61 citations · 179 across the 39 of their papers we have counts for
12 papers · 1 filter
Replicable Composition
Kiarash Banihashem, MohammadHossein Bateni, Hossein Esfandiari +2
Replicability requires that algorithmic conclusions remain consistent when rerun on independently drawn data. A central structural question is composition: given problems each…
Robust and differentially private stochastic linear bandits
Vasileios Charisopoulos, Hossein Esfandiari, Vahab Mirrokni
In this paper, we study the stochastic linear bandit problem under the additional requirements of differential privacy, robustness and batched observations. In particular, we assum…
Replicable Clustering
Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni +2
We design replicable algorithms in the context of statistical clustering under the recently introduced notion of replicability from Impagliazzo et al. [2022]. According to this def…
Anonymous Bandits for Multi-User Systems
Hossein Esfandiari, Vahab Mirrokni, Jon Schneider
In this work, we present and study a new framework for online learning in systems with multiple users that provide user anonymity. Specifically, we extend the notion of bandits to…
Replicable Bandits
Hossein Esfandiari, Alkis Kalavasis, Amin Karbasi +3
In this paper, we introduce the notion of replicable policies in the context of stochastic bandits, one of the canonical problems in interactive learning. A policy in the bandit en…
Tackling Provably Hard Representative Selection via Graph Neural Networks
Mehran Kazemi, Anton Tsitsulin, Hossein Esfandiari +4
Representative Selection (RS) is the problem of finding a small subset of exemplars from a dataset that is representative of the dataset. In this paper, we study RS for attributed…
Label differential privacy via clustering
Hossein Esfandiari, Vahab Mirrokni, Umar Syed +1
We present new mechanisms for \emph{label differential privacy}, a relaxation of differentially private machine learning that only protects the privacy of the labels in the trainin…
Almost Tight Approximation Algorithms for Explainable Clustering
Hossein Esfandiari, Vahab Mirrokni, Shyam Narayanan
Recently, due to an increasing interest for transparency in artificial intelligence, several methods of explainable machine learning have been developed with the simultaneous goal…
Feature Cross Search via Submodular Optimization
Lin Chen, Hossein Esfandiari, Gang Fu +2
In this paper, we study feature cross search as a fundamental primitive in feature engineering. The importance of feature cross search especially for the linear model has been know…
Adaptivity in Adaptive Submodularity
Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni
Adaptive sequential decision making is one of the central challenges in machine learning and artificial intelligence. In such problems, the goal is to design an interactive policy…
Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond
Lin Chen, Hossein Esfandiari, Thomas Fu +1
Computing approximate nearest neighbors in high dimensional spaces is a central problem in large-scale data mining with a wide range of applications in machine learning and data sc…
Categorical Feature Compression via Submodular Optimization
MohammadHossein Bateni, Lin Chen, Hossein Esfandiari +3
In the era of big data, learning from categorical features with very large vocabularies (e.g., 28 million for the Criteo click prediction dataset) has become a practical challenge…