[. Atamtürk, . Johnson, M. Linderoth, and . Savelsbergh, A Relational Modeling System for Linear and Integer Programming, Operations Research, vol.48, issue.6, pp.846-857, 2000.
DOI : 10.1287/opre.48.6.846.12388

K. [. Apsel, M. Kersting, and . Mladenov, Lifting relational map-lps using cluster signatures, Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI), 2014.

B. Ahmadi, K. Kersting, M. Mladenov, and S. Natarajan, Exploiting symmetries for scaling loopy belief propagation and relational training, Machine Learning, vol.2, issue.2, pp.91-132112, 2013.
DOI : 10.1007/s10994-013-5385-0

K. Ravindra, . Ahuja, L. Thomas, J. B. Magnanti, and . Orlin, Network flows: theory, algorithms , and applications, 1993.

A. Airola, S. Pyysalo, J. Björne, T. Pahikkala, F. Ginter et al., All-paths graph kernel for protein-protein interaction extraction with evaluation of crosscorpus learning, BMC bioinformatics, issue.9 11, p.2, 2008.

W. [. Ataman, Y. Street, and . Zhang, Learning to rank by maximizing auc with linear programming, Proceedings of the International Joint Conference on Neural Networks (IJCNN), pp.123-129, 2006.

J. Bi, K. P. Bennett, M. J. Embrechts, C. M. Breneman, and M. Song, Dimensionality reduction via sparse support vector machines, Journal of Machine Learning Research, vol.3, pp.1229-1243, 2003.

P. [. Berkholz, M. Bonsma, and . Grohe, Tight Lower and Upper Bounds for the Complexity of Canonical Colour Refinement, Proceedings of the 21st Annual European Symposium on Algorithms (SEA), pp.145-156, 2013.
DOI : 10.1007/s00224-016-9686-0

T. [. Bödi, K. Grundhöfer, and . Herr, Symmetries of linear programs, Note di Matematica, vol.30, issue.1, pp.129-132, 2010.

K. [. Bödi, M. Herr, and . Joswig, Algorithms for highly symmetric linear and integer programs, Mathematical Programming, vol.8, issue.4, pp.65-90, 2013.
DOI : 10.1007/s10107-011-0487-6

T. [. Bui, S. Huynh, and . Riedel, Automorphism groups of graphical models and lifted variational inference, Proc. of the 29th Conference on Uncertainty in Artificial Intelligence (UAI-2013), 2013.

M. Karsten, H. Borgwardt, and . Kriegel, Shortest-path kernels on graphs, Data Mining, Fifth IEEE International Conference on, p.8, 2005.

. A. Bkm92, D. Brooke, A. Kendrick, and . Meeraus, GAMS: A User's Guide, 1992.

M. [. Berthold and . Pfetsch, Detecting Orbitopal Symmetries, Operations Research Proceedings, pp.433-438, 2008.
DOI : 10.1007/978-3-642-00142-0_70

Y. [. Ciriani, S. Colombani, and . Heipcke, Embedding optimisation algorithms with Mosel, Quarterly Journal of the Belgian, French and Italian Operations Research Societies, vol.1, issue.2, pp.155-167, 2003.
DOI : 10.1007/s10288-003-0014-6

B. [. Chakrabarti, P. Dom, and . Indyk, Enhanced hypertext categorization using hyperlinks, Proceedings ACM SIGMOD International Conference on Management of Data (SIGMOD), pp.307-318, 1998.

T. [. Conitzer and . Sandholm, Computing the optimal strategy to commit to, Proceedings of the 7th ACM conference on Electronic commerce , EC '06, pp.82-90, 2006.
DOI : 10.1145/1134707.1134717

K. [. Demiriz, J. Bennett, and . Shawe-taylor, Linear programming boosting via column generation, Machine Learning, pp.225-254, 2002.

A. Demiriz, P. Kristin, J. Bennett, and . Shawe-taylor, Linear programming boosting via column generation, Machine Learning, pp.225-254, 2002.

R. [. Dean and . Givan, Model minimization in markov decision processes, Proc. of the Fourteenth National Conference on Artificial Intelligence (AAAI-97), pp.106-111, 1997.

G. Dantzig, M. [. Thapa, J. Fragniere, and . Gondzio, Linear Programming 2: Theory and Extensions Optimization modeling languages, Handbook of Applied Optimization, pp.993-1007, 2002.

[. Fourer, M. David, . Gay, W. Brian, and . Kernighan, AMPL: A Mathematical Programing Language, 1993.
DOI : 10.1007/978-3-642-83724-1_12

]. D. Fkd-+-12, K. Fierens, J. Kersting, J. Davis, M. Chen et al., Pairwise markov logic, Proceedings of the 22nd International Conference (ILP), pp.58-73, 2012.

