We study the Densest Subgraph problem under the additional constraint of...
The classical ski-rental problem admits a textbook 2-competitive
determi...
Compact user representations (such as embeddings) form the backbone of
p...
Recent work has shown that leveraging learned predictions can improve th...
ML models are ubiquitous in real world applications and are a constant f...
The streaming model of computation is a popular approach for working wit...
The research area of algorithms with predictions has seen recent success...
When applying differential privacy to sensitive data, a common way of ge...
Linear regression is a fundamental tool for statistical analysis. This h...
When working with user data providing well-defined privacy guarantees is...
We study the private k-median and k-means clustering problem in d
dimens...
A burgeoning paradigm in algorithm design is the field of algorithms wit...
Differential privacy has become the standard for private data analysis, ...
We present new mechanisms for label differential privacy, a relaxation
o...
A recent line of research investigates how algorithms can be augmented w...
The secretary problem is probably the purest model of decision making un...
We study the problem of differentially private optimization with linear
...
As machine learning has become more prevalent, researchers have begun to...
We introduce algorithms that use predictions from machine learning appli...
The sliding window model of computation captures scenarios in which data...
We study the question of fair clustering under the disparate impact
doc...
Traditional online algorithms encapsulate decision making under uncertai...
The rollout of new versions of a feature in modern applications is a man...