4 papers
Debiasing Random Oblique Projections for Subsampled OLS and Fast CUR in High Dimensions
Chengmei Niu, Sachin Garg, MichaÅ DereziÅski +1
Random sampling is a fundamental tool in modern machine learning and numerical linear algebra for reducing the computational cost of large-scale matrix problems. Existing analyses,…
Perfect Parallelization in Mini-Batch SGD with Classical Momentum Acceleration
Sachin Garg, MichaÅ DereziÅski
Accelerating stochastic gradient methods with classical momentum schemes, such as Polyak's heavy ball, has proven highly successful in training large-scale machine learning models,…
Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project
Pratik Rathore, Zachary Frangella, Sachin Garg +3
Gaussian processes (GPs) play an essential role in biostatistics, scientific machine learning, and Bayesian optimization for their ability to provide probabilistic predictions and…
Faster Low-Rank Approximation and Kernel Ridge Regression via the Block-Nyström Method
Sachin Garg, MichaÅ DereziÅski
The Nyström method is a popular low-rank approximation technique for large matrices that arise in kernel methods and convex optimization. Yet, when the data exhibits heavy-tailed…