Search Results for author:
Found 9 papers, 2 papers with code
The Lifecycle of a Statistical Model: Model Failure Detection, Identification, and Refitting
The statistical machine learning community has demonstrated considerable resourcefulness over the years in developing highly expressive tools for estimation, prediction, and inference.
Predictive Inference with Weak Supervision
The expense of acquiring labels in large-scale statistical machine learning makes partially and weakly-labeled data attractive, though it is not always apparent how to leverage such data for model fitting or validation.
Accelerated Gradient Flow: Risk, Stability, and Implicit Regularization
Acceleration and momentum are the de facto standard in modern applications of machine learning and optimization, yet the bulk of the work on implicit regularization focuses instead on unaccelerated methods.
Minimum-Distortion Embedding
Our software scales to data sets with millions of items and tens of millions of distortion functions.
Robust Validation: Confident Predictions Even When Distributions Shift
One strategy -- coming from robust statistics and optimization -- is thus to build a model robust to distributional perturbations.
The Implicit Regularization of Stochastic Gradient Flow for Least Squares
We study the implicit regularization of mini-batch stochastic gradient descent, when applied to the fundamental problem of least squares regression.
A Continuous-Time View of Early Stopping for Least Squares
Our primary focus is to compare the risk of gradient flow to that of ridge regression.
Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation
Across a variety of scientific disciplines, sparse inverse covariance estimation is a popular tool for capturing the underlying dependency relationships in multivariate data.
