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01/31/2023
Learning noisy-OR Bayesian Networks with Max-Product Belief Propagation
Noisy-OR Bayesian Networks (BNs) are a family of probabilistic graphical...
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12/06/2021
Graphical Models with Attention for Context-Specific Independence and an Application to Perceptual Grouping
Discrete undirected graphical models, also known as Markov Random Fields...
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11/03/2021
Perturb-and-max-product: Sampling and learning in discrete energy-based models
Perturb-and-MAP offers an elegant approach to approximately sample from ...
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12/03/2020
Sample-efficient L0-L2 constrained structure learning of sparse Ising models
We consider the problem of learning the underlying graph of a sparse Isi...
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06/11/2020
Query Training: Learning and inference for directed and undirected graphical models
Probabilistic graphical models (PGMs) provide a compact representation o...
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01/17/2020
Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives
We consider a discrete optimization based approach for learning sparse c...
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12/21/2019
An error bound for Lasso and Group Lasso in high dimensions
We leverage recent advances in high-dimensional statistics to derive new...
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10/20/2019
Sparse (group) learning with Lipschitz loss functions: a unified analysis
We study a family of sparse estimators defined as minimizers of some emp...
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05/01/2019
Learning higher-order sequential structure with cloned HMMs
Variable order sequence modeling is an important problem in artificial a...
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01/06/2019
Solving large-scale L1-regularized SVMs and cousins: the surprising effectiveness of column and constraint generation
The linear Support Vector Machine (SVM) is one of the most popular binar...
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10/07/2018
Error bounds for sparse classifiers in high-dimensions
We prove an L2 recovery bound for a family of sparse estimators defined ...
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03/04/2018
Hierarchical Modeling and Shrinkage for User Session Length Prediction in Media Streaming
An important metric of users' satisfaction and engagement within on-line...
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08/10/2017