44 citations · 124 across the 14 of their papers we have counts for
16 papers
A State-of-the-Art SQL Reasoning Model using RLVR
Alnur Ali, Ashutosh Baheti, Jonathan Chang +13
Developing custom reasoning models via Reinforcement Learning (RL) that can incorporate organization-specific knowledge has great potential to address problems faced by enterprise…
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…
A comment and erratum on "Excess Optimism: How Biased is the Apparent Error of an Estimator Tuned by SURE?"
Maxime Cauchois, Alnur Ali, John Duchi
We identify and correct an error in the paper "Excess Optimism: How Biased is the Apparent Error of an Estimator Tuned by SURE?" This correction allows new guarantees on the excess…
Minimizing Oracle-Structured Composite Functions
Xinyue Shen, Alnur Ali, Stephen Boyd
We consider the problem of minimizing a composite convex function with two different access methods: an oracle, for which we can evaluate the value and gradient, and a structured f…
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…
Confidence bands for a log-concave density
Guenther Walther, Alnur Ali, Xinyue Shen +1
We present a new approach for inference about a log-concave distribution: Instead of using the method of maximum likelihood, we propose to incorporate the log-concavity constraint…
Robust Validation: Confident Predictions Even When Distributions Shift
Maxime Cauchois, Suyash Gupta, Alnur Ali +1
While the traditional viewpoint in machine learning and statistics assumes training and testing samples come from the same population, practice belies this fiction. One strategy --…
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…
The Generalized Lasso Problem and Uniqueness
Alnur Ali, Ryan J. Tibshirani
We study uniqueness in the generalized lasso problem, where the penalty is the norm of a matrix times the coefficient vector. We derive a broad result on uniqueness th…
Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation
Penporn Koanantakool, Alnur Ali, Ariful Azad +5
Across a variety of scientific disciplines, sparse inverse covariance estimation is a popular tool for capturing the underlying dependency relationships in multivariate data. Unfor…
A Semismooth Newton Method for Fast, Generic Convex Programming
Alnur Ali, Eric Wong, J. Zico Kolter
We introduce Newton-ADMM, a method for fast conic optimization. The basic idea is to view the residuals of consecutive iterates generated by the alternating direction method of mul…
The Multiple Quantile Graphical Model
Alnur Ali, J. Zico Kolter, Ryan J. Tibshirani
We introduce the Multiple Quantile Graphical Model (MQGM), which extends the neighborhood selection approach of Meinshausen and Buhlmann for learning sparse graphical models. The l…
Generalized Pseudolikelihood Methods for Inverse Covariance Estimation
Alnur Ali, Kshitij Khare, Sang-Yun Oh +1
We introduce PseudoNet, a new pseudolikelihood-based estimator of the inverse covariance matrix, that has a number of useful statistical and computational properties. We show, thro…