Search Results for author: Vahab Mirrokni

Found 44 papers, 8 papers with code

Robust Pricing in Dynamic Mechanism Design

no code implementations ICML 2020 Yuan Deng, Sébastien Lahaie, Vahab Mirrokni

Motivated by the repeated sale of online ads via auctions, optimal pricing in repeated auctions has attracted a large body of research.

Improved Approximations for Euclidean $k$-means and $k$-median, via Nested Quasi-Independent Sets

no code implementations11 Apr 2022 Vincent Cohen-Addad, Hossein Esfandiari, Vahab Mirrokni, Shyam Narayanan

Motivated by data analysis and machine learning applications, we consider the popular high-dimensional Euclidean $k$-median and $k$-means problems.

From Online Optimization to PID Controllers: Mirror Descent with Momentum

no code implementations12 Feb 2022 Santiago R. Balseiro, Haihao Lu, Vahab Mirrokni, Balasubramanian Sivan

We study a family of first-order methods with momentum based on mirror descent for online convex optimization, which we dub online mirror descent with momentum (OMDM).

Tight and Robust Private Mean Estimation with Few Users

no code implementations22 Oct 2021 Hossein Esfandiari, Vahab Mirrokni, Shyam Narayanan

In this work, we study high-dimensional mean estimation under user-level differential privacy, and attempt to design an $(\epsilon,\delta)$-differentially private mechanism using as few users as possible.

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.

Scalable Community Detection via Parallel Correlation Clustering

1 code implementation27 Jul 2021 Jessica Shi, Laxman Dhulipala, David Eisenstat, Jakub Łącki, Vahab Mirrokni

Our empirical evaluation shows that this framework improves the state-of-the-art trade-offs between speed and quality of scalable community detection.

Community Detection Graph Clustering

Almost Tight Approximation Algorithms for Explainable Clustering

no code implementations1 Jul 2021 Hossein Esfandiari, Vahab Mirrokni, Shyam Narayanan

Next, we study the $k$-means problem in this context and provide an $O(k \log k)$-approximation algorithm for explainable $k$-means, improving over the $O(k^2)$ bound of Dasgupta et al. and the $O(d k \log k)$ bound of \cite{laber2021explainable}.

Hierarchical Agglomerative Graph Clustering in Nearly-Linear Time

no code implementations10 Jun 2021 Laxman Dhulipala, David Eisenstat, Jakub Łącki, Vahab Mirrokni, Jessica Shi

For this variant, this is the first exact algorithm that runs in subquadratic time, as long as $m=n^{2-\epsilon}$ for some constant $\epsilon > 0$.

Graph Clustering

Parallelizing Thompson Sampling

no code implementations NeurIPS 2021 Amin Karbasi, Vahab Mirrokni, Mohammad Shadravan

How can we make use of information parallelism in online decision making problems while efficiently balancing the exploration-exploitation trade-off?

Decision Making

Batched Neural Bandits

no code implementations25 Feb 2021 Quanquan Gu, Amin Karbasi, Khashayar Khosravi, Vahab Mirrokni, Dongruo Zhou

In many sequential decision-making problems, the individuals are split into several batches and the decision-maker is only allowed to change her policy at the end of batches.

Decision Making

Smoothly Bounding User Contributions in Differential Privacy

no code implementations NeurIPS 2020 Alessandro Epasto, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, Lijie Ren

But at the same time, more noise might need to be added to the algorithm in order to keep the algorithm differentially private and this might hurt the algorithm’s performance.

Optimal Approximation - Smoothness Tradeoffs for Soft-Max Functions

no code implementations NeurIPS 2020 Alessandro Epasto, Mohammad Mahdian, Vahab Mirrokni, Emmanouil Zampetakis

A soft-max function has two main efficiency measures: (1) approximation - which corresponds to how well it approximates the maximum function, (2) smoothness - which shows how sensitive it is to changes of its input.

Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming

1 code implementation NeurIPS 2020 Joey Huchette, Haihao Lu, Hossein Esfandiari, Vahab Mirrokni

Moreover, we show that this MIP formulation is ideal (i. e. the strongest possible formulation) for the revenue function of a single impression.

The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems

no code implementations18 Nov 2020 Santiago Balseiro, Haihao Lu, Vahab Mirrokni

In this paper, we consider a data-driven setting in which the reward and resource consumption of each request are generated using an input model that is unknown to the decision maker.

