A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Improved CountSketch method reduces variance for estimating vector coordinates.
New algorithm optimizes positions of CountSketch non-zero entries for better data compression.
A well-known problem in data science and machine learning is {\em linear regression}, which is recently extended to dynamic graphs. Existing exact algorithms for updating the solution of dynamic graph regression require at least a linear time (in terms of : the size of the graph). However, this time complexity might…
Scalable algorithms to solve optimization and regression tasks even approximately, are needed to work with large datasets. In this paper we study efficient techniques from matrix sketching to solve a variety of convex constrained regression problems. We adopt "Iterative Hessian Sketching" (IHS) and show that the fast C…
New sketches for weighted sampling without replacement improve accuracy and efficiency.
A new method uses matrix sketches for efficient graph clustering in dynamic environments.
We improve prediction risk estimation for large datasets using sketching and ridge regression.
Unified methodology for statistical inference in least squares and PCA via randomized sketching.
Although federated learning improves privacy of training data by exchanging local gradients or parameters rather than raw data, the adversary still can leverage local gradients and parameters to obtain local training data by launching reconstruction and membership inference attacks. To defend such privacy attacks, many…