Search Results for author:
Found 13 papers, 6 papers with code
Error Reduction from Stacked Regressions
In this paper, we learn these weights analogously by minimizing an estimate of the population risk subject to a nonnegativity constraint.
MDI+: A Flexible Random Forest-Based Feature Importance Framework
We show that the MDI for a feature $X_k$ in each tree in an RF is equivalent to the unnormalized $R^2$ value in a linear regression of the response on the collection of decision stumps that split on $X_k$.
A Mixing Time Lower Bound for a Simplified Version of BART
In this paper, we provide the first lower bound on the mixing time for a simplified version of BART in which we reduce the sum to a single tree and use a subset of the possible moves for the MCMC proposal distribution.
Hierarchical Shrinkage: improving the accuracy and interpretability of tree-based methods
Tree-based models such as decision trees and random forests (RF) are a cornerstone of modern machine-learning practice.
Fast Interpretable Greedy-Tree Sums
In such settings, practitioners often use highly interpretable decision tree models, but these suffer from inductive bias against additive structure.
A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds
We prove a sharp squared error generalization lower bound for a large class of decision tree algorithms fitted to sparse additive models with $C^1$ component functions.
Stable discovery of interpretable subgroups via calibration in causal studies
Building on Yu and Kumbier's PCS framework and for randomized experiments, we introduce a novel methodology for Stable Discovery of Interpretable Subgroups via Calibration (StaDISC), with large heterogeneous treatment effects.
Curating a COVID-19 data repository and forecasting county-level death counts in the United States
We use this data to develop predictions and corresponding prediction intervals for the short-term trajectory of COVID-19 cumulative death counts at the county-level in the United States up to two weeks ahead.
Online Stochastic Gradient Descent with Arbitrary Initialization Solves Non-smooth, Non-convex Phase Retrieval
In recent literature, a general two step procedure has been formulated for solving the problem of phase retrieval.
Sparse Phase Retrieval via Sparse PCA Despite Model Misspecification: A Simplified and Extended Analysis
We consider the problem of high-dimensional misspecified phase retrieval.
Subspace Clustering using Ensembles of $K$-Subspaces
We present a novel geometric approach to the subspace clustering problem that leverages ensembles of the K-subspaces (KSS) algorithm via the evidence accumulation clustering framework.
Phase Retrieval via Randomized Kaczmarz: Theoretical Guarantees
We consider the problem of phase retrieval, i. e. that of solving systems of quadratic equations.
Polynomial Time and Sample Complexity for Non-Gaussian Component Analysis: Spectral Methods
The problem of Non-Gaussian Component Analysis (NGCA) is about finding a maximal low-dimensional subspace $E$ in $\mathbb{R}^n$ so that data points projected onto $E$ follow a non-gaussian distribution.
