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ORCIDVahab S. Mirrokni
Vahab Mirrokni – Seyed Vahab Mirrokni
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- affiliation: Google Research, New York City, USA
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2020 – today
- 2024
[c231]Gene Li, Lin Chen, Adel Javanmard, Vahab Mirrokni:
Optimistic Rates for Learning from Label Proportions. COLT 2024: 3437-3474- [c230]Laxman Dhulipala, Jakub Lacki, Jason Lee, Vahab Mirrokni:
TeraHAC: Hierarchical Agglomerative Clustering of Trillion-Edge Graphs (Abstract). HOPC@SPAA 2024 - [c229]Insu Han, Rajesh Jayaram, Amin Karbasi, Vahab Mirrokni, David P. Woodruff, Amir Zandieh
:
HyperAttention: Long-context Attention in Near-Linear Time. ICLR 2024 - [c228]Adel Javanmard, Lin Chen, Vahab Mirrokni, Ashwinkumar Badanidiyuru, Gang Fu:
Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions. ICLR 2024 - [c227]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. ICML 2024 - [c226]Santiago R. Balseiro, Kshipra Bhawalkar, Zhe Feng, Haihao Lu, Vahab Mirrokni, Balasubramanian Sivan, Di Wang:
A Field Guide for Pacing Budget and ROS Constraints. ICML 2024 - [c225]Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto, Vahab Mirrokni, Peilin Zhong:
Perturb-and-Project: Differentially Private Similarities and Marginals. ICML 2024 - [c224]Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni, David P. Woodruff, Peilin Zhong:
High-Dimensional Geometric Streaming for Nearly Low Rank Data. ICML 2024 - [c223]Adel Javanmard, Matthew Fahrbach, Vahab Mirrokni:
PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses. ICML 2024 - [c222]Praneeth Kacham, Vahab Mirrokni, Peilin Zhong:
PolySketchFormer: Fast Transformers via Sketching Polynomial Kernels. ICML 2024 - [c221]Rajesh Jayaram, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong:
Massively Parallel Algorithms for High-Dimensional Euclidean Minimum Spanning Tree. SODA 2024: 3960-3996 - [c220]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: 1911-1922 - [c219]Yuan Deng, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo:
Efficiency of the Generalized Second-Price Auction for Value Maximizers. WWW 2024: 46-56 - [c218]Paul Dütting, Vahab Mirrokni, Renato Paes Leme, Haifeng Xu, Song Zuo:
Mechanism Design for Large Language Models. WWW 2024: 144-155 - [c217]Yuan Deng, Jieming Mao, Vahab Mirrokni, Yifeng Teng, Song Zuo:
Non-uniform Bid-scaling and Equilibria for Different Auctions: An Empirical Study. WWW 2024: 256-266 - [c216]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: 267-275 - [c215]Alessandro Epasto, Hossein Esfandiari, Vahab Mirrokni, Andrés Muñoz Medina:
Smooth Anonymity for Sparse Graphs. WWW (Companion Volume) 2024: 621-624 - [i132]Adel Javanmard, Lin Chen, Vahab Mirrokni, Ashwinkumar Badanidiyuru, Gang Fu:
Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions. CoRR abs/2401.11081 (2024) - [i131]Adel Javanmard, Matthew Fahrbach, Vahab Mirrokni:
PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses. CoRR abs/2402.04987 (2024) - [i130]Amir Zandieh, Insu Han, Vahab Mirrokni, Amin Karbasi:
SubGen: Token Generation in Sublinear Time and Memory. CoRR abs/2402.06082 (2024) - [i129]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 abs/2402.17327 (2024) - [i128]Taisuke Yasuda, Kyriakos Axiotis, Gang Fu, MohammadHossein Bateni, Vahab Mirrokni:
SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization. CoRR abs/2402.17902 (2024) - [i127]Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni, David P. Woodruff, Peilin Zhong:
Optimal Communication for Classic Functions in the Coordinator Model and Beyond. CoRR abs/2403.20307 (2024) - [i126]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 abs/2405.18512 (2024) - [i125]Laxman Dhulipala, Majid Hadian, Rajesh Jayaram, Jason Lee, Vahab Mirrokni:
