PPT-Learning Submodular Functions

TexPoint fonts used in EMF Read the TexPoint manual before you delete this box A A A A A A A A Maria Florina Balcan LGO 11162010 fSfT fS Å T fS T . washingtonedu Jeff Bilmes Department of Electrical Engineering University of Washington bilmesuwashingtonedu Abstract We investigate two new optimization problems minimizing a submodular function subject to a submodular lower bound constraint submod Bayesian Submodular Models. Josip . Djolonga. joint work with Andreas Krause. Motivation. inference with higher order potentials. MAP Computation . ✓. Inference? . ✘. We provide a method for inference in such models. A Mini-Survey. Chandra . Chekuri. Univ. of Illinois, Urbana-Champaign. Submodular Set Functions. A function . f. : 2. N. . . . R . is submodular if. . f(A. ) + . f(B. ) ≥ . f(A. . B. ) + . . Tripathi. Georgia Institute of Technology. Approximability. of Combinatorial Optimization Problems with . Submodular. Cost Functions. Based on joint work with . Gagan. . Goel. , . Chinmay. . Karande. on . approximation. of . submodular. and XOS . functions by juntas. Vitaly. Feldman and Jan . Vondrâk. IBM . Research - . Almaden. Prelims:. Submodular. and XOS. Functions; . Learning. $20. $27. Submodular. Functions. Grigory. . Yaroslavtsev. Columbia University. October 26, 2012. With . Sofya. . Raskhodnikova. (SODA’13). + Work in progress with Rocco . Servedio. Submodularity. Discrete analog of convexity/concavity, law . A Mini-Survey. Chandra . Chekuri. Univ. of Illinois, Urbana-Champaign. Submodular Set Functions. A function . f. : 2. N. . . . R . is submodular if. . f(A. ) + . f(B. ) ≥ . f(A. . B. ) + . general . submodular. functions. CVPR 2015 . Tutorial. Stefanie Jegelka. MIT. The set function view. 2. cost. of buying items . together, or. utility, . or. probability, …. (. . ). submodular. . objectives: . continuous extensions . &. . dependent . randomized . rounding. Chandra . Chekuri. Univ. of Illinois, Urbana-Champaign. Combinatorial Optimization. N. a finite ground set. Dictionary Selection. for Sparse Representation. Volkan . Cevher. volkan.cevher@epfl.ch. Laboratory. for Information . . and Inference Systems - . LIONS. & . Idiap. Research Institute. . Joint work with . of . submodular. and XOS . functions by juntas. Vitaly. Feldman and Jan . Vondrâk. IBM . Research - . Almaden. Prelims:. Submodular. and XOS. Functions; . Learning. $20. $27. Approximation by juntas. OO. L 2. 0. 12 KY. O. T. O. Briefing & Report. By: Masayuki . Kouno. . (D1) & . Kourosh. . Meshgi. . (D1). Kyoto University, Graduate School of Informatics, Department of Systems Science. Ishii Lab (Integrated System Biology). OO. L 2. 0. 12 KY. O. T. O. Briefing & Report. By: Masayuki . Kouno. . (D1) & . Kourosh. . Meshgi. . (D1). Kyoto University, Graduate School of Informatics, Department of Systems Science. Ishii Lab (Integrated System Biology). Approximability. of Combinatorial Optimization Problems with . Submodular. Cost Functions. Based on joint work with . Gagan. . Goel. , . Chinmay. . Karande. , and Wang Lei. Motivation . Network Design Problem.