Search Results for author:
Found 8 papers, 2 papers with code
Improved Frequency Estimation Algorithms with and without Predictions
In particular, their learning-augmented frequency estimation algorithm uses a learned heavy-hitter oracle which predicts which elements will appear many times in the stream.
Improved Space Bounds for Learning with Experts
We give improved tradeoffs between space and regret for the online learning with expert advice problem over $T$ days with $n$ experts.
High Probability Convergence of Stochastic Gradient Methods
Instead, we show high probability convergence with bounds depending on the initial distance to the optimal solution.
Improved Learning-augmented Algorithms for k-means and k-medians Clustering
We consider the problem of clustering in the learning-augmented setting, where we are given a data set in $d$-dimensional Euclidean space, and a label for each data point given by an oracle indicating what subsets of points should be clustered together.
Streaming Submodular Maximization with Differential Privacy
In this work, we study the problem of privately maximizing a submodular function in the streaming setting.
An Efficient Algorithm for Fair Multi-Agent Multi-Armed Bandit with Low Regret
This is an improvement to the previous approach, which has regret bound of $\tilde{O}( \min(NK, \sqrt{N} K^{3/2})\sqrt{T})$.
Private Frequency Estimation via Projective Geometry
In many parameter settings used in practice this is a significant improvement over the $ O(n+k^2)$ computation cost that is achieved by the recent PI-RAPPOR algorithm (Feldman and Talwar; 2021).
Differentially Private Clustering via Maximum Coverage
This paper studies the problem of clustering in metric spaces while preserving the privacy of individual data.
