Vahab Mirrokni

Publication Activity (10 Years)

Years Active: 2022-2024
Publications (10 Years): 92

Top Topics

Differentially Private

Mechanism Design

Hierarchical Agglomerative

Top Venues

Publications

Kyriakos Axiotis, Vincent Cohen-Addad, Monika Henzinger, Sammy Jerome, Vahab Mirrokni, David Saulpic, David P. Woodruff, Michael Wunder
Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond. CoRR (2024)
Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni, David P. Woodruff, Peilin Zhong
Optimal Communication for Classic Functions in the Coordinator Model and Beyond. CoRR (2024)
Gene Li, Lin Chen, Adel Javanmard, Vahab Mirrokni
Optimistic Rates for Learning from Label Proportions. CoRR (2024)
Amir Zandieh, Insu Han, Vahab Mirrokni, Amin Karbasi
SubGen: Token Generation in Sublinear Time and Memory. CoRR (2024)
Alessandro Epasto, Hossein Esfandiari, Vahab Mirrokni, Andrés Muñoz Medina
Smooth Anonymity for Sparse Graphs. WWW (Companion Volume) (2024)
Clayton Sanford, Bahare Fatemi, Ethan Hall, Anton Tsitsulin, Seyed Mehran Kazemi, Jonathan Halcrow, Bryan Perozzi, Vahab Mirrokni
Understanding Transformer Reasoning Capabilities via Graph Algorithms. CoRR (2024)
Rajesh Jayaram, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong
Massively Parallel Algorithms for High-Dimensional Euclidean Minimum Spanning Tree. SODA (2024)
Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni, David P. Woodruff, Peilin Zhong
Optimal Communication Bounds for Classic Functions in the Coordinator Model and Beyond. STOC (2024)
Paul Dütting, Vahab Mirrokni, Renato Paes Leme, Haifeng Xu, Song Zuo
Mechanism Design for Large Language Models. WWW (2024)
Laxman Dhulipala, Jakub Lacki, Jason Lee, Vahab Mirrokni
TeraHAC: Hierarchical Agglomerative Clustering of Trillion-Edge Graphs (Abstract). HOPC@SPAA (2024)
Adel Javanmard, Lin Chen, Vahab Mirrokni, Ashwinkumar Badanidiyuru, Gang Fu
Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions. CoRR (2024)
Rudrajit Das, Inderjit S. Dhillon, Alessandro Epasto, Adel Javanmard, Jieming Mao, Vahab Mirrokni, Sujay Sanghavi, Peilin Zhong
Retraining with Predicted Hard Labels Provably Increases Model Accuracy. CoRR (2024)
Yuan Deng, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo
Efficiency of the Generalized Second-Price Auction for Value Maximizers. WWW (2024)
Insu Han, Rajesh Jayaram, Amin Karbasi, Vahab Mirrokni, David P. Woodruff, Amir Zandieh
HyperAttention: Long-context Attention in Near-Linear Time. ICLR (2024)
Adel Javanmard, Matthew Fahrbach, Vahab Mirrokni
PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses. CoRR (2024)
Hossein Esfandiari, Vahab Mirrokni, Praneeth Kacham, David P. Woodruff, Peilin Zhong
High-Dimensional Geometric Streaming for Nearly Low Rank Data. CoRR (2024)
Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto, Vahab Mirrokni, Peilin Zhong
Perturb-and-Project: Differentially Private Similarities and Marginals. CoRR (2024)
Adel Javanmard, Lin Chen, Vahab Mirrokni, Ashwinkumar Badanidiyuru, Gang Fu
Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions. ICLR (2024)
Yuan Deng, Jieming Mao, Vahab Mirrokni, Yifeng Teng, Song Zuo
Non-uniform Bid-scaling and Equilibria for Different Auctions: An Empirical Study. WWW (2024)
Taisuke Yasuda, Kyriakos Axiotis, Gang Fu, MohammadHossein Bateni, Vahab Mirrokni
SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization. CoRR (2024)
Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni
Individual Welfare Guarantees in the Autobidding World with Machine-learned Advice. WWW (2024)
