Large language models (LLMs) provide a promising tool that enable robots...
Hyperparameter optimization, also known as hyperparameter tuning, is a w...
This paper studies federated linear contextual bandits under the notion ...
We investigate the contraction properties of locally differentially priv...
Many modern machine learning algorithms are composed of simple private
a...
In modern settings of data analysis, we may be running our algorithms on...
We study stochastic convex optimization with heavy-tailed data under the...
We study robust testing and estimation of discrete distributions in the
...
Despite intense interest and considerable effort, the current generation...
The problem of joint design of transmit waveforms and receive filters is...
Le Cam's method, Fano's inequality, and Assouad's lemma are three widely...
We initiate the study of hypothesis selection under local differential
p...
We consider the problem of learning Markov Random Fields (including the
...
In this paper, we study the problem of estimating smooth Generalized Lin...
We develop differentially private methods for estimating various
distrib...
We consider discrete distribution estimation over k elements under
ε-loc...