]. P. Fla94 and . Flach, Simply logical -intelligent reasoning by example, 1994.

T. [. Farrell and . Maness, A relational database approach to a linear programming-based decision support system for production planning in secondary wood product manufacturing, Decision Support Systems, vol.40, issue.2, pp.183-196, 2005.
DOI : 10.1016/j.dss.2004.02.001

T. Gärtner, Exponential and geometric kernels for graphs, NIPS Workshop on Unreal Data: Principles of Modeling Nonvectorial Data, pp.49-58, 2002.

]. H. Gef14 and . Geffner, Artificial intelligence: From programs to solvers, AI Commun, vol.27, issue.1, pp.45-51, 2014.

S. [. Gordon, M. Hong, and . Dudík, First-order mixed integer linear programming, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI), pp.213-222, 2009.

M. Grohe, K. Kersting, M. Mladenov, and E. Selman, Dimension Reduction via Colour Refinement, 2013.
DOI : 10.1007/978-3-662-44777-2_42

S. [. Guns, L. Nijssen, and . De-raedt, Itemset mining: A constraint programming perspective, Artificial Intelligence, vol.175, issue.12-13, pp.12-131951, 2011.
DOI : 10.1016/j.artint.2011.05.002

]. C. God97, Compact graphs and equitable partitions, Linear Algebra Appl, vol.255, pp.259-266, 1997.

G. [. Godsil and . Royle, Algebraic Graph Theory, 2001.
DOI : 10.1007/978-1-4613-0163-9

L. Getoor and B. Taskar, Introduction to Statistical Relational Learning, 2007.

D. Haussler, Convolution kernels on discrete structures, 1999.

[. Horváth, T. Gärtner, and S. Wrobel, Cyclic pattern kernels for predictive graph mining, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, pp.158-167, 2004.
DOI : 10.1145/1014052.1014072

B. [. Kersting, S. Ahmadi, and . Natarajan, Counting Belief Propagation, Proc. of the 25th Conf. on Uncertainty in Artificial Intelligence (UAI?09), 2009.

T. Klein, U. Brefeld, and T. Scheffer, Exact and Approximate Inference for Annotating Graphs with Structural SVMs, Proceedings of ECML/PKDD, Part 1), pp.611-623, 2008.
DOI : 10.1007/978-3-540-87479-9_58

]. K. Ker12 and . Kersting, Lifted probabilistic inference, Proceedings of ECAI-2012KPT08] Nikos Komodakis, Nikos Paragios, and Georgios Tziritas. Clustering via lp-based stabilities. In NIPS, pp.865-872, 2008.

]. C. Kui93 and . Kuip, Algebraic languages for mathematical programming, European Journal of Operation Research, vol.67, pp.25-51

T. [. Littman, L. Dean, . Pack, and . Kaelbling, On the complexity of solving markov decision problems, Proc. of the 11th International Conference on Uncertainty in Artificial Intelligence (UAI-95), pp.394-402, 1995.

T. [. Littman, L. Dean, . Pack, and . Kaelbling, On the complexity of solving markov decision problems, roceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence (UAI), pp.394-402, 1995.

J. Lloyd, Foundations of Logic Programmin, 1987.

M. Mladenov, B. Ahmadi, K. Kerstingmar10, and ]. F. Margot, Lifted linear programming Symmetry in integer linear programming, International Conference on Artificial Intelligence and Statistics 50 Years of Integer Programming From the Early Years to the State-of-the-Art, pp.788-797, 1958.

S. [. Mattingley and . Boyd, CVXGEN: a code generator for embedded convex optimization, Optimization and Engineering, vol.14, issue.6, pp.1-27, 2012.
DOI : 10.1007/s11081-011-9176-9

]. G. Mdsmk95, C. Mitra, S. Luca-dn, B. Moody, and . Kristjanssonl, Sets and indices in linear programming modelling and their integration with relational data models, Computational Optimization and Applications, vol.4, pp.263-283, 1995.

A. [. Mladenov, K. Globerson, and . Kersting, Efficient lifting of map lp relaxations using k-locality, 17th Int. Conf. on Artificial Intelligence and Statistics, p.2014, 2014.

]. N. Nil86 and . Nilsson, Probabilistic logic, Artificial Intelligence, vol.28, issue.1, pp.71-87, 1986.

J. Neville and D. Jensen, Iterative classification in relational data, Proc. AAAI- 2000 Workshop on Learning Statistical Models from Relational Data, pp.13-20, 2000.