Gene Li, Lin Chen, Adel Javanmard, Vahab Mirrokni
Optimistic Rates for Learning from Label Proportions. COLT (2024)
Laxman Dhulipala, Majid Hadian, Rajesh Jayaram, Jason Lee, Vahab Mirrokni
MUVERA: Multi-Vector Retrieval via Fixed Dimensional Encodings. CoRR (2024)
Matthew Fahrbach, Adel Javanmard, Vahab Mirrokni, Pratik Worah
Learning Rate Schedules in the Presence of Distribution Shift. CoRR (2023)
Laxman Dhulipala, Jason Lee, Jakub Lacki, Vahab Mirrokni
TeraHAC: Hierarchical Agglomerative Clustering of Trillion-Edge Graphs. CoRR (2023)
Rajesh Jayaram, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong
Massively Parallel Algorithms for High-Dimensional Euclidean Minimum Spanning Tree. CoRR (2023)
Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni
Multi-channel Autobidding with Budget and ROI Constraints. ICML (2023)
Evan Munro, David Jones, Jennifer Brennan, Roland Nelet, Vahab Mirrokni, Jean Pouget-Abadie
Causal Estimation of User Learning in Personalized Systems. CoRR (2023)
Alessandro Epasto, Jieming Mao, Andres Muñoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, Peilin Zhong
Differentially Private Continual Releases of Streaming Frequency Moment Estimations. CoRR (2023)
Sara Ahmadian, Hossein Esfandiari, Vahab Mirrokni, Binghui Peng
Robust Load Balancing with Machine Learned Advice. J. Mach. Learn. Res. 24 (2023)
Adel Javanmard, Vahab Mirrokni
Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization. NeurIPS (2023)
Matthew Fahrbach, Adel Javanmard, Vahab Mirrokni, Pratik Worah
Learning Rate Schedules in the Presence of Distribution Shift. ICML (2023)
Yuan Deng, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo
Autobidding Auctions in the Presence of User Costs. CoRR (2023)
Yuan Deng, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo
Efficiency of the Generalized Second-Price Auction for Value Maximizers. CoRR (2023)
Jacob Imola, Alessandro Epasto, Mohammad Mahdian, Vincent Cohen-Addad, Vahab Mirrokni
Differentially-Private Hierarchical Clustering with Provable Approximation Guarantees. CoRR (2023)
Mehrdad Ghadiri, Matthew Fahrbach, Gang Fu, Vahab Mirrokni
Approximately Optimal Core Shapes for Tensor Decompositions. CoRR (2023)
Taisuke Yasuda, Mohammad Hossein Bateni, Lin Chen, Matthew Fahrbach, Gang Fu, Vahab Mirrokni
Sequential Attention for Feature Selection. ICLR (2023)
Santiago R. Balseiro, Kshipra Bhawalkar, Zhe Feng, Haihao Lu, Vahab Mirrokni, Balasubramanian Sivan, Di Wang
Joint Feedback Loop for Spend and Return-On-Spend Constraints. CoRR (2023)
Vasileios Charisopoulos, Hossein Esfandiari, Vahab Mirrokni
Robust and private stochastic linear bandits. ICML (2023)
Insu Han, Rajesh Jayaram, Amin Karbasi, Vahab Mirrokni, David P. Woodruff, Amir Zandieh
HyperAttention: Long-context Attention in Near-Linear Time. CoRR (2023)
CJ Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andres Muñoz Medina, Vahab Mirrokni, Gabriel Henrique Nunes, Sergei Vassilvitskii, Peilin Zhong
Measuring Re-identification Risk. Proc. ACM Manag. Data 1 (2) (2023)
Jacob Imola, Alessandro Epasto, Mohammad Mahdian, Vincent Cohen-Addad, Vahab Mirrokni
Differentially Private Hierarchical Clustering with Provable Approximation Guarantees. ICML (2023)
Lin Chen, Gang Fu, Amin Karbasi, Vahab Mirrokni
Learning from Aggregated Data: Curated Bags versus Random Bags. CoRR (2023)
CJ Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andrés Muñoz Medina, Vahab Mirrokni, Gabriel Henrique Nunes, Sergei Vassilvitskii, Peilin Zhong