Optimal Approximation -- Smoothness Tradeoffs for Soft-Max Functions

no code implementations22 Oct 2020 Alessandro Epasto, Mohammad Mahdian, Vahab Mirrokni, Manolis Zampetakis

A soft-max function has two main efficiency measures: (1) approximation - which corresponds to how well it approximates the maximum function, (2) smoothness - which shows how sensitive it is to changes of its input.

Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems

no code implementations20 Oct 2020 Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab Mirrokni

Unlike nonconvex optimization, where gradient descent is guaranteed to converge to a local optimizer, algorithms for nonconvex-nonconcave minimax optimization can have topologically different solution paths: sometimes converging to a solution, sometimes never converging and instead following a limit cycle, and sometimes diverging.

Regularized Online Allocation Problems: Fairness and Beyond

no code implementations1 Jul 2020 Santiago Balseiro, Haihao Lu, Vahab Mirrokni

In this paper, we introduce the \emph{regularized online allocation problem}, a variant that includes a non-linear regularizer acting on the total resource consumption.

Fairness

The Landscape of the Proximal Point Method for Nonconvex-Nonconcave Minimax Optimization

no code implementations15 Jun 2020 Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab Mirrokni

Critically, we show this envelope not only smooths the objective but can convexify and concavify it based on the level of interaction present between the minimizing and maximizing variables.

Bandits with adversarial scaling

no code implementations ICML 2020 Thodoris Lykouris, Vahab Mirrokni, Renato Paes Leme

We study "adversarial scaling", a multi-armed bandit model where rewards have a stochastic and an adversarial component.

Dynamic Incentive-aware Learning: Robust Pricing in Contextual Auctions

no code implementations NeurIPS 2019 Negin Golrezaei, Adel Javanmard, Vahab Mirrokni

Motivated by pricing in ad exchange markets, we consider the problem of robust learning of reserve prices against strategic buyers in repeated contextual second-price auctions.

Dual Mirror Descent for Online Allocation Problems

no code implementations ICML 2020 Haihao Lu, Santiago Balseiro, Vahab Mirrokni

The revenue function and resource consumption of each request are drawn independently and at random from a probability distribution that is unknown to the decision maker.

Optimization and Control

Contextual Reserve Price Optimization in Auctions via Mixed-Integer Programming

1 code implementation20 Feb 2020 Joey Huchette, Haihao Lu, Hossein Esfandiari, Vahab Mirrokni

Moreover, we show that this MIP formulation is ideal (i. e. the strongest possible formulation) for the revenue function of a single impression.

A Robust Non-Clairvoyant Dynamic Mechanism for Contextual Auctions

no code implementations NeurIPS 2019 Yuan Deng, Sébastien Lahaie, Vahab Mirrokni

Dynamic mechanisms offer powerful techniques to improve on both revenue and efficiency by linking sequential auctions using state information, but these techniques rely on exact distributional information of the buyers’ valuations (present and future), which limits their use in learning settings.

Variance Reduction in Bipartite Experiments through Correlation Clustering

no code implementations NeurIPS 2019 Jean Pouget-Abadie, Kevin Aydin, Warren Schudy, Kay Brodersen, Vahab Mirrokni

This paper introduces a novel clustering objective and a corresponding algorithm that partitions a bipartite graph so as to maximize the statistical power of a bipartite experiment on that graph.

Causal Inference

Adaptivity in Adaptive Submodularity

no code implementations9 Nov 2019 Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni

We propose an efficient semi adaptive policy that with $O(\log n \times\log k)$ adaptive rounds of observations can achieve an almost tight $1-1/e-\epsilon$ approximation guarantee with respect to an optimal policy that carries out $k$ actions in a fully sequential manner.

Active Learning Decision Making +1

Regret Bounds for Batched Bandits

no code implementations11 Oct 2019 Hossein Esfandiari, Amin Karbasi, Abbas Mehrabian, Vahab Mirrokni

We present simple and efficient algorithms for the batched stochastic multi-armed bandit and batched stochastic linear bandit problems.

Multi-Armed Bandits

Accelerating Gradient Boosting Machine

1 code implementation20 Mar 2019 Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab Mirrokni

This is the first GBM type of algorithm with theoretically-justified accelerated convergence rate.

Contextual Bandits with Cross-learning

no code implementations NeurIPS 2019 Santiago Balseiro, Negin Golrezaei, Mohammad Mahdian, Vahab Mirrokni, Jon Schneider

We consider the variant of this problem where in addition to receiving the reward $r_{i, t}(c)$, the learner also learns the values of $r_{i, t}(c')$ for some other contexts $c'$ in set $\mathcal{O}_i(c)$; i. e., the rewards that would have been achieved by performing that action under different contexts $c'\in \mathcal{O}_i(c)$.