MUVERA: Multi-Vector Retrieval via Fixed Dimensional Encodings. CoRR abs/2405.19504 (2024) - [i124]Gene Li, Lin Chen, Adel Javanmard, Vahab Mirrokni:
Optimistic Rates for Learning from Label Proportions. CoRR abs/2406.00487 (2024) - [i123]Hossein Esfandiari, Vahab Mirrokni, Praneeth Kacham, David P. Woodruff, Peilin Zhong:
High-Dimensional Geometric Streaming for Nearly Low Rank Data. CoRR abs/2406.02910 (2024) - [i122]Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto, Vahab Mirrokni, Peilin Zhong:
Perturb-and-Project: Differentially Private Similarities and Marginals. CoRR abs/2406.04868 (2024) - [i121]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 abs/2406.11206 (2024) - [i120]Amir Azarmehr, Soheil Behnezhad, Rajesh Jayaram, Jakub Lacki, Vahab Mirrokni, Peilin Zhong:
Massively Parallel Minimum Spanning Tree in General Metric Spaces. CoRR abs/2408.06455 (2024) - [i119]Gagan Aggarwal, Ashwinkumar Badanidiyuru, Santiago R. Balseiro, Kshipra Bhawalkar, Yuan Deng, Zhe Feng, Gagan Goel, Christopher Liaw, Haihao Lu, Mohammad Mahdian, Jieming Mao, Aranyak Mehta, Vahab Mirrokni, Renato Paes Leme, Andrés Perlroth, Georgios Piliouras, Jon Schneider, Ariel Schvartzman, Balasubramanian Sivan, Kelly Spendlove, Yifeng Teng, Di Wang, Hanrui Zhang, Mingfei Zhao, Wennan Zhu, Song Zuo:
Auto-bidding and Auctions in Online Advertising: A Survey. CoRR abs/2408.07685 (2024) - [i118]Xinwei Zhang, Zhiqi Bu, Borja Balle, Mingyi Hong, Meisam Razaviyayn, Vahab Mirrokni:
DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction. CoRR abs/2410.03883 (2024) - [i117]Zeman Li, Xinwei Zhang, Peilin Zhong, Yuan Deng, Meisam Razaviyayn, Vahab Mirrokni:
Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models. CoRR abs/2410.06441 (2024) - 2023
- [j68]Santiago R. Balseiro, Haihao Lu, Vahab Mirrokni:
The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems. Oper. Res. 71(1): 101-119 (2023) - [j67]Sara Ahmadian, Hossein Esfandiari, Vahab Mirrokni, Binghui Peng:
Robust Load Balancing with Machine Learned Advice. J. Mach. Learn. Res. 24: 44:1-44:46 (2023) - [j66]Santiago R. Balseiro, Negin Golrezaei, Mohammad Mahdian, Vahab Mirrokni, Jon Schneider:
Contextual Bandits with Cross-Learning. Math. Oper. Res. 48(3): 1607-1629 (2023) - [j65]Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab Mirrokni:
The landscape of the proximal point method for nonconvex-nonconcave minimax optimization. Math. Program. 201(1): 373-407 (2023) - [j64]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): 149:1-149:26 (2023) - [j63]Laxman Dhulipala, Jakub Lacki, Jason Lee, Vahab Mirrokni:
TeraHAC: Hierarchical Agglomerative Clustering of Trillion-Edge Graphs. Proc. ACM Manag. Data 1(3): 221:1-221:27 (2023) - [j62]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) - [c214]Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni:
Pricing against a Budget and ROI Constrained Buyer. AISTATS 2023: 9282-9307 - [c213]Taisuke Yasuda, Mohammad Hossein Bateni, Lin Chen, Matthew Fahrbach, Gang Fu, Vahab Mirrokni:
Sequential Attention for Feature Selection. ICLR 2023 - [c212]Hossein Esfandiari, Alkis Kalavasis, Amin Karbasi, Andreas Krause, Vahab Mirrokni, Grigoris Velegkas:
Replicable Bandits. ICLR 2023 - [c211]Santiago R. Balseiro, Rachitesh Kumar, Vahab Mirrokni, Balasubramanian Sivan, Di Wang:
Robust Budget Pacing with a Single Sample. ICML 2023: 1636-1659 - [c210]Vasileios Charisopoulos, Hossein Esfandiari, Vahab Mirrokni:
Robust and private stochastic linear bandits. ICML 2023: 4096-4115 - [c209]Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni:
Multi-channel Autobidding with Budget and ROI Constraints. ICML 2023: 7617-7644 - [c208]Matthew Fahrbach, Adel Javanmard, Vahab Mirrokni, Pratik Worah:
Learning Rate Schedules in the Presence of Distribution Shift. ICML 2023: 9523-9546 - [c207]Mehrdad Ghadiri, Matthew Fahrbach, Gang Fu, Vahab Mirrokni:
Approximately Optimal Core Shapes for Tensor Decompositions. ICML 2023: 11237-11254 - [c206]Jacob Imola, Alessandro Epasto, Mohammad Mahdian, Vincent Cohen-Addad, Vahab Mirrokni:
Differentially Private Hierarchical Clustering with Provable Approximation Guarantees. ICML 2023: 14353-14375 - [c205]Alessandro Epasto, Jieming Mao, Andres Muñoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, Peilin Zhong:
Differentially Private Continual Releases of Streaming Frequency Moment Estimations. ITCS 2023: 48:1-48:24 - [c204]Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong:
k-Means Clustering with Distance-Based Privacy. NeurIPS 2023 - [c203]Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni, Grigoris Velegkas, Felix Zhou:
Replicable Clustering. NeurIPS 2023 - [c202]Adel Javanmard, Vahab Mirrokni:
Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization. NeurIPS 2023 - [c201]Evan Munro, David Jones, Jennifer Brennan, Roland Nelet, Vahab Mirrokni, Jean Pouget-Abadie:
Causal Estimation of User Learning in Personalized Systems. EC 2023: 992-1016 - [c200]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. SODA 2023: 2677-2727 - [c199]Hossein Esfandiari, Vahab Mirrokni, Peilin Zhong:
Brief Announcement: Streaming Balanced Clustering. SPAA 2023: 311-314 - [c198]Yuan Deng, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo:
Autobidding Auctions in the Presence of User Costs. WWW 2023: 3428-3435 - [i116]Alessandro Epasto, Jieming Mao, Andres Muñoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, Peilin Zhong:
Differentially Private Continual Releases of Streaming Frequency Moment Estimations. CoRR abs/2301.05605 (2023) - [i115]Jacob Imola, Alessandro Epasto, Mohammad Mahdian, Vincent Cohen-Addad, Vahab Mirrokni:
Differentially-Private Hierarchical Clustering with Provable Approximation Guarantees. CoRR abs/2302.00037 (2023) - [i114]Yuan Deng, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo:
Autobidding Auctions in the Presence of User Costs. CoRR abs/2302.00377 (2023) - [i113]Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni:
Multi-channel Autobidding with Budget and ROI Constraints. CoRR abs/2302.01523 (2023) - [i112]Santiago R. Balseiro, Rachitesh Kumar, Vahab Mirrokni, Balasubramanian Sivan, Di Wang:
Robust Budget Pacing with a Single Sample. CoRR abs/2302.02006 (2023) - [i111]Mehrdad Ghadiri, Matthew Fahrbach, Gang Fu, Vahab Mirrokni:
Approximately Optimal Core Shapes for Tensor Decompositions. CoRR abs/2302.03886 (2023) - [i110]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 abs/2302.08530 (2023) - [i109]Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni, Grigoris Velegkas, Felix Zhou:
Replicable Clustering. CoRR abs/2302.10359 (2023) - [i108]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 abs/2303.11843 (2023) - [i107]Matthew Fahrbach, Adel Javanmard, Vahab Mirrokni, Pratik Worah:
Learning Rate Schedules in the Presence of Distribution Shift. CoRR abs/2303.15634 (2023) - [i106]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 abs/2304.07210 (2023) - [i105]Vasileios Charisopoulos, Hossein Esfandiari, Vahab Mirrokni:
Robust and differentially private stochastic linear bandits. CoRR abs/2304.11741 (2023) - [i104]Lin Chen, Gang Fu, Amin Karbasi, Vahab Mirrokni:
Learning from Aggregated Data: Curated Bags versus Random Bags. CoRR abs/2305.09557 (2023) - [i103]Yangsibo Huang, Haotian Jiang, Daogao Liu, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni:
Learning across Data Owners with Joint Differential Privacy. CoRR abs/2305.15723 (2023) - [i102]Evan Munro, David Jones, Jennifer Brennan, Roland Nelet, Vahab Mirrokni, Jean Pouget-Abadie:
Causal Estimation of User Learning in Personalized Systems. CoRR abs/2306.00485 (2023) - [i101]Rajesh Jayaram, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong:
Massively Parallel Algorithms for High-Dimensional Euclidean Minimum Spanning Tree. CoRR abs/2308.00503 (2023) - [i100]Adel Javanmard, Vahab Mirrokni, Jean Pouget-Abadie:
