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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
- 2023
[j66]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)- [j65]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) - [j64]Santiago R. Balseiro, Negin Golrezaei, Mohammad Mahdian, Vahab Mirrokni, Jon Schneider:
Contextual Bandits with Cross-Learning. Math. Oper. Res. 48(3): 1607-1629 (2023) - [j63]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) - [j62]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) - [c211]Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni:
Pricing against a Budget and ROI Constrained Buyer. AISTATS 2023: 9282-9307 - [c210]Taisuke Yasuda, Mohammad Hossein Bateni, Lin Chen, Matthew Fahrbach, Gang Fu, Vahab Mirrokni:
Sequential Attention for Feature Selection. ICLR 2023 - [c209]Hossein Esfandiari, Alkis Kalavasis, Amin Karbasi, Andreas Krause, Vahab Mirrokni, Grigoris Velegkas:
Replicable Bandits. ICLR 2023 - [c208]Santiago R. Balseiro, Rachitesh Kumar, Vahab Mirrokni, Balasubramanian Sivan, Di Wang:
Robust Budget Pacing with a Single Sample. ICML 2023: 1636-1659 - [c207]Vasileios Charisopoulos, Hossein Esfandiari, Vahab Mirrokni:
Robust and private stochastic linear bandits. ICML 2023: 4096-4115 - [c206]Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni:
Multi-channel Autobidding with Budget and ROI Constraints. ICML 2023: 7617-7644 - [c205]Matthew Fahrbach, Adel Javanmard, Vahab Mirrokni, Pratik Worah:
Learning Rate Schedules in the Presence of Distribution Shift. ICML 2023: 9523-9546 - [c204]Mehrdad Ghadiri, Matthew Fahrbach, Gang Fu, Vahab Mirrokni:
Approximately Optimal Core Shapes for Tensor Decompositions. ICML 2023: 11237-11254 - [c203]Jacob Imola, Alessandro Epasto, Mohammad Mahdian, Vincent Cohen-Addad, Vahab Mirrokni:
Differentially Private Hierarchical Clustering with Provable Approximation Guarantees. ICML 2023: 14353-14375 - [c202]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 - [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 - [i110]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) - [i109]Jacob Imola, Alessandro Epasto, Mohammad Mahdian, Vincent Cohen-Addad, Vahab Mirrokni:
Differentially-Private Hierarchical Clustering with Provable Approximation Guarantees. CoRR abs/2302.00037 (2023) - [i108]Yuan Deng, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo:
Autobidding Auctions in the Presence of User Costs. CoRR abs/2302.00377 (2023) - [i107]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) - [i106]Santiago R. Balseiro, Rachitesh Kumar, Vahab Mirrokni, Balasubramanian Sivan, Di Wang:
Robust Budget Pacing with a Single Sample. CoRR abs/2302.02006 (2023) - [i105]Mehrdad Ghadiri, Matthew Fahrbach, Gang Fu, Vahab Mirrokni:
Approximately Optimal Core Shapes for Tensor Decompositions. CoRR abs/2302.03886 (2023) - [i104]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) - [i103]Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni, Grigoris Velegkas, Felix Zhou:
Replicable Clustering. CoRR abs/2302.10359 (2023) - [i102]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) - [i101]Matthew Fahrbach, Adel Javanmard, Vahab Mirrokni, Pratik Worah:
Learning Rate Schedules in the Presence of Distribution Shift. CoRR abs/2303.15634 (2023) - [i100]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) - [i99]Vasileios Charisopoulos, Hossein Esfandiari, Vahab Mirrokni:
Robust and differentially private stochastic linear bandits. CoRR abs/2304.11741 (2023) - [i98]Lin Chen, Gang Fu, Amin Karbasi, Vahab Mirrokni:
Learning from Aggregated Data: Curated Bags versus Random Bags. CoRR abs/2305.09557 (2023) - [i97]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) - [i96]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) - [i95]Rajesh Jayaram, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong:
