Milan Studený

66 papers A 6C 6Journal 45Unranked 5

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Self-adhesivity in lattices of abstract conditional independence models. Discret. Appl. Math. Tobias Boege, Janneke H. Bolt, Milan Studený
2024	J	`jnl`	On combinatorial descriptions of faces of the cone of supermodular functions. CoRR Milan Studený
2024	J	`jnl`	Self-adhesivity in lattices of abstract conditional independence models. CoRR Tobias Boege, Janneke H. Bolt, Milan Studený
2022	J	`jnl`	Algorithmic Aspects of Information Theory (Dagstuhl Seminar 22301). Dagstuhl Reports Phokion G. Kolaitis, Andrei E. Romashchenko, Milan Studený, Dan Suciu, Tobias Boege
2022	J	`jnl`	Facets of the cone of exact games. Math. Methods Oper. Res. Milan Studený, Václav Kratochvíl
2021	J	`jnl`	Conditional Independence Structures Over Four Discrete Random Variables Revisited: Conditional Ingleton Inequalities. IEEE Trans. Inf. Theory Milan Studený
2021	J	`jnl`	Facets of the cone of exact games. CoRR Milan Studený, Václav Kratochvíl
2021	J	`jnl`	The dual polyhedron to the chordal graph polytope and the rebuttal of the chordal graph conjecture. Int. J. Approx. Reason. Milan Studený, James Cussens, Václav Kratochvíl
2020	J	`jnl`	Conditional independence structures over four discrete random variables revisited: conditional Ingleton inequalities. CoRR Milan Studený
2020	J	`jnl`	Contribution of Frantisek Matus to the research on conditional independence. Kybernetika Milan Studený
2020	—	`conf`	Dual Formulation of the Chordal Graph Conjecture. PGM Milan Studený, James Cussens, Václav Kratochvíl
2020	J	`jnl`	Special issue in memory of František Matúš. Kybernetika Nihat Ay, Milan Studený
2019	J	`jnl`	Facets of the cone of totally balanced games. Math. Methods Oper. Res. Tomás Kroupa, Milan Studený
2019	C	`conf`	On Irreducible Min-Balanced Set Systems. ECSQARU Milan Studený, Václav Kratochvíl, Jirí Vomlel
2018	—	`ed.`	International Conference on Probabilistic Graphical Models, PGM 2018, 11-14 September 2018, Prague, Czech Republic PGM Milan Studený, Václav Kratochvíl
2018	J	`jnl`	Linear criterion for testing the extremity of an exact game based on its finest min-representation. Int. J. Approx. Reason. Milan Studený, Václav Kratochvíl
2018	—	`conf`	Proceedings of the 9th International Conference on Probabilistic Graphical Models. PGM Václav Kratochvíl, Milan Studený
2017	—	`conf`	Linear Core-Based Criterion for Testing Extreme Exact Games. ISIPTA Milan Studený, Václav Kratochvíl
2017	J	`jnl`	Polyhedral aspects of score equivalence in Bayesian network structure learning. Math. Program. James Cussens, David Haws, Milan Studený
2017	J	`jnl`	Towards using the chordal graph polytope in learning decomposable models. Int. J. Approx. Reason. Milan Studený, James Cussens
2016	J	`jnl`	Core-based criterion for extreme supermodular functions. Discret. Appl. Math. Milan Studený, Tomás Kroupa
2016	—	`conf`	The Chordal Graph Polytope for Learning Decomposable Models. Probabilistic Graphical Models Milan Studený, James Cussens
2015	A	`conf`	How matroids occur in the context of learning Bayesian network structure. UAI Milan Studený
2014	J	`jnl`	Learning Bayesian network structure: Towards the essential graph by integer linear programming tools. Int. J. Approx. Reason. Milan Studený, David Haws
2013	J	`jnl`	Bayesian Networks from the Point of View of Chain Graphs CoRR Milan Studený
2013	J	`jnl`	On Separation Criterion and Recovery Algorithm for Chain Graphs CoRR Milan Studený
2013	J	`jnl`	On characterizing Inclusion of Bayesian Networks CoRR Tomas Kocka, Remco R. Bouckaert, Milan Studený
2013	J	`jnl`	Semigraphoids Are Two-Antecedental Approximations of Stochastic Conditional Independence Models CoRR Milan Studený
2012	J	`jnl`	Characteristic imsets for learning Bayesian network structure. Int. J. Approx. Reason. Raymond Hemmecke, Silvia Lindner, Milan Studený
2011	J	`jnl`	On open questions in the geometric approach to structural learning Bayesian nets. Int. J. Approx. Reason. Milan Studený, Jirí Vomlel
2010	J	`jnl`	A geometric view on learning Bayesian network structures. Int. J. Approx. Reason. Milan Studený, Jirí Vomlel, Raymond Hemmecke