Causal Inference with Differentially Private (Clustered) Outcomes. CoRR abs/2308.00957 (2023) - [i99]Laxman Dhulipala, Jason Lee, Jakub Lacki, Vahab Mirrokni:
TeraHAC: Hierarchical Agglomerative Clustering of Trillion-Edge Graphs. CoRR abs/2308.03578 (2023) - [i98]Praneeth Kacham, Vahab Mirrokni, Peilin Zhong:
PolySketchFormer: Fast Transformers via Sketches for Polynomial Kernels. CoRR abs/2310.01655 (2023) - [i97]Yuan Deng, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo:
Efficiency of the Generalized Second-Price Auction for Value Maximizers. CoRR abs/2310.03105 (2023) - [i96]Adel Javanmard, Vahab Mirrokni:
Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization. CoRR abs/2310.04015 (2023) - [i95]Insu Han, Rajesh Jayaram, Amin Karbasi, Vahab Mirrokni, David P. Woodruff, Amir Zandieh:
HyperAttention: Long-context Attention in Near-Linear Time. CoRR abs/2310.05869 (2023) - [i94]Paul Duetting, Vahab Mirrokni, Renato Paes Leme, Haifeng Xu, Song Zuo:
Mechanism Design for Large Language Models. CoRR abs/2310.10826 (2023) - [i93]Yuan Deng, Jieming Mao, Vahab Mirrokni, Yifeng Teng, Song Zuo:
Non-uniform Bid-scaling and Equilibria for Different Auctions: An Empirical Study. CoRR abs/2311.10679 (2023) - 2022
- [j61]MohammadHossein Bateni, Yiwei Chen, Dragos Florin Ciocan, Vahab S. Mirrokni:
Fair Resource Allocation in a Volatile Marketplace. Oper. Res. 70(1): 288-308 (2022) - [j60]Santiago R. Balseiro, Vahab Mirrokni, Renato Paes Leme, Song Zuo:
Dynamic Double Auctions: Toward First Best. Oper. Res. 70(4): 2299-2317 (2022) - [j59]Mahsa Derakhshan, Negin Golrezaei, Vahideh H. Manshadi, Vahab Mirrokni:
Product Ranking on Online Platforms. Manag. Sci. 68(6): 4024-4041 (2022) - [c197]Hossein Esfandiari, Vahab S. Mirrokni, Umar Syed, Sergei Vassilvitskii:
Label differential privacy via clustering. AISTATS 2022: 7055-7075 - [c196]Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab S. Mirrokni:
Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems. ALT 2022: 465-487 - [c195]Sepehr Assadi, Vaggos Chatziafratis, Jakub Lacki, Vahab Mirrokni, Chen Wang:
Hierarchical Clustering in Graph Streams: Single-Pass Algorithms and Space Lower Bounds. COLT 2022: 4643-4702 - [c194]Vincent Cohen-Addad, Vahab S. Mirrokni, Peilin Zhong:
Massively Parallel k-Means Clustering for Perturbation Resilient Instances. ICML 2022: 4180-4201 - [c193]Shyam Narayanan, Vahab S. Mirrokni, Hossein Esfandiari:
Tight and Robust Private Mean Estimation with Few Users. ICML 2022: 16383-16412 - [c192]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: 221-230 - [c191]Jennifer Brennan, Vahab Mirrokni, Jean Pouget-Abadie:
Cluster Randomized Designs for One-Sided Bipartite Experiments. NeurIPS 2022 - [c190]CJ Carey, Jonathan Halcrow, Rajesh Jayaram, Vahab Mirrokni, Warren Schudy, Peilin Zhong:
Stars: Tera-Scale Graph Building for Clustering and Learning. NeurIPS 2022 - [c189]Vincent Cohen-Addad, Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong:
Near-Optimal Private and Scalable $k$-Clustering. NeurIPS 2022 - [c188]Yuan Deng, Vahab Mirrokni, Hanrui Zhang:
Posted Pricing and Dynamic Prior-independent Mechanisms with Value Maximizers. NeurIPS 2022 - [c187]Laxman Dhulipala, David Eisenstat, Jakub Lacki, Vahab Mirrokni, Jessica Shi:
Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic Depth. NeurIPS 2022 - [c186]Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong:
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank. NeurIPS 2022 - [c185]Hossein Esfandiari, Vahab Mirrokni, Jon Schneider:
Anonymous Bandits for Multi-User Systems. NeurIPS 2022 - [c184]Santiago R. Balseiro, Yuan Deng, Jieming Mao, Vahab S. Mirrokni, Song Zuo:
Optimal Mechanisms for Value Maximizers with Budget Constraints via Target Clipping. EC 2022: 475 - [c183]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: 606 - [c182]Sara Ahmadian, Hossein Esfandiari, Vahab S. Mirrokni, Binghui Peng:
Robust Load Balancing with Machine Learned Advice. SODA 2022: 20-34 - [c181]Alessandro Epasto, Mohammad Mahdian, Vahab S. Mirrokni, Peilin Zhong:
Massively Parallel and Dynamic Algorithms for Minimum Size Clustering. SODA 2022: 1613-1660 - [c180]Hossein Esfandiari, Vahab S. Mirrokni, Shyam Narayanan:
Almost Tight Approximation Algorithms for Explainable Clustering. SODA 2022: 2641-2663 - [c179]Alessandro Epasto, Mohammad Mahdian, Vahab S. Mirrokni, Peilin Zhong:
Improved Sliding Window Algorithms for Clustering and Coverage via Bucketing-Based Sketches. SODA 2022: 3005-3042 - [c178]Vincent Cohen-Addad, Hossein Esfandiari, Vahab S. Mirrokni, Shyam Narayanan:
Improved approximations for Euclidean k-means and k-median, via nested quasi-independent sets. STOC 2022: 1621-1628 - [i92]Santiago R. Balseiro, Haihao Lu, Vahab S. Mirrokni, Balasubramanian Sivan:
From Online Optimization to PID Controllers: Mirror Descent with Momentum. CoRR abs/2202.06152 (2022) - [i91]Vincent Cohen-Addad, Hossein Esfandiari, Vahab S. Mirrokni, Shyam Narayanan:
Improved Approximations for Euclidean k-means and k-median, via Nested Quasi-Independent Sets. CoRR abs/2204.04828 (2022) - [i90]Seyed Mehran Kazemi, Anton Tsitsulin, Hossein Esfandiari, MohammadHossein Bateni, Deepak Ramachandran, Bryan Perozzi, Vahab S. Mirrokni:
Tackling Provably Hard Representative Selection via Graph Neural Networks. CoRR abs/2205.10403 (2022) - [i89]Sepehr Assadi, Vaggos Chatziafratis, Jakub Lacki, Vahab S. Mirrokni, Chen Wang:
Hierarchical Clustering in Graph Streams: Single-Pass Algorithms and Space Lower Bounds. CoRR abs/2206.07554 (2022) - [i88]Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi, Vahab S. Mirrokni, Andres Muñoz Medina, David Saulpic, Chris Schwiegelshohn, Sergei Vassilvitskii:
Scalable Differentially Private Clustering via Hierarchically Separated Trees. CoRR abs/2206.08646 (2022) - [i87]Laxman Dhulipala, David Eisenstat, Jakub Lacki, Vahab Mirrokni, Jessica Shi:
Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic Depth. CoRR abs/2206.11654 (2022) - [i86]Oleksandr Ferludin, Arno Eigenwillig, Martin Blais, Dustin Zelle, Jan Pfeifer, Alvaro Sanchez-Gonzalez, Wai Lok Sibon Li, Sami Abu-El-Haija, Peter W. Battaglia, Neslihan Bulut, Jonathan Halcrow, Filipe Miguel Gonçalves de Almeida, Silvio Lattanzi, André Linhares, Brandon A. Mayer, Vahab S. Mirrokni, John Palowitch, Mihir Paradkar, Jennifer She, Anton Tsitsulin, Kevin Villela, Lisa Wang, David Wong, Bryan Perozzi:
TF-GNN: Graph Neural Networks in TensorFlow. CoRR abs/2207.03522 (2022) - [i85]Hossein Esfandiari, Alessandro Epasto, Vahab S. Mirrokni, Andres Muñoz Medina, Sergei Vassilvitskii:
Smooth Anonymity for Sparse Binary Matrices. CoRR abs/2207.06358 (2022) - [i84]Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong:
Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank. CoRR abs/2207.06944 (2022) - [i83]Yuan Deng, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo:
Efficiency of the First-Price Auction in the Autobidding World. CoRR abs/2208.10650 (2022) - [i82]Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni:
Fairness in the Autobidding World with Machine-learned Advice. CoRR abs/2209.04748 (2022) - [i81]MohammadHossein Bateni, Lin Chen, Matthew Fahrbach, Gang Fu, Vahab Mirrokni, Taisuke Yasuda:
Sequential Attention for Feature Selection. CoRR abs/2209.14881 (2022) - [i80]Hossein Esfandiari, Alkis Kalavasis, Amin Karbasi, Andreas Krause, Vahab Mirrokni, Grigoris Velegkas:
Reproducible Bandits. CoRR abs/2210.01898 (2022) - [i79]Hossein Esfandiari, Vahab Mirrokni, Jon Schneider:
Anonymous Bandits for Multi-User Systems. CoRR abs/2210.12198 (2022) - [i78]CJ Carey, Jonathan Halcrow, Rajesh Jayaram, Vahab Mirrokni, Warren Schudy, Peilin Zhong:
Stars: Tera-Scale Graph Building for Clustering and Graph Learning. CoRR abs/2212.02635 (2022) - [i77]