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Found 6 papers, 0 papers with code
Selective inference using randomized group lasso estimators for general models
Selective inference methods are developed for group lasso estimators for use with a wide class of distributions and loss functions.
Exact Selective Inference with Randomization
We introduce a pivot for exact selective inference with randomization.
Selective Inference for Sparse Multitask Regression with Applications in Neuroimaging
Multi-task learning is frequently used to model a set of related response variables from the same set of features, improving predictive performance and modeling accuracy relative to methods that handle each response variable separately.
Approximate Post-Selective Inference for Regression with the Group LASSO
After selection with the Group LASSO (or generalized variants such as the overlapping, sparse, or standardized Group LASSO), inference for the selected parameters is unreliable in the absence of adjustments for selection bias.
Causal Structure Discovery from Distributions Arising from Mixtures of DAGs
We consider distributions arising from a mixture of causal models, where each model is represented by a directed acyclic graph (DAG).
A relevance-scalability-interpretability tradeoff with temporally evolving user personas
The current work characterizes the users of a VoD streaming space through user-personas based on a tenure timeline and temporal behavioral features in the absence of explicit user profiles.