2010	J	`jnl`	Efficient Algorithms for Conditional Independence Inference. J. Mach. Learn. Res. Remco R. Bouckaert, Raymond Hemmecke, Silvia Lindner, Milan Studený
2010	J	`jnl`	Learning restricted Bayesian network structures CoRR Raymond Hemmecke, Silvia Lindner, Milan Studený
2009	J	`jnl`	A reconstruction algorithm for the essential graph. Int. J. Approx. Reason. Milan Studený, Jirí Vomlel
2009	J	`jnl`	Two Operations of Merging and Splitting Components in a Chain Graph. Kybernetika Milan Studený, Alberto Roverato, Sárka Stepánová
2008	J	`jnl`	Editorial Note. Int. J. Approx. Reason. Milan Studený, Jirí Vomlel
2007	J	`jnl`	Comparison of two methods for approximation of probability distributions with prescribed marginals. Kybernetika Albert Perez, Milan Studený
2007	J	`jnl`	Racing algorithms for conditional independence inference. Int. J. Approx. Reason. Remco R. Bouckaert, Milan Studený
2006	J	`jnl`	A Graphical Representation of Equivalence Classes of AMP Chain Graphs. J. Mach. Learn. Res. Alberto Roverato, Milan Studený
2006	—	`ed.`	Third European Workshop on Probabilistic Graphical Models, 12-15 September 2006, Prague, Czech Republic. Electronic Proceedings. Probabilistic Graphical Models Milan Studený, Jirí Vomlel
2005	J	`jnl`	Characterization of inclusion neighbourhood in terms of the essential graph. Int. J. Approx. Reason. Milan Studený
2005	—	`book`	Probabilistic conditional independence structures. Milan Studený
2005	C	`conf`	Racing for Conditional Independence Inference. ECSQARU Remco R. Bouckaert, Milan Studený
2004	J	`jnl`	Characterization Of Essential Graphs By Means Of The Operation Of Legal Merging Of Components. Int. J. Uncertain. Fuzziness Knowl. Based Syst. Milan Studený
2003	C	`conf`	Characterization of Inclusion Neighbourhood in Terms of the Essential Graph: Uper Neighbours. ECSQARU Milan Studený
2002	—	`conf`	Characterization of Essential Graphs by Means of an Operation of Legal Component Merging. Probabilistic Graphical Models Milan Studený
2002	J	`jnl`	On Stochastic Conditional Independence: the Problems of Characterization and Description. Ann. Math. Artif. Intell. Milan Studený
2001	A	`conf`	On characterizing Inclusion of Bayesian Networks. UAI Tomás Kocka, Remco R. Bouckaert, Milan Studený
2000	J	`jnl`	Representation of Irrelevance Relations by Annotated Graphs. Fundam. Informaticae Azaria Paz, Robert Y. Geva, Milan Studený
1999	J	`jnl`	A graphical characterization of the largest chain graphs. Int. J. Approx. Reason. Martin Volf, Milan Studený
1999	A	`conf`	Conditional products: An alternative approach to conditional independence. AISTATS A. Philip Dawid, Milan Studený
1998	A	`conf`	Bayesian Networks from the Point of View of Chain Graphs. UAI Milan Studený
1998	—	`ch.`	The Multiinformation Function as a Tool for Measuring Stochastic Dependence. Learning in Graphical Models Milan Studený, Jirina Vejnarová
1997	J	`jnl`	A recovery algorithm for chain graphs. Int. J. Approx. Reason. Milan Studený
1997	J	`jnl`	Semigraphoids and Structures of Probabilistic Conditional Independence. Ann. Math. Artif. Intell. Milan Studený
1996	A	`conf`	On Separation Criterion and Recovery Algorithm for Chain Graphs. UAI Milan Studený
1995	C	`conf`	Chain graphs: semantics and expressiveness. ECSQARU Remco R. Bouckaert, Milan Studený
1995	J	`jnl`	Conditional Independences among Four Random Variables 1. Comb. Probab. Comput. Frantisek Matús, Milan Studený
1995	J	`jnl`	Conditional independence and natural conditional functions. Int. J. Approx. Reason. Milan Studený
1994	C	`conf`	Marginal Problem in Different Calculi of AI. IPMU Milan Studený
1994	A	`conf`	Semigraphoids Are Two-Antecedental Approximations of Stochastic Conditional Independence Models. UAI Milan Studený
1993	J	`jnl`	Convex cones in finite-dimensional real vector spaces. Kybernetika Milan Studený
1993	C	`conf`	Formal Properties of Conditional Independence in Different Calculi of AI. ECSQARU Milan Studený
1992	J	`jnl`	Comment on "A unique formal system for binary decompositions of database relations, probability distributions, and graphs". Inf. Sci. Francesco M. Malvestuto, Milan Studený
1989	J	`jnl`	Attempts at axiomatic description of conditional independence. Kybernetika Milan Studený
1987	J	`jnl`	Asymptotic behaviour of empirical multiinformation. Kybernetika Milan Studený