Mirrokni, Vahab

102 publications

ICLR 2025 Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models Zeman Li, Xinwei Zhang, Peilin Zhong, Yuan Deng, Meisam Razaviyayn, Vahab Mirrokni

ICML 2025 Best of Both Worlds: Advantages of Hybrid Graph Sequence Models Ali Behrouz, Ali Parviz, Mahdi Karami, Clayton Sanford, Bryan Perozzi, Vahab Mirrokni

ICLRW 2025 ClusterGen: Token Generation in Sublinear Time and Memory with Clustering KV Cache Amir Zandieh, Insu Han, Amin Karbasi, Vahab Mirrokni

ICML 2025 DeepCrossAttention: Supercharging Transformer Residual Connections Mike Heddes, Adel Javanmard, Kyriakos Axiotis, Gang Fu, Mohammadhossein Bateni, Vahab Mirrokni

ICLR 2025 DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction Xinwei Zhang, Zhiqi Bu, Borja Balle, Mingyi Hong, Meisam Razaviyayn, Vahab Mirrokni

NeurIPS 2025 Efficient Data Selection at Scale via Influence Distillation Mahdi Nikdan, Vincent Cohen-Addad, Dan Alistarh, Vahab Mirrokni

ICML 2025 Improving the Variance of Differentially Private Randomized Experiments Through Clustering Adel Javanmard, Vahab Mirrokni, Jean Pouget-Abadie

ICLRW 2025 MS-SSM: A Multi-Scale State Space Model for Enhanced Sequence Modeling Mahdi Karami, Ali Behrouz, Peilin Zhong, Razvan Pascanu, Vahab Mirrokni

ICML 2025 Maximum Coverage in Turnstile Streams with Applications to Fingerprinting Measures Alina Ene, Alessandro Epasto, Vahab Mirrokni, Hoai-An Nguyen, Huy Nguyen, David Woodruff, Peilin Zhong

IJCAI 2025 Mechanism Design for Large Language Models (Extended Abstract) Paul Dütting, Vahab Mirrokni, Renato Paes Leme, Haifeng Xu, Song Zuo

NeurIPS 2025 Nested Learning: The Illusion of Deep Learning Architectures Ali Behrouz, Meisam Razaviyayn, Peilin Zhong, Vahab Mirrokni

NeurIPS 2025 PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts Zeman Li, Yuan Deng, Peilin Zhong, Meisam Razaviyayn, Vahab Mirrokni

ICLRW 2025 PiKE: Adaptive Data Mixing for Multi-Task Learning Under Low Gradient Conflicts Zeman Li, Yuan Deng, Peilin Zhong, Meisam Razaviyayn, Vahab Mirrokni

ICML 2025 Procurement Auctions via Approximately Optimal Submodular Optimization Yuan Deng, Amin Karbasi, Vahab Mirrokni, Renato Paes Leme, Grigoris Velegkas, Song Zuo

ICML 2025 Retraining with Predicted Hard Labels Provably Increases Model Accuracy Rudrajit Das, Inderjit S Dhillon, Alessandro Epasto, Adel Javanmard, Jieming Mao, Vahab Mirrokni, Sujay Sanghavi, Peilin Zhong

NeurIPS 2025 Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing Adel Javanmard, Rudrajit Das, Alessandro Epasto, Vahab Mirrokni

ICML 2025 Synthetic Text Generation for Training Large Language Models via Gradient Matching Dang Nguyen, Zeman Li, Mohammadhossein Bateni, Vahab Mirrokni, Meisam Razaviyayn, Baharan Mirzasoleiman

NeurIPS 2025 Titans: Learning to Memorize at Test Time Ali Behrouz, Peilin Zhong, Vahab Mirrokni

ICML 2024 A Field Guide for Pacing Budget and ROS Constraints Santiago R. Balseiro, Kshipra Bhawalkar, Zhe Feng, Haihao Lu, Vahab Mirrokni, Balasubramanian Sivan, Di Wang

