Optimizing proper loss functions is popularly believed to yield predicto...
Multicalibration is a notion of fairness that aims to provide accurate
p...
This work initiates the systematic study of explicit distributions that ...
We study the fundamental question of how to define and measure the dista...
In many learning theory problems, a central role is played by a hypothes...
Recovering linear subspaces from data is a fundamental and important tas...
We present a new perspective on loss minimization and the recent notion ...
The notion of omnipredictors (Gopalan, Kalai, Reingold, Sharan and Wiede...
We give the first sample complexity characterizations for outcome
indist...
We present a new local-search algorithm for the k-median clustering
prob...
Many clustering algorithms are guided by certain cost functions such as ...
In the non-negative matrix factorization (NMF) problem, the input is an
...
We study the problem of robustly estimating the mean of a d-dimensional
...
Memorization in over-parameterized neural networks could severely hurt
g...
The randomized query complexity R(f) of a boolean function
f{0,1}^n→{0,1...
It is widely believed that learning good representations is one of the m...
In this work, we give the first algorithms for tolerant testing of nontr...
In this work we study the quantitative relation between the recursive
te...