126 citations · 569 across the 45 of their papers we have counts for
52 papers
Lossy Compression via Sparse Regression Codes: Generalized Construction and Finite-length Bounds
Galen Reeves, Ramji Venkataramanan
We study sparse regression codes (SPARCs) for lossy compression under simple greedy encoding rules, including both correlation-based and distance-based methods. We generalize the S…
Diffusion Models Observe Only Gradients: A Geometric Perspective on Score Matching Errors
Naïl B. Khelifa, Richard E. Turner, Ramji Venkataramanan
Score-based diffusion models are typically trained by minimizing the score matching error, and standard theoretical analyses rely on this quantity to bound the sampling discr…
Recursively Trained Diffusion Models: Limiting Collapse Distribution and Spectral Characterization
Naïl B. Khelifa, Richard E. Turner, Ramji Venkataramanan
Recursive training of generative models on their own outputs can lead to model collapse, a compounding drift away from the true data distribution. Existing theoretical works bound…
Inferring Change Points in Regression via Sample Weighting
Gabriel Arpino, Ramji Venkataramanan
We study the problem of identifying change points in high-dimensional generalized linear models, and propose an approach based on sample-weighted empirical risk minimization. Our m…
Optimal Estimation in Orthogonally Invariant Generalized Linear Models: Spectral Initialization and Approximate Message Passing
Yihan Zhang, Hong Chang Ji, Ramji Venkataramanan +1
We consider the problem of parameter estimation from a generalized linear model with a random design matrix that is orthogonally invariant in law. Such a model allows the design ha…
Quantifying Error Propagation and Model Collapse in Diffusion Models
Nail B. Khelifa, Richard E. Turner, Ramji Venkataramanan
Machine learning models are increasingly trained or fine-tuned on synthetic data. Recursively training on such data has been observed to significantly degrade performance in a wide…