Multi-Armed Bandits

Parallel and Streaming Algorithms for K-Core Decomposition

no code implementations ICML 2018 Hossein Esfandiari, Silvio Lattanzi, Vahab Mirrokni

The $k$-core decomposition is a fundamental primitive in many machine learning and data mining applications.

Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions

2 code implementations ICML 2018 Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab Mirrokni

Consider the following class of learning schemes: $$\hat{\boldsymbol{\beta}} := \arg\min_{\boldsymbol{\beta}}\;\sum_{j=1}^n \ell(\boldsymbol{x}_j^\top\boldsymbol{\beta}; y_j) + \lambda R(\boldsymbol{\beta}),\qquad\qquad (1) $$ where $\boldsymbol{x}_i \in \mathbb{R}^p$ and $y_i \in \mathbb{R}$ denote the $i^{\text{th}}$ feature and response variable respectively.

Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy

no code implementations ICML 2018 Shipra Agrawal, Morteza Zadimoghaddam, Vahab Mirrokni

Inspired by many applications of bipartite matching in online advertising and machine learning, we study a simple and natural iterative proportional allocation algorithm: Maintain a priority score $\priority_a$ for each node $a\in \mathds{A}$ on one side of the bipartition, initialized as $\priority_a=1$.

Fairness

Accelerating Greedy Coordinate Descent Methods

no code implementations ICML 2018 Haihao Lu, Robert Freund, Vahab Mirrokni

On the empirical side, while both AGCD and ASCD outperform Accelerated Randomized Coordinate Descent on most instances in our numerical experiments, we note that AGCD significantly outperforms the other two methods in our experiments, in spite of a lack of theoretical guarantees for this method.

Stochastic bandits robust to adversarial corruptions

no code implementations25 Mar 2018 Thodoris Lykouris, Vahab Mirrokni, Renato Paes Leme

We introduce a new model of stochastic bandits with adversarial corruptions which aims to capture settings where most of the input follows a stochastic pattern but some fraction of it can be adversarially changed to trick the algorithm, e. g., click fraud, fake reviews and email spam.

Dynamic Revenue Sharing

no code implementations NeurIPS 2017 Santiago Balseiro, Max Lin, Vahab Mirrokni, Renato Leme, Iiis Song Zuo

In this paper, we characterize the optimal revenue sharing scheme that satisfies both constraints in expectation.

Matroids Hitting Sets and Unsupervised Dependency Grammar Induction

no code implementations24 May 2017 Nicholas Harvey, Vahab Mirrokni, David Karger, Virginia Savova, Leonid Peshkin

This paper formulates a novel problem on graphs: find the minimal subset of edges in a fully connected graph, such that the resulting graph contains all spanning trees for a set of specifed sub-graphs.

Dependency Grammar Induction

Bi-Objective Online Matching and Submodular Allocations

no code implementations NeurIPS 2016 Hossein Esfandiari, Nitish Korula, Vahab Mirrokni

In particular, in online advertising it is fairly common to optimize multiple metrics, such as clicks, conversions, and impressions, as well as other metrics which may be largely uncorrelated such as ‘share of voice’, and ‘buyer surplus’.

Consistent Hashing with Bounded Loads

1 code implementation3 Aug 2016 Vahab Mirrokni, Mikkel Thorup, Morteza Zadimoghaddam

Designing algorithms for balanced allocation of clients to servers in dynamic settings is a challenging problem for a variety of reasons.

Data Structures and Algorithms

Tight Bounds for Approximate Carathéodory and Beyond

no code implementations ICML 2017 Vahab Mirrokni, Renato Paes Leme, Adrian Vladu, Sam Chiu-wai Wong

We give a deterministic nearly-linear time algorithm for approximating any point inside a convex polytope with a sparse convex combination of the polytope's vertices.

Distributed Balanced Clustering via Mapping Coresets

no code implementations NeurIPS 2014 Mohammadhossein Bateni, Aditya Bhaskara, Silvio Lattanzi, Vahab Mirrokni

Large-scale clustering of data points in metric spaces is an important problem in mining big data sets.

Local Graph Clustering Beyond Cheeger's Inequality

no code implementations30 Apr 2013 Zeyuan Allen Zhu, Silvio Lattanzi, Vahab Mirrokni

We also prove that our analysis is tight, and perform empirical evaluation to support our theory on both synthetic and real data.

Graph Clustering

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