Measuring Re-identification Risk. CoRR (2023)
Praneeth Kacham, Vahab Mirrokni, Peilin Zhong
PolySketchFormer: Fast Transformers via Sketches for Polynomial Kernels. CoRR (2023)
Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni, Grigoris Velegkas, Felix Zhou
Replicable Clustering. CoRR (2023)
Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni
Multi-channel Autobidding with Budget and ROI Constraints. CoRR (2023)
Santiago R. Balseiro, Negin Golrezaei, Mohammad Mahdian, Vahab Mirrokni, Jon Schneider
Contextual Bandits with Cross-Learning. Math. Oper. Res. 48 (3) (2023)
Mehrdad Ghadiri, Matthew Fahrbach, Gang Fu, Vahab Mirrokni
Approximately Optimal Core Shapes for Tensor Decompositions. ICML (2023)
MohammadHossein Bateni, Hossein Esfandiari, Hendrik Fichtenberger, Monika Henzinger, Rajesh Jayaram, Vahab Mirrokni, Andreas Wiese
-Center Clustering for Adaptive and Oblivious Adversaries. SODA (2023)
Santiago R. Balseiro, Rachitesh Kumar, Vahab Mirrokni, Balasubramanian Sivan, Di Wang
Robust Budget Pacing with a Single Sample. ICML (2023)
Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni
Pricing against a Budget and ROI Constrained Buyer. AISTATS (2023)
Adel Javanmard, Vahab Mirrokni
Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization. CoRR (2023)
Paul Duetting, Vahab Mirrokni, Renato Paes Leme, Haifeng Xu, Song Zuo
Mechanism Design for Large Language Models. CoRR (2023)
Santiago R. Balseiro, Rachitesh Kumar, Vahab Mirrokni, Balasubramanian Sivan, Di Wang
Robust Budget Pacing with a Single Sample. CoRR (2023)
Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab Mirrokni
The landscape of the proximal point method for nonconvex-nonconcave minimax optimization. Math. Program. 201 (1) (2023)
Santiago R. Balseiro, Haihao Lu, Vahab Mirrokni
The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems. Oper. Res. 71 (1) (2023)
Vasileios Charisopoulos, Hossein Esfandiari, Vahab Mirrokni
Robust and differentially private stochastic linear bandits. CoRR (2023)
Mehran Kazemi, Anton Tsitsulin, Hossein Esfandiari, MohammadHossein Bateni, Deepak Ramachandran, Bryan Perozzi, Vahab Mirrokni
Tackling Provably Hard Representative Selection via Graph Neural Networks. Trans. Mach. Learn. Res. 2023 (2023)
Alessandro Epasto, Jieming Mao, Andres Muñoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, Peilin Zhong
Differentially Private Continual Releases of Streaming Frequency Moment Estimations. ITCS (2023)
Laxman Dhulipala, Jakub Lacki, Jason Lee, Vahab Mirrokni
TeraHAC: Hierarchical Agglomerative Clustering of Trillion-Edge Graphs. Proc. ACM Manag. Data 1 (3) (2023)
Adel Javanmard, Vahab Mirrokni, Jean Pouget-Abadie
Causal Inference with Differentially Private (Clustered) Outcomes. CoRR (2023)
Evan Munro, David Jones, Jennifer Brennan, Roland Nelet, Vahab Mirrokni, Jean Pouget-Abadie
Causal Estimation of User Learning in Personalized Systems. EC (2023)
Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni, Grigoris Velegkas, Felix Zhou
Replicable Clustering. NeurIPS (2023)
MohammadHossein Bateni, Hossein Esfandiari, Hendrik Fichtenberger, Monika Henzinger, Rajesh Jayaram, Vahab Mirrokni, Andreas Wiese
Optimal Fully Dynamic k-Center Clustering for Adaptive and Oblivious Adversaries. CoRR (2023)
Hossein Esfandiari, Vahab Mirrokni, Peilin Zhong
Brief Announcement: Streaming Balanced Clustering. SPAA (2023)
Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong
k-Means Clustering with Distance-Based Privacy. NeurIPS (2023)
