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Found 7 papers, 0 papers with code
Mathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions
Our data-driven framework for mathematical language processing (MLP) leverages solution data from a large number of learners to evaluate the correctness of their solutions, assign partial-credit scores, and provide feedback to each learner on the likely locations of any errors.
Active Learning for Undirected Graphical Model Selection
This paper studies graphical model selection, i. e., the problem of estimating a graph of statistical relationships among a collection of random variables.
Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression
In this paper, we address the challenging problem of selecting tuning parameters for high-dimensional sparse regression.
Swapping Variables for High-Dimensional Sparse Regression with Correlated Measurements
We consider the high-dimensional sparse linear regression problem of accurately estimating a sparse vector using a small number of linear measurements that are contaminated by noise.
When in Doubt, SWAP: High-Dimensional Sparse Recovery from Correlated Measurements
We consider the problem of accurately estimating a high-dimensional sparse vector using a small number of linear measurements that are contaminated by noise.
A Junction Tree Framework for Undirected Graphical Model Selection
We highlight three main properties of using junction trees for UGMS.
High-Dimensional Screening Using Multiple Grouping of Variables
Screening is the problem of finding a superset of the set of non-zero entries in an unknown p-dimensional vector \beta* given n noisy observations.