NeurIPSW 2024 Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models Zeman Li, Xinwei Zhang, Peilin Zhong, Yuan Deng, Meisam Razaviyayn, Vahab Mirrokni

NeurIPSW 2024 Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models Zeman Li, Xinwei Zhang, Peilin Zhong, Yuan Deng, Meisam Razaviyayn, Vahab Mirrokni

NeurIPS 2024 Autobidder's Dilemma: Why More Sophisticated Autobidders Lead to Worse Auction Efficiency Yuan Deng, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo

ICML 2024 Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond Kyriakos Axiotis, Vincent Cohen-Addad, Monika Henzinger, Sammy Jerome, Vahab Mirrokni, David Saulpic, David Woodruff, Michael Wunder

NeurIPSW 2024 DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction Xinwei Zhang, Zhiqi Bu, Borja Balle, Mingyi Hong, Meisam Razaviyayn, Vahab Mirrokni

NeurIPS 2024 Efficiency of the First-Price Auction in the Autobidding World Yuan Deng, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo

ICML 2024 High-Dimensional Geometric Streaming for Nearly Low Rank Data Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni, David Woodruff, Peilin Zhong

ICLR 2024 HyperAttention: Long-Context Attention in Near-Linear Time Insu Han, Rajesh Jayaram, Amin Karbasi, Vahab Mirrokni, David Woodruff, Amir Zandieh

ICMLW 2024 LLMs at the Bargaining Table Yuan Deng, Vahab Mirrokni, Renato Paes Leme, Hanrui Zhang, Song Zuo

ICLR 2024 Learning from Aggregate Responses: Instance Level Versus Bag Level Loss Functions Adel Javanmard, Lin Chen, Vahab Mirrokni, Ashwinkumar Badanidiyuru, Gang Fu

NeurIPS 2024 MUVERA: Multi-Vector Retrieval via Fixed Dimensional Encoding Laxman Dhulipala, Majid Hadian, Rajesh Jayaram, Jason Lee, Vahab Mirrokni

ICMLW 2024 Mechanism Design for Large Language Models Paul Duetting, Vahab Mirrokni, Renato Paes Leme, Haifeng Xu, Song Zuo

COLT 2024 Optimistic Rates for Learning from Label Proportions Gene Li, Lin Chen, Adel Javanmard, Vahab Mirrokni

ICML 2024 Perturb-and-Project: Differentially Private Similarities and Marginals Vincent Cohen-Addad, Tommaso D’Orsi, Alessandro Epasto, Vahab Mirrokni, Peilin Zhong

ICML 2024 PolySketchFormer: Fast Transformers via Sketching Polynomial Kernels Praneeth Kacham, Vahab Mirrokni, Peilin Zhong

ICML 2024 PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses Adel Javanmard, Matthew Fahrbach, Vahab Mirrokni

NeurIPS 2024 SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization Taisuke Yasuda, Kyriakos Axiotis, Gang Fu, MohammadHossein Bateni, Vahab Mirrokni

NeurIPS 2024 Understanding Transformer Reasoning Capabilities via Graph Algorithms Clayton Sanford, Bahare Fatemi, Ethan Hall, Anton Tsitsulin, Mehran Kazemi, Jonathan Halcrow, Bryan Perozzi, Vahab Mirrokni

NeurIPS 2023 $k$-Means Clustering with Distance-Based Privacy Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong

NeurIPSW 2023 A New Framework for Measuring Re-Identification Risk Cj Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andres Munoz Medina, Vahab Mirrokni, Gabriel Nunes, Sergei Vassilvitskii, Peilin Zhong

NeurIPS 2023 Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization Adel Javanmard, Vahab Mirrokni

ICML 2023 Approximately Optimal Core Shapes for Tensor Decompositions Mehrdad Ghadiri, Matthew Fahrbach, Gang Fu, Vahab Mirrokni

ICML 2023 Differentially Private Hierarchical Clustering with Provable Approximation Guarantees Jacob Imola, Alessandro Epasto, Mohammad Mahdian, Vincent Cohen-Addad, Vahab Mