PPT-Unconstrained Submodular

Unconstrained Submodular Maximization Moran Feldman The Open University of Israel Based On Maximizing Nonmonotone Submodular Functions Uriel Feige Vahab S Mirrokni and Jan Vondrák SIAM J. umassedu Erik LearnedMiller University of Massachusetts Amherst Amherst MA 01003 elmcsumassedu Abstract Despite the maturity of face detection research it re mains dif64257cult to compare different algorithms for face de tection This is partly due to umdedu Yi Li NICTA Australia yilinictacomau Cornelia Ferm uller University of Maryland ferumiacsumdedu Yiannis Aloimonos University of Maryland yianniscsumdedu Abstract In order to advance action generation and creation in robots beyond simple learne As is convex and di64256erentiable min 0 Sometimes the latter equation may be solved analytically Eg if Ax then the optimal solution has Ax 0 Rudi Pendavingh TUE Unconstrained minimization of selfconcordant functions NLO9 2 10 brPage 3br It Submodular. Bandits Problem. Yisong Yue. Carnegie Mellon University. Joint work with Carlos Guestrin & Sue Ann Hong. Optimizing Recommender Systems. Must predict what the user finds interesting. 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. Unconstrained (TRU) strategy is celebrating its 10-year anniversary. This gives Western Asset one of the longest track records in the growing unconstrained space. At the time of TRU’s inception, 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. . for Binary . Pairwise. Energies. Lena . Gorelick. . joint work with. . O. . Veksler. I. Ben . Ayed. A. Delong. . Y. . Boykov. Example of Simple Binary Energy. 2. Potts Model. Binary . Pairwise. (x) = 0. h. i. (x) <= 0. Objective function. Equality constraints. Inequality constraints. Terminology. Feasible set. Degrees of freedom. Active constraint. classifications. Unconstrained v. constrained. Unconstrained minimization. Steepest descent vs. conjugate gradients. Newton and quasi-Newton methods. Matlab. . fminunc. Unconstrained local minimization. The necessity for one dimensional searches. 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. Unconstrained minimization. Steepest descent vs. conjugate gradients. Newton and quasi-Newton methods. Matlab. . fminunc. Unconstrained local minimization. The necessity for one dimensional searches. 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.