J. Neville and D. Jensen, Collective classification with relational dependency networks, Proceedings of the Second International Workshop on Multi-Relational Data Mining, pp.77-91, 2003.

J. Neville and D. Jensen, Relational dependency networks, The Journal of Machine Learning Research, vol.8, pp.653-692, 2007.

J. Noessner, M. Niepert, and H. Stuckenschmidt, Rockit: Exploiting parallelism and symmetry for map inference in statistical relational models, Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013.

Y. Andrew, . Ng, J. Stuart, and . Russell, Algorithms for inverse reinforcement learning, Icml, pp.663-670, 2000.

B. [. Narayanamurthy and . Ravindran, On the hardness of finding symmetries in Markov decision processes, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.688-695, 2008.
DOI : 10.1145/1390156.1390243

F. Niu, C. Ré, A. Doan, and J. Shavlik, Tuffy, Proceedings of the VLDB Endowment, pp.373-384, 2011.
DOI : 10.14778/1978665.1978669

]. D. Poo03 and . Poole, First-order probabilistic inference, Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI), pp.985-991, 2003.

A. [. Ravindran and . Barto, Symmetries and model minimization in markov decision processes, 2001.

M. Richardson and P. Domingos, Markov logic networks, Markov logic networks, pp.107-136, 2006.
DOI : 10.1007/s10994-006-5833-1

K. [. De-raedt and . Kersting, Statistical relational learning, Encyclopedia of Machine Learning, pp.916-924, 2010.

E. [. Ramana, D. Scheinerman, and . Ullman, Fractional isomorphism of graphs, Discrete Mathematics, vol.132, issue.1-3, pp.247-265, 1994.
DOI : 10.1016/0012-365X(94)90241-0

[. Sandler, On the use of linear programming for unsupervised text classification, Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining , KDD '05, pp.256-264, 2005.
DOI : 10.1145/1081870.1081901

A. [. Sutton, . Bartosb09-]-s, C. Sanner, and . Boutilier, Reinforcement Learning: An Introduction Practical solution techniques for first-order mdps, Artif. Intell, vol.173, pp.5-6748, 1998.
DOI : 10.1007/978-1-4615-3618-5

[. Syed, M. Bowling, E. Robert, and . Schapire, Apprenticeship learning using linear programming, Proceedings of the 25th international conference on Machine learning, ICML '08, pp.1032-1039, 2008.
DOI : 10.1145/1390156.1390286

P. [. Singla and . Domingos, Lifted First-Order Belief Propagation, Proc. of the 23rd AAAI Conf. on Artificial Intelligence (AAAI-08), pp.1094-1099, 2008.

J. Suzuki, T. Hirao, Y. Sasaki, and E. Maeda, Hierarchical directed acyclic graph kernel, Proceedings of the 41st Annual Meeting on Association for Computational Linguistics , ACL '03, pp.32-39, 2003.
DOI : 10.3115/1075096.1075101

. Snb-+-08a-]-p, G. Sen, M. Namata, L. Bilgic, B. Getoor et al., Collective classification in network data, AI Magazine, vol.29, issue.3, pp.93-106, 2008.

G. Snb-+-08b-]-prithviraj-sen, M. Mark-namata, L. Bilgic, B. Getoor, T. Gallagher et al., Collective classification in network data Sellmann and P. Van Hentenryck. Structural symmetry breaking, Proc. of 19th International Joint Conference on Artificial Intelligence (IJCAI-05), pp.93-106, 2005.

D. [. Torkamani and . Lowd, Convex adversarial collective classification, Proceedings of the 30th International Conference on Machine Learning (ICML) (1), pp.642-650, 2013.

N. Vladimir and . Vapnik, Statistical learning theory (adaptive and learning systems for signal processing , communications and control series, 1998.

J. Martin, . Wainwright, I. Michael, and . Jordan, Graphical models, exponential families, and variational inference, Foundations and Trends R in Machine Learning, vol.1, issue.12, pp.1-305, 2008.

J. [. Wang and . Shawe-taylor, Large-margin structured prediction via linear programming, 12th Int. Conf. on Artificial Intelligence and Statistics, pp.599-606, 2009.

G. [. Zawadzki, A. Gordon, and . Platzer, An instantiation-based theorem prover for firstorder programming, Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS) JMLR Proceedings, pp.855-863, 2011.

[. Zhou, L. Zhang, and L. Jiao, Linear programming support vector machines, Pattern Recognition, vol.35, issue.12, pp.2927-2936, 2002.
DOI : 10.1016/S0031-3203(01)00210-2