Search Results for author: Sergei Vassilvitskii

Found 19 papers, 3 papers with code

Robust Online Correlation Clustering

no code implementations NeurIPS 2021 Silvio Lattanzi, Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang, Rudy Zhou

In correlation clustering we are given a set of points along with recommendations whether each pair of points should be placed in the same cluster or into separate clusters.

On the Pitfalls of Label Differential Privacy

no code implementations NeurIPS Workshop LatinX_in_AI 2021 Andres Munoz Medina, Robert Istvan Busa-Fekete, Umar Syed, Sergei Vassilvitskii

We complement the negative results with a non-parametric estimator for the true privacy loss, and apply our techniques on large-scale benchmark data to demonstrate how to achieve a desired privacy protection.

Label differential privacy via clustering

no code implementations5 Oct 2021 Hossein Esfandiari, Vahab Mirrokni, Umar Syed, Sergei Vassilvitskii

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 training set.

Faster Matchings via Learned Duals

no code implementations NeurIPS 2021 Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii

Second, once the duals are feasible, they may not be optimal, so we show that they can be used to quickly find an optimal solution.

Combinatorial Optimization

Private Optimization Without Constraint Violations

no code implementations2 Jul 2020 Andrés Muñoz Medina, Umar Syed, Sergei Vassilvitskii, Ellen Vitercik

We also prove a lower bound demonstrating that the difference between the objective value of our algorithm's solution and the optimal solution is tight up to logarithmic factors among all differentially private algorithms.

Fair Hierarchical Clustering

no code implementations NeurIPS 2020 Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang

As machine learning has become more prevalent, researchers have begun to recognize the necessity of ensuring machine learning systems are fair.


Sliding Window Algorithms for k-Clustering Problems

1 code implementation NeurIPS 2020 Michele Borassi, Alessandro Epasto, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam

The sliding window model of computation captures scenarios in which data is arriving continuously, but only the latest $w$ elements should be used for analysis.

Data Structures and Algorithms

Differentially Private Covariance Estimation

no code implementations NeurIPS 2019 Kareem Amin, Travis Dick, Alex Kulesza, Andres Munoz, Sergei Vassilvitskii

The covariance matrix of a dataset is a fundamental statistic that can be used for calculating optimum regression weights as well as in many other learning and data analysis settings.

Competitive caching with machine learned advice

no code implementations ICML 2018 Thodoris Lykouris, Sergei Vassilvitskii

Traditional online algorithms encapsulate decision making under uncertainty, and give ways to hedge against all possible future events, while guaranteeing a nearly optimal solution as compared to an offline optimum.

Decision Making Decision Making Under Uncertainty

Fair Clustering Through Fairlets

2 code implementations NeurIPS 2017 Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvitskii

We show that any fair clustering problem can be decomposed into first finding good fairlets, and then using existing machinery for traditional clustering algorithms.

Online Learning for Non-Stationary A/B Tests

no code implementations14 Feb 2018 Andrés Muñoz Medina, Sergei Vassilvitskii, Dong Yin

The rollout of new versions of a feature in modern applications is a manual multi-stage process, as the feature is released to ever larger groups of users, while its performance is carefully monitored.

Consistent k-Clustering

no code implementations ICML 2017 Silvio Lattanzi, Sergei Vassilvitskii

The study of online algorithms and competitive analysis provides a solid foundation for studying the quality of irrevocable decision making when the data arrives in an online manner.

Decision Making

Revenue Optimization with Approximate Bid Predictions

no code implementations NeurIPS 2017 Andrés Muñoz Medina, Sergei Vassilvitskii

In the context of advertising auctions, finding good reserve prices is a notoriously challenging learning problem.

Statistical Cost Sharing

no code implementations NeurIPS 2017 Eric Balkanski, Umar Syed, Sergei Vassilvitskii

We first show that when cost functions come from the family of submodular functions with bounded curvature, $\kappa$, the Shapley value can be approximated from samples up to a $\sqrt{1 - \kappa}$ factor, and that the bound is tight.

On Mixtures of Markov Chains

no code implementations NeurIPS 2016 Rishi Gupta, Ravi Kumar, Sergei Vassilvitskii

We study the problem of reconstructing a mixture of Markov chains from the trajectories generated by random walks through the state space.

Scalable K-Means++

1 code implementation29 Mar 2012 Bahman Bahmani, Benjamin Moseley, Andrea Vattani, Ravi Kumar, Sergei Vassilvitskii

The recently proposed k-means++ initialization algorithm achieves this, obtaining an initial set of centers that is provably close to the optimum solution.


Ad Serving Using a Compact Allocation Plan

no code implementations16 Mar 2012 Peiji Chen, Wenjing Ma, Srinath Mandalapu, Chandrashekhar Nagarajan, Jayavel Shanmugasundaram, Sergei Vassilvitskii, Erik Vee, Manfai Yu, Jason Zien

A large fraction of online display advertising is sold via guaranteed contracts: a publisher guarantees to the advertiser a certain number of user visits satisfying the targeting predicates of the contract.

Data Structures and Algorithms

SHALE: An Efficient Algorithm for Allocation of Guaranteed Display Advertising

no code implementations16 Mar 2012 Vijay Bharadwaj, Peiji Chen, Wenjing Ma, Chandrashekhar Nagarajan, John Tomlin, Sergei Vassilvitskii, Erik Vee, Jian Yang

Motivated by the problem of optimizing allocation in guaranteed display advertising, we develop an efficient, lightweight method of generating a compact {\em allocation plan} that can be used to guide ad server decisions.

Data Structures and Algorithms

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