Massively Parallel Algorithms for High-Dimensional Euclidean Minimum Spanning Tree. CoRR abs/2308.00503 (2023) - [i94]Adel Javanmard, Vahab Mirrokni, Jean Pouget-Abadie:
Causal Inference with Differentially Private (Clustered) Outcomes. CoRR abs/2308.00957 (2023) - [i93]Laxman Dhulipala, Jason Lee, Jakub Lacki, Vahab Mirrokni:
TeraHAC: Hierarchical Agglomerative Clustering of Trillion-Edge Graphs. CoRR abs/2308.03578 (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]Jakub Lacki, Vahab Mirrokni, Christian Sohler:
Constant Approximation for Normalized Modularity and Associations Clustering. CoRR abs/2212.14334 (2022) - 2021
- [j58]Negin Golrezaei, Adel Javanmard, Vahab S. Mirrokni:
Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions. Oper. Res. 69(1): 297-314 (2021) - [j57]Santiago R. Balseiro, Anthony Kim, Mohammad Mahdian, Vahab S. Mirrokni:
Budget-Management Strategies in Repeated Auctions. Oper. Res. 69(3): 859-876 (2021) - [j56]Anthony Kim, Vahab S. Mirrokni, Hamid Nazerzadeh:
Deals or No Deals: Contract Design for Online Advertising. Oper. Res. 69(5): 1450-1467 (2021) - [j55]Jessica Shi, Laxman Dhulipala, David Eisenstat, Jakub Lacki, Vahab S. Mirrokni:
Scalable Community Detection via Parallel Correlation Clustering. Proc. VLDB Endow. 14(11): 2305-2313 (2021) - [j54]Soheil Behnezhad, Laxman Dhulipala, Hossein Esfandiari, Jakub Lacki, Vahab S. Mirrokni, Warren Schudy:
Massively Parallel Computation via Remote Memory Access. ACM Trans. Parallel Comput. 8(3): 13:1-13:25 (2021) - [c177]MohammadHossein Bateni, Hossein Esfandiari, Manuela Fischer, Vahab S. Mirrokni:
Extreme k-Center Clustering. AAAI 2021: 3941-3949 - [c176]Hossein Esfandiari, Amin Karbasi, Abbas Mehrabian, Vahab S. Mirrokni:
Regret Bounds for Batched Bandits. AAAI 2021: 7340-7348 - [c175]Hossein Esfandiari, Vahab S. Mirrokni, Peilin Zhong:
Almost Linear Time Density Level Set Estimation via DBSCAN. AAAI 2021: 7349-7357 - [c174]Hossein Esfandiari, Amin Karbasi, Vahab S. Mirrokni:
Adaptivity in Adaptive Submodularity. COLT 2021: 1823-1846 - [c173]Lin Chen, Hossein Esfandiari, Gang Fu, Vahab S. Mirrokni, Qian Yu:
Feature Cross Search via Submodular Optimization. ESA 2021: 31:1-31:16 - [c172]Santiago R. Balseiro, Haihao Lu, Vahab S. Mirrokni:
Regularized Online Allocation Problems: Fairness and Beyond. ICML 2021: 630-639 - [c171]Yuan Deng, Sébastien Lahaie, Vahab S. Mirrokni, Song Zuo:
Revenue-Incentive Tradeoffs in Dynamic Reserve Pricing. ICML 2021: 2601-2610 - [c170]Laxman Dhulipala, David Eisenstat, Jakub Lacki, Vahab S. Mirrokni, Jessica Shi:
Hierarchical Agglomerative Graph Clustering in Nearly-Linear Time. ICML 2021: 2676-2686 - [c169]Negin Golrezaei, Max Lin, Vahab S. Mirrokni, Hamid Nazerzadeh:
Boosted Second Price Auctions: Revenue Optimization for Heterogeneous Bidders. KDD 2021: 447-457 - [c168]Alessandro Epasto, Andrés Muñoz Medina, Steven Avery, Yijian Bai, Róbert Busa-Fekete, CJ Carey, Ya Gao, David Guthrie, Subham Ghosh, James Ioannidis, Junyi Jiao, Jakub Lacki, Jason Lee, Arne Mauser, Brian Milch, Vahab S. Mirrokni, Deepak Ravichandran, Wei Shi, Max Spero, Yunting Sun, Umar Syed, Sergei Vassilvitskii, Shuo Wang:
Clustering for Private Interest-based Advertising. KDD 2021: 2802-2810 - [c167]Nick Doudchenko, Khashayar Khosravi, Jean Pouget-Abadie, Sébastien Lahaie, Miles Lubin, Vahab S. Mirrokni, Jann Spiess, Guido Imbens:
Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls. NeurIPS 2021: 8691-8701 - [c166]Amin Karbasi, Vahab S. Mirrokni, Mohammad Shadravan:
Parallelizing Thompson Sampling. NeurIPS 2021: 10535-10548 - [c165]Santiago R. Balseiro, Yuan Deng, Jieming Mao, Vahab S. Mirrokni, Song Zuo:
Robust Auction Design in the Auto-bidding World. NeurIPS 2021: 17777-17788 - [c164]Shipra Agrawal, Eric Balkanski, Vahab S. Mirrokni, Balasubramanian Sivan:
Robust Repeated First Price Auctions. EC 2021: 4 - [c163]Santiago R. Balseiro, Yuan Deng, Jieming Mao, Vahab S. Mirrokni, Song Zuo:
The Landscape of Auto-bidding Auctions: Value versus Utility Maximization. EC 2021: 132-133 - [c162]Yuan Deng, Vahab S. Mirrokni, Song Zuo:
Non-Clairvoyant Dynamic Mechanism Design with Budget Constraints and Beyond. EC 2021: 369 - [c161]Santiago R. Balseiro, Vahab S. Mirrokni, Renato Paes Leme, Song Zuo:
Non-Excludable Dynamic Mechanism Design. SODA 2021: 1357-1373 - [c160]Yuan Deng, Jieming Mao, Vahab S. Mirrokni, Song Zuo:
Towards Efficient Auctions in an Auto-bidding World. WWW 2021: 3965-3973 - [i76]Quanquan Gu, Amin Karbasi, Khashayar Khosravi, Vahab S. Mirrokni, Dongruo Zhou:
Batched Neural Bandits. CoRR abs/2102.13028 (2021) - [i75]Yuan Deng, Jieming Mao, Vahab S. Mirrokni, Song Zuo:
Towards Efficient Auctions in an Auto-bidding World. CoRR abs/2103.13356 (2021) - [i74]Amin Karbasi, Vahab S. Mirrokni, Mohammad Shadravan:
Parallelizing Thompson Sampling. CoRR abs/2106.01420 (2021) - [i73]Alessandro Epasto, Mohammad Mahdian, Vahab S. Mirrokni, Peilin Zhong:
Massively Parallel and Dynamic Algorithms for Minimum Size Clustering. CoRR abs/2106.02685 (2021) - [i72]Laxman Dhulipala, David Eisenstat, Jakub Lacki, Vahab S. Mirrokni, Jessica Shi:
Hierarchical Agglomerative Graph Clustering in Nearly-Linear Time. CoRR abs/2106.05610 (2021) - [i71]Hossein Esfandiari, Vahab S. Mirrokni, Shyam Narayanan:
Almost Tight Approximation Algorithms for Explainable Clustering. CoRR abs/2107.00774 (2021) - [i70]Lin Chen, Hossein Esfandiari, Gang Fu, Vahab S. Mirrokni, Qian Yu:
Feature Cross Search via Submodular Optimization. CoRR abs/2107.02139 (2021) - [i69]Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab S. Mirrokni:
Bidding and Pricing in Budget and ROI Constrained Markets. CoRR abs/2107.07725 (2021) - [i68]Jessica Shi, Laxman Dhulipala, David Eisenstat, Jakub Lacki, Vahab S. Mirrokni:
Scalable Community Detection via Parallel Correlation Clustering. CoRR abs/2108.01731 (2021) - [i67]Hossein Esfandiari, Vahab S. Mirrokni, Umar Syed, Sergei Vassilvitskii:
Label differential privacy via clustering. CoRR abs/2110.02159 (2021) - [i66]Hossein Esfandiari, Vahab S. Mirrokni, Shyam Narayanan:
Tight and Robust Private Mean Estimation with Few Users. CoRR abs/2110.11876 (2021) - [i65]Alessandro Epasto, Mohammad Mahdian, Vahab S. Mirrokni, Peilin Zhong:
Improved Sliding Window Algorithms for Clustering and Coverage via Bucketing-Based Sketches. CoRR abs/2110.15533 (2021) - [i64]Santiago R. Balseiro, Yuan Deng, Jieming Mao, Vahab S. Mirrokni, Song Zuo:
Robust Auction Design in the Auto-bidding World. CoRR abs/2111.02468 (2021) - [i63]MohammadHossein Bateni, Hossein Esfandiari, Rajesh Jayaram, Vahab S. Mirrokni:
Optimal Fully Dynamic k-Centers Clustering. CoRR abs/2112.07050 (2021) - 2020
- [j53]Gagan Goel, Vahab S. Mirrokni, Renato Paes Leme:
Clinching auctions with online supply. Games Econ. Behav. 123: 342-358 (2020) - [j52]Soheil Behnezhad, Laxman Dhulipala, Hossein Esfandiari, Jakub Lacki, Vahab S. Mirrokni, Warren Schudy:
Parallel Graph Algorithms in Constant Adaptive Rounds: Theory meets Practice. Proc. VLDB Endow. 13(13): 3588-3602 (2020) - [c159]Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab S. Mirrokni:
Accelerating Gradient Boosting Machines. AISTATS 2020: 516-526 - [c158]Santiago R. Balseiro, Haihao Lu, Vahab S. Mirrokni:
Dual Mirror Descent for Online Allocation Problems. ICML 2020: 613-628 - [c157]Yuan Deng, Sébastien Lahaie, Vahab S. Mirrokni:
Robust Pricing in Dynamic Mechanism Design. ICML 2020: 2494-2503 - [c156]