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Workshop: Negative Dependence and Submodularity: Theory and Applications in Machine Learning
Poster session
Abstract:
Submodular maximization
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- Online Algorithms for Budget-Constrained DR-Submodular Maximization, Omid Sadeghi, Reza Eghbali, Maryam Fazel
- Constrained Maximization of Lattice Submodular Functions, Aytunc Sahin, Joachim Buhmann, Andreas Krause
- Mode Finding for SLC Distributions via Regularized Submodular Maximization, Ehsan Kazemi, Shervin Minaee, Moran Feldman, Amin Karbasi
Determinantal point processes
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- MO-PaDGAN: Generating Diverse Designs with Multivariate Performance Enhancement, Wei Chen, Faez Ahmed
- On the Relationship Between Probabilistic Circuits and Determinantal Point Processes, Honghua Zhang, Steven J Holtzen, Guy Van den Broeck
- Ensemble Kernel Methods, Implicit Regularization and Determinantal Point Processes, Joachim Schreurs, Michaƫl Fanuel, Johan Suykens
Negative dependence for inference and bipartite matching
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- DisARM: An Antithetic Gradient Estimator for Binary Latent Variables, Zhe Dong, Andriy Mnih, George Tucker
- On Diverse Bipartite b-Matching, Saba Ahmadi, Faez Ahmed, John P Dickerson, Mark Fuge, Samir Khuller
- Negative Dependence Tightens Variational Bounds, Pierre-Alexandre Mattei, Jes Frellsen
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