4 citations · 6 across the 4 of their papers we have counts for
8 papers
Predictive Inference with Weak Supervision
Maxime Cauchois, Suyash Gupta, Alnur Ali +1
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 suc…
The Lifecycle of a Statistical Model: Model Failure Detection, Identification, and Refitting
Alnur Ali, Maxime Cauchois, John C. Duchi
The statistical machine learning community has demonstrated considerable resourcefulness over the years in developing highly expressive tools for estimation, prediction, and infere…
Accelerated Gradient Flow: Risk, Stability, and Implicit Regularization
Yue Sheng, Alnur Ali
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…
Minimum-Distortion Embedding
Akshay Agrawal, Alnur Ali, Stephen Boyd
We consider the vector embedding problem. We are given a finite set of items, with the goal of assigning a representative vector to each one, possibly under some constraints (such…
The Implicit Regularization of Stochastic Gradient Flow for Least Squares
Alnur Ali, Edgar Dobriban, Ryan J. Tibshirani
We study the implicit regularization of mini-batch stochastic gradient descent, when applied to the fundamental problem of least squares regression. We leverage a continuous-time s…
A Continuous-Time View of Early Stopping for Least Squares
Alnur Ali, J. Zico Kolter, Ryan J. Tibshirani
We study the statistical properties of the iterates generated by gradient descent, applied to the fundamental problem of least squares regression. We take a continuous-time view, i…