Search Results for author:
Found 9 papers, 4 papers with code
Conformal PID Control for Time Series Prediction
We study the problem of uncertainty quantification for time series prediction, with the goal of providing easy-to-use algorithms with formal guarantees.
Predictive inference with the jackknife+
This paper introduces the jackknife+, which is a novel method for constructing predictive confidence intervals.
Methodology
Conformal Prediction Under Covariate Shift
We extend conformal prediction methodology beyond the case of exchangeable data.
Methodology
The phase transition for the existence of the maximum likelihood estimate in high-dimensional logistic regression
This paper rigorously establishes that the existence of the maximum likelihood estimate (MLE) in high-dimensional logistic regression models with Gaussian covariates undergoes a sharp `phase transition'.
Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems
We complement our theoretical study with numerical examples showing that solving random quadratic systems is both computationally and statistically not much harder than solving linear systems of the same size---hence the title of this paper.
A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights
We derive a second-order ordinary differential equation (ODE) which is the limit of Nesterov's accelerated gradient method.
Robust Principal Component Analysis?
This suggests the possibility of a principled approach to robust principal component analysis since our methodology and results assert that one can recover the principal components of a data matrix even though a positive fraction of its entries are arbitrarily corrupted.
Information Theory Information Theory
A Singular Value Thresholding Algorithm for Matrix Completion
Off-the-shelf algorithms such as interior point methods are not directly amenable to large problems of this kind with over a million unknown entries.
Optimization and Control
Exact Matrix Completion via Convex Optimization
We show that one can perfectly recover most low-rank matrices from what appears to be an incomplete set of entries.
Information Theory Information Theory
