Search Results for author:
Found 27 papers, 5 papers with code
Fair MP-BOOST: Fair and Interpretable Minipatch Boosting
Ensemble methods, particularly boosting, have established themselves as highly effective and widely embraced machine learning techniques for tabular data.
Fair Feature Importance Scores for Interpreting Tree-Based Methods and Surrogates
Specifically, we develop a novel fair feature importance score for trees that can be used to interpret how each feature contributes to fairness or bias in trees, tree-based ensembles, or tree-based surrogates of any complex ML system.
Data Augmentation via Subgroup Mixup for Improving Fairness
Furthermore, our method allows us to use the generalization ability of mixup to improve both fairness and accuracy.
Interpretable Machine Learning for Discovery: Statistical Challenges \& Opportunities
In this paper, we discuss and review the field of interpretable machine learning, focusing especially on the techniques as they are often employed to generate new knowledge or make discoveries from large data sets.
Nonparanormal Graph Quilting with Applications to Calcium Imaging
Probabilistic graphical models have become an important unsupervised learning tool for detecting network structures for a variety of problems, including the estimation of functional neuronal connectivity from two-photon calcium imaging data.
Low-Rank Covariance Completion for Graph Quilting with Applications to Functional Connectivity
This leads to the Graph Quilting problem, as first introduced by (Vinci et. al.
Model-Agnostic Confidence Intervals for Feature Importance: A Fast and Powerful Approach Using Minipatch Ensembles
Our approach is fast as we avoid model refitting by leveraging a form of random observation and feature subsampling called minipatch ensembles; this approach also improves statistical power by avoiding data splitting.
Network Clustering for Latent State and Changepoint Detection
In this paper, we propose a convex approach for the task of network clustering.
Fast and Accurate Graph Learning for Huge Data via Minipatch Ensembles
Gaussian graphical models provide a powerful framework for uncovering conditional dependence relationships between sets of nodes; they have found applications in a wide variety of fields including sensor and communication networks, physics, finance, and computational biology.
Fast and Interpretable Consensus Clustering via Minipatch Learning
Additionally, we develop adaptive sampling schemes for observations, which result in both improved reliability and computational savings, as well as adaptive sampling schemes of features, which leads to interpretable solutions by quickly learning the most relevant features that differentiate clusters.
Thresholded Graphical Lasso Adjusts for Latent Variables: Application to Functional Neural Connectivity
In neuroscience, researchers seek to uncover the connectivity of neurons from large-scale neural recordings or imaging; often people employ graphical model selection and estimation techniques for this purpose.
Simultaneous Grouping and Denoising via Sparse Convex Wavelet Clustering
In contrast to common practice which denoises then clusters, our method is a unified, convex approach that performs both simultaneously.
Interpretable Visualization and Higher-Order Dimension Reduction for ECoG Data
After signal processing, this type of data may be organized as a 4-way tensor with dimensions representing trials, electrodes, frequency, and time.
MP-Boost: Minipatch Boosting via Adaptive Feature and Observation Sampling
We achieve this by developing MP-Boost, an algorithm loosely based on AdaBoost that learns by adaptively selecting small subsets of instances and features, or what we term minipatches (MP), at each iteration.
Feature Selection for Huge Data via Minipatch Learning
While feature selection is a well-studied problem with many widely-used techniques, there are typically two key challenges: i) many existing approaches become computationally intractable in huge-data settings with millions of observations and features; and ii) the statistical accuracy of selected features degrades in high-noise, high-correlation settings, thus hindering reliable model interpretation.
Supervised Convex Clustering
Clustering has long been a popular unsupervised learning approach to identify groups of similar objects and discover patterns from unlabeled data in many applications.
Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data
While several techniques for such integrative clustering have been explored, we propose and develop a convex formalization that will inherit the strong statistical, mathematical and empirical properties of increasingly popular convex clustering methods.
Clustered Gaussian Graphical Model via Symmetric Convex Clustering
Knowledge of functional groupings of neurons can shed light on structures of neural circuits and is valuable in many types of neuroimaging studies.
Feature Selection for Data Integration with Mixed Multi-view Data
B-RAIL serves as a versatile data integration method for sparse regression and graph selection, and we demonstrate the effectiveness of B-RAIL through extensive simulations and a case study to infer the ovarian cancer gene regulatory network.
Dynamic Visualization and Fast Computation for Convex Clustering via Algorithmic Regularization
Convex clustering is a promising new approach to the classical problem of clustering, combining strong performance in empirical studies with rigorous theoretical foundations.
A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution
The Poisson distribution has been widely studied and used for modeling univariate count-valued data.
A General Framework for Mixed Graphical Models
"Mixed Data" comprising a large number of heterogeneous variables (e. g. count, binary, continuous, skewed continuous, among other data types) are prevalent in varied areas such as genomics and proteomics, imaging genetics, national security, social networking, and Internet advertising.
Convex Biclustering
In the biclustering problem, we seek to simultaneously group observations and features.
On Poisson Graphical Models
Undirected graphical models, such as Gaussian graphical models, Ising, and multinomial/categorical graphical models, are widely used in a variety of applications for modeling distributions over a large number of variables.
Conditional Random Fields via Univariate Exponential Families
We thus introduce a “novel subclass of CRFs”, derived by imposing node-wise conditional distributions of response variables conditioned on the rest of the responses and the covariates as arising from univariate exponential families.
Sparse and Functional Principal Components Analysis
We propose a unified approach to regularized PCA which can induce both sparsity and smoothness in both the row and column principal components.
On Graphical Models via Univariate Exponential Family Distributions
Undirected graphical models, or Markov networks, are a popular class of statistical models, used in a wide variety of applications.
