40 citations · 104 across the 11 of their papers we have counts for
11 papers
Testing linear combinations of multiple variance components
Alex Stringer, Jeffrey Negrea
We test the hypothesis that simulataneous linear contrasts of multiple variance components equal zero in a Gaussian variance components model via a parametric bootstrap. Applicatio…
Quantitative Error Bounds for Scaling Limits of Stochastic Iterative Algorithms
Xiaoyu Wang, Mikolaj J. Kasprzak, Jeffrey Negrea +2
Stochastic iterative algorithms, including stochastic gradient descent (SGD) and stochastic gradient Langevin dynamics (SGLD), are widely utilized for optimization and sampling in…
Concept Algebra for (Score-Based) Text-Controlled Generative Models
Zihao Wang, Lin Gui, Jeffrey Negrea +1
This paper concerns the structure of learned representations in text-guided generative models, focusing on score-based models. A key property of such models is that they can compos…
Tuning Stochastic Gradient Algorithms for Statistical Inference via Large-Sample Asymptotics
Jeffrey Negrea, Jun Yang, Haoyue Feng +2
The tuning of stochastic gradient algorithms (SGAs) for optimization and sampling is often based on heuristics and trial-and-error rather than generalizable theory. We address this…
Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers
Jeffrey Negrea, Blair Bilodeau, Nicolò Campolongo +2
Quantile (and, more generally, KL) regret bounds, such as those achieved by NormalHedge (Chaudhuri, Freund, and Hsu 2009) and its variants, relax the goal of competing against the…
Relaxing the I.I.D. Assumption: Adaptively Minimax Optimal Regret via Root-Entropic Regularization
Blair Bilodeau, Jeffrey Negrea, Daniel M. Roy
We consider prediction with expert advice when data are generated from distributions varying arbitrarily within an unknown constraint set. This semi-adversarial setting includes (a…
Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms
Mahdi Haghifam, Jeffrey Negrea, Ashish Khisti +2
The information-theoretic framework of Russo and J. Zou (2016) and Xu and Raginsky (2017) provides bounds on the generalization error of a learning algorithm in terms of the mutual…
In Defense of Uniform Convergence: Generalization via derandomization with an application to interpolating predictors
Jeffrey Negrea, Gintare Karolina Dziugaite, Daniel M. Roy
We propose to study the generalization error of a learned predictor in terms of that of a surrogate (potentially randomized) predictor that is coupled to and desi…
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates
Jeffrey Negrea, Mahdi Haghifam, Gintare Karolina Dziugaite +2
In this work, we improve upon the stepwise analysis of noisy iterative learning algorithms initiated by Pensia, Jog, and Loh (2018) and recently extended by Bu, Zou, and Veeravalli…
Optimal Scaling and Shaping of Random Walk Metropolis via Diffusion Limits of Block-I.I.D. Targets
Jeffrey Negrea
This work extends Roberts et al. (1997) by considering limits of Random Walk Metropolis (RWM) applied to block IID target distributions, with corresponding block-independent propos…
Approximations of Geometrically Ergodic Reversible Markov Chains
Jeffrey Negrea, Jeffrey S. Rosenthal
A common tool in the practice of Markov Chain Monte Carlo is to use approximating transition kernels to speed up computation when the desired kernel is slow to evaluate or intracta…