12 citations · 49 across the 10 of their papers we have counts for
20 papers
Domain Sparsification of Discrete Distributions using Entropic Independence
Nima Anari, Michał Dereziński, Thuy-Duong Vuong +1
We present a framework for speeding up the time it takes to sample from discrete distributions defined over subsets of size of a ground set of elements, in the regime $…
Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update
Michał Dereziński, Jonathan Lacotte, Mert Pilanci +1
In second-order optimization, a potential bottleneck can be computing the Hessian matrix of the optimized function at every iteration. Randomized sketching has emerged as a powerfu…
LocalNewton: Reducing Communication Bottleneck for Distributed Learning
Vipul Gupta, Avishek Ghosh, Michal Derezinski +3
To address the communication bottleneck problem in distributed optimization within a master-worker framework, we propose LocalNewton, a distributed second-order algorithm with loca…
Query Complexity of Least Absolute Deviation Regression via Robust Uniform Convergence
Xue Chen, Michał Dereziński
Consider a regression problem where the learner is given a large collection of -dimensional data points, but can only query a small subset of the real-valued labels. How many qu…
Sparse sketches with small inversion bias
Michał Dereziński, Zhenyu Liao, Edgar Dobriban +1
For a tall matrix and a random sketching matrix , the sketched estimate of the inverse covariance matrix is typically biased: $E[(\…
Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization
Michał Dereziński, Burak Bartan, Mert Pilanci +1
In distributed second order optimization, a standard strategy is to average many local estimates, each of which is based on a small sketch or batch of the data. However, the local…