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From sets to multisets: provable variational inference for probabilistic integer submodular models
Submodular functions have been studied extensively in machine learning and data mining.
In particular, the optimization of submodular functions over the integer lattice (integer …
In particular, the optimization of submodular functions over the integer lattice (integer …
Testing, Learning, and Optimization in High Dimensions
K Gatmiry - 2022 - dspace.mit.edu
In this thesis we study two separate problems:(1) What is the sample complexity of testing
the class of Determinantal Point Processes? and (2) Introducing a new analysis for …
the class of Determinantal Point Processes? and (2) Introducing a new analysis for …
Testing determinantal point processes
Determinantal point processes (DPPs) are popular probabilistic models of diversity. In this
paper, we investigate DPPs from a new perspective: property testing of distributions. Given …
paper, we investigate DPPs from a new perspective: property testing of distributions. Given …
[PDF][PDF] Integer Submodular Maximization: Algorithms and Modeling
A Sahin - 2022 - research-collection.ethz.ch
Submodular set functions are widely used in discrete optimization, machine learning and
computer vision because of their attractive properties in modeling and optimization. Despite …
computer vision because of their attractive properties in modeling and optimization. Despite …
Sampling from Probabilistic Submodular Models
A Gotovos - 2019 - research-collection.ethz.ch
Practical problems of discrete nature are very common in machine learning; application
domains include computer vision (eg, image segmentation), sequential decision making (eg …
domains include computer vision (eg, image segmentation), sequential decision making (eg …