Hossein Esfandiari, Alkis Kalavasis, Amin Karbasi, Andreas Krause, Vahab Mirrokni, Grigoris Velegkas
Replicable Bandits. ICLR (2023)
Yuan Deng, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo
Autobidding Auctions in the Presence of User Costs. WWW (2023)
Yangsibo Huang, Haotian Jiang, Daogao Liu, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni
Learning across Data Owners with Joint Differential Privacy. CoRR (2023)
Yuan Deng, Jieming Mao, Vahab Mirrokni, Yifeng Teng, Song Zuo
Non-uniform Bid-scaling and Equilibria for Different Auctions: An Empirical Study. CoRR (2023)
CJ Carey, Jonathan Halcrow, Rajesh Jayaram, Vahab Mirrokni, Warren Schudy, Peilin Zhong
Stars: Tera-Scale Graph Building for Clustering and Graph Learning. CoRR (2022)
Christopher Harshaw, Fredrik Sävje, David Eisenstat, Vahab Mirrokni, Jean Pouget-Abadie
Design and Analysis of Bipartite Experiments Under a Linear Exposure-response Model. EC (2022)
Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank. CoRR (2022)
Santiago R. Balseiro, Vahab Mirrokni, Renato Paes Leme, Song Zuo
Dynamic Double Auctions: Toward First Best. Oper. Res. 70 (4) (2022)
Hossein Esfandiari, Alkis Kalavasis, Amin Karbasi, Andreas Krause, Vahab Mirrokni, Grigoris Velegkas
Reproducible Bandits. CoRR (2022)
Sepehr Assadi, Vaggos Chatziafratis, Jakub Lacki, Vahab Mirrokni, Chen Wang
Hierarchical Clustering in Graph Streams: Single-Pass Algorithms and Space Lower Bounds. COLT (2022)
Mahsa Derakhshan, Negin Golrezaei, Vahideh H. Manshadi, Vahab Mirrokni
Product Ranking on Online Platforms. Manag. Sci. 68 (6) (2022)
Jennifer Brennan, Vahab Mirrokni, Jean Pouget-Abadie
Cluster Randomized Designs for One-Sided Bipartite Experiments. NeurIPS (2022)
Hossein Esfandiari, Vahab Mirrokni, Jon Schneider
Anonymous Bandits for Multi-User Systems. NeurIPS (2022)
Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi, Vahab Mirrokni, Andres Muñoz Medina, David Saulpic, Chris Schwiegelshohn, Sergei Vassilvitskii
Scalable Differentially Private Clustering via Hierarchically Separated Trees. KDD (2022)
Yuan Deng, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo
Efficiency of the First-Price Auction in the Autobidding World. CoRR (2022)
Hossein Esfandiari, Vahab Mirrokni, Jon Schneider
Anonymous Bandits for Multi-User Systems. CoRR (2022)
CJ Carey, Jonathan Halcrow, Rajesh Jayaram, Vahab Mirrokni, Warren Schudy, Peilin Zhong
Stars: Tera-Scale Graph Building for Clustering and Learning. NeurIPS (2022)
Yuan Deng, Vahab Mirrokni, Hanrui Zhang
Posted Pricing and Dynamic Prior-independent Mechanisms with Value Maximizers. NeurIPS (2022)
Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni
Fairness in the Autobidding World with Machine-learned Advice. CoRR (2022)
MohammadHossein Bateni, Lin Chen, Matthew Fahrbach, Gang Fu, Vahab Mirrokni, Taisuke Yasuda
Sequential Attention for Feature Selection. CoRR (2022)
Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank. NeurIPS (2022)
Vincent Cohen-Addad, Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong
Near-Optimal Private and Scalable $k$-Clustering. NeurIPS (2022)
Laxman Dhulipala, David Eisenstat, Jakub Lacki, Vahab Mirrokni, Jessica Shi
Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic Depth. CoRR (2022)
Jakub Lacki, Vahab Mirrokni, Christian Sohler
Constant Approximation for Normalized Modularity and Associations Clustering. CoRR (2022)
Laxman Dhulipala, David Eisenstat, Jakub Lacki, Vahab Mirrokni, Jessica Shi
Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic Depth. NeurIPS (2022)