We consider the randomized communication complexity of the distributed
ℓ...
We study streaming algorithms in the white-box adversarial stream model,...
We consider sketching algorithms which first compress data by multiplica...
We introduce efficient (1+ε)-approximation algorithms for the
binary mat...
In large scale machine learning, random sampling is a popular way to
app...
Given 𝐀∈ℝ^n × n with entries bounded in
magnitude by 1, it is well-known...
Given a symmetric matrix A, we show from the simple sketch GAG^T, where
...
Subset selection for the rank k approximation of an n× d matrix A
offers...
We study dynamic algorithms robust to adaptive input generated from sour...
We revisit Nisan's classical pseudorandom generator (PRG) for space-boun...
We study oblivious sketching for k-sparse linear regression under variou...
In the online learning with experts problem, an algorithm must make a
pr...
We study the space complexity of the two related fields of differential
...
We study fundamental problems in linear algebra, such as finding a maxim...
In the ℓ_p-subspace sketch problem, we are given an n× d matrix
A with n...
We study L_p polynomial regression. Given query access to a function
f:[...
We consider the problem of minimizing the number of matrix-vector querie...
The seminal work of Cohen and Peng introduced Lewis weight sampling to t...
We initiate a broad study of classical problems in the streaming model w...
We introduce data structures for solving robust regression through stoch...
We study the problem of approximating a given tensor with q modes A ∈ℝ^n...
A common method in training neural networks is to initialize all the wei...
A treap is a classic randomized binary search tree data structure that i...
Online learning with expert advice is a fundamental problem of sequentia...
We study streaming algorithms in the white-box adversarial model, where ...
We give a sketching-based iterative algorithm that computes 1+ε
approxim...
Many existing algorithms for streaming geometric data analysis have been...
We study the problem of testing whether a symmetric d × d input matrix
A...
We propose data-driven one-pass streaming algorithms for estimating the
...
We study the ℓ_p regression problem, which requires finding
𝐱∈ℝ^d that m...
We consider the following oblivious sketching problem: given ϵ∈
(0,1/3) ...
We study iterative methods based on Krylov subspaces for low-rank
approx...
We give an input sparsity time sampling algorithm for spectrally
approxi...
We study active sampling algorithms for linear regression, which aim to ...
Frequency estimation is one of the most fundamental problems in streamin...
We overcome two major bottlenecks in the study of low rank approximation...
k-means clustering is a well-studied problem due to its wide applicabili...
In the G-sampling problem, the goal is to output an index i of a vector
...
We study the distribution of the matrix product G_1 G_2 ⋯ G_r of
r indep...
Kernel methods are fundamental in machine learning, and faster algorithm...
Currently, in the numerical linear algebra community, it is thought that...
In applications such as natural language processing or computer vision, ...
We give the first single-pass streaming algorithm for Column Subset Sele...
We present the first semi-streaming PTAS for the minimum feedback arc se...
Sketching is a powerful dimensionality reduction technique for accelerat...
The Boolean Hidden Matching (BHM) problem, introduced in a seminal paper...
We study statistical problems, such as planted clique, its variants, and...
A variety of dimensionality reduction techniques have been applied for
c...
The multiplayer promise set disjointness is one of the most widely used
...
We consider the problem of learning a latent k-vertex simplex
K⊂ℝ^d, giv...