den Broeck, Angelika Kimmig, Wannes Meert and Luc De Raedt. Anytime Inference in Probabilistic Logic Programs with Tp-compilation, In Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), 2015.
[58]	Luc De Raedt, Anton Dries, Ingo Thon, Guy Van den Broeck and Mathias Verbeke. Inducing Probabilistic Relational Rules from Probabilistic Examples, In Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), 2015.
[57]	Guy Van den Broeck, Karthika Mohan, Arthur Choi, Adnan Darwiche and 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. Oral full presentation, acceptance rate 28/292 = 9%
[56]	Guy Van den Broeck. Towards High-Level Probabilistic Reasoning with Lifted Inference, In Proceedings of the AAAI Spring Symposium on KRR, 2015.
[55]	Guy Van den Broeck and Adnan Darwiche. On the Role of Canonicity in Knowledge Compilation, In Proceedings of the 29th Conference on Artificial Intelligence (AAAI), 2015.
[54]	Guy Van den Broeck and Mathias Niepert. Lifted Probabilistic Inference for Asymmetric Graphical Models, In Proceedings of the 29th Conference on Artificial Intelligence (AAAI), 2015.
[53]	Jan Van Haaren, Guy Van den Broeck, Wannes Meert and Jesse Davis. Lifted Generative Learning of Markov Logic Networks, In Machine Learning, volume 103, 2015.
[52]	Wannes Meert, Vincent Nys, Robin Theunis, Thomas Fannes, Monique Ingels, Tias Guns, Guy Van den Broeck, Kurt Driessens, Danny De Schreye and Marian Verhelst. Innovation Lab @ KU Leuven: Education, Engineering and Artificial Intelligence, In , 2015.
[51]	Anton Dries, Angelika Kimmig, Wannes Meert, Joris Renkens, Guy Van den Broeck, Jonas Vlasselaer and Luc De Raedt. ProbLog2: Probabilistic logic programming, In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Demo Track, 2015.
[50]	Arthur Choi, Guy Van den Broeck and Adnan Darwiche. Probability Distributions over Structured Spaces, In Proceedings of the AAAI Spring Symposium on KRR, 2015.
[49]	Arthur Choi, Guy Van den Broeck and 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.
[48]	Vaishak Belle, Guy Van den Broeck and Andrea Passerini. Hashing-Based Approximate Probabilistic Inference in Hybrid Domains, In Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI), 2015. UAI best paper award
[47]	Vaishak Belle, Andrea Passerini and 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.
[46]	Paul Beame, Guy Van den Broeck, Eric Gribkoff and Dan Suciu. Symmetric Weighted First-Order Model Counting, In Proceedings of the 34th ACM Symposium on Principles of Database Systems (PODS), 2015.
2014
[45]	Eric Gribkoff, Dan Suciu and 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.
[44]	Eric Gribkoff, Guy Van den Broeck and 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.
[43]	Doga Kisa, Guy Van den Broeck, Arthur Choi and Adnan Darwiche. Probabilistic sentential decision diagrams, In Proceedings of the 14th International Conference on Principles of Knowledge Representation and Reasoning (KR), 2014.
[42]	Wannes Meert, Guy Van den Broeck and Adnan Darwiche. Lifted inference for probabilistic logic programs, In Workshop on Probabilistic Logic Programming (PLP), 2014.
[41]	Mathias Niepert and 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. AAAI best paper award honorable mention
[40]	Guy Van den Broeck, Wannes Meert and 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.
[39]	Jonas Vlasselaer, Wannes Meert, Guy Van den Broeck and Luc De Raedt. Efficient probabilistic inference for dynamic relational models, 2014. International Workshop on Statistical Relational AI
[38]	Jonas Vlasselaer, Joris Renkens, Guy Van den Broeck and Luc De Raedt. Compiling probabilistic logic programs into sentential decision diagrams, In Workshop on Probabilistic Logic Programming (PLP), 2014.
[37]	Karthika Mohan, Guy Van den Broeck, Arthur Choi and Judea Pearl. An Efficient Method for Bayesian Network Parameter Learning from Incomplete Data, In ICML Workshop on Causal Modeling & Machine Learning, 2014.
[36]	Doga Kisa, Guy Van den Broeck, Arthur Choi and Adnan Darwiche. Probabilistic sentential decision diagrams: Learning with massive logical constraints, In ICML Workshop on Learning Tractable Probabilistic Models (LTPM), 2014.
[35]	Jan Van Haaren, Guy Van den Broeck, Wannes Meert and Jesse Davis. Tractable learning of liftable Markov logic networks, In Proceedings of the ICML-14 Workshop on Learning Tractable Probabilistic Models (LTPM), 2014.
[34]	Eric Gribkoff, Guy Van den Broeck and Dan Suciu. The most probable database problem, In Proceedings of the First International Workshop on Big Uncertain Data (BUDA), 2014.
[33]	Joris Renkens, Angelika Kimmig, Guy Van den Broeck and Luc De Raedt. Explanation-based approximate weighted model counting for probabilistic logics, In Proceedings of the 28th AAAI Conference on Artificial Intelligence, AAAI, 2014.
2013
[32]	Guy Van den Broeck and Adnan Darwiche. On the complexity and approximation of binary evidence in lifted inference, In Advances in Neural Information Processing Systems 26 (NIPS), 2013. Oral spotlight presentation, acceptance rate 72/1420 = 5%
[31]	Jan Van Haaren, Albrecht Zimmermann, Joris Renkens, Guy Van den Broeck, Tim Op De Beéck, Wannes Meert and Jesse Davis. Machine learning and data mining for sports analytics, 2013. LStat 25th Anniversary Scientific Event
[30]	Guy Van den Broeck, Wannes Meert and Jesse Davis. Lifted generative parameter learning, In Statistical Relational AI (StaRAI) workshop, 2013.
[29]	Guy Van den Broeck. On the complexity and approximation of binary evidence for lifted inference, In Proceedings of StaRAI, Statistical Relational AI workshop, Bellevue, Washington, USA, 2013.
[28]	Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis and Hendrik Blockeel. On the completeness of lifted variable elimination, In International Workshop on Statistical Relational AI (StarAI-13), Bellevue, Washington, 15 July 2013, 2013.
[27]	Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis and 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.
[26]	Guy Van den Broeck. Lifted Inference and Learning in Statistical Relational Models, PhD thesis, KU Leuven, 2013. ECCAI Artificial Intelligence Dissertation Award Scientific prize IBM Belgium for Informatics
2012
[25]	Daan Fierens, Guy Van den Broeck, Maurice Bruynooghe and Luc De Raedt. Constraints for probabilistic logic programming, In Proceedings of the NIPS Probabilistic Programming Workshop, (Daniel Roy, Vikash Mansinghka, Noah Goodman, eds.), 2012.
[24]	Wannes Meert, Guy Van den Broeck, Nima Taghipour, Daan Fierens, Hendrik Blockeel, Jesse Davis and Luc De Raedt. Lifted inference for probabilistic programming, In Proceedings of the NIPS Probabilistic Programming Workshop,, 2012.
[23]	Joris Renkens, Dimitar Shterionov, Guy Van den Broeck, Jonas Vlasselaer, Daan Fierens, Wannes Meert, Gerda Janssens and 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.
[22]	Nima Taghipour, Daan Fierens, Guy Van den Broeck, Jesse Davis and Hendrik Blockeel. Lifted Variable Elimination: A Novel Operator and Completeness Results, In ArXiv e-prints, 2012.
[21]	Joris Renkens, Guy Van den Broeck and Siegfried Nijssen. k-optimal: A novel approximate inference algorithm for ProbLog, In Machine Learning, volume 89, 2012. ILP best student paper award
[20]	Guy Van den Broeck, Arthur Choi and 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.
[19]	Guy Van den Broeck and 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.
[18]	Jan Van Haaren and Guy Van den Broeck. Relational learning for football-related predictions, In Proceedings of the 21st Belgian-Dutch Conference on Machine Learning, 2012.
[17]	Angelika Kimmig, Guy Van den Broeck and Luc De Raedt. Algebraic Model Counting, In CoRR, volume abs/1211.4475, 2012.
[16]	Manfred Jaeger and Guy Van den Broeck. Liftability of probabilistic inference: Upper and lower bounds, In Proceedings of the 2nd International Workshop on Statistical Relational AI,, 2012.
2011
[15]	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. Oral full presentation, acceptance rate 20/1400 = 1.4%
[14]	Guy Van den Broeck and 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.
[13]	Guy Van den Broeck, Nima Taghipour, Wannes Meert, Jesse Davis and 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.
[12]	Jan Van Haaren and Guy Van den Broeck. Relational learning for football-related predictions, In Preliminary Papers ILP, 2011.
[11]	Joris Renkens, Guy Van den Broeck and Siegfried Nijssen. k-Optimal: A novel approximate inference algorithm for ProbLog, In Preliminary Papers ILP, 2011.
[10]	Davide Nitti, Guy Van den Broeck and Luc De Raedt. Probabilistic logic in dynamic domains: Particle filter with distributional clauses, In Preliminary Papers ILP, 2011.
[9]	Angelika Kimmig, Bernd Gutmann, Theofrastos Mantadelis, Guy Van den Broeck, Vitor Santos Costa, Gerda Janssens and Luc De Raedt. ProbLog, Association for Logic Programming, 2011. ALP Newsletter
[8]	Angelika Kimmig, Guy Van den Broeck and 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.
[7]	Daan Fierens, Guy Van den Broeck, Ingo Thon, Bernd Gutmann and 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. Oral full presentation, acceptance rate 24/285 = 8%
2010
[6]	Guy Van den Broeck, Ingo Thon, Martijn van Otterlo and 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.
[5]	Ingo Thon, Bernd Gutmann and Guy Van den Broeck. Probabilistic programming for planning problems, In Statistical Relational AI workshop (Kristian Kersting, Stuart Russell, Leslie Pack Kaelbling, Alon Halevy, Sriraam Natarajan, Lilyana Mihalkova, eds.), 2010.
2009
[4]	Guy Van den Broeck, Kurt Driessens and Jan Ramon. Monte-Carlo tree search in poker using expected reward distributions, In Proceedings of the 21st Benelux Conference on Artificial Intelligence (BNAIC) (Toon Calders, Karl Tuyls, Mykola Pechenizkiy, eds.), 2009.
[3]	Guy Van den Broeck, Kurt Driessens and 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.
[2]	Maurice Bruynooghe, Broes De Cat, Jochen Drijkoningen, Daan Fierens, Jan Goos, Bernd Gutmann, Angelika Kimmig, Wouter Labeeuw, Steven Langenaken, Niels Landwehr, Wannes Meert, Ewoud Nuyts, Robin Pellegrims, Roel Rymenants, Stefan Segers, Ingo Thon, Jelle Van Eyck, Guy Van den Broeck, Tine Vangansewinkel, Lucie Van Hove, Joost Vennekens, Timmy Weytjens and Luc De Raedt. An exercise with statistical relational learning systems, In International Workshop on Statistical Relational Learning (Pedro Domingos, Kristian Kersting, eds.), 2009.
[1]	Guy Van den Broeck. Algorithms and assessment in no-limit computer poker, Master's thesis, KU Leuven, 2009. Alcatel-Lucent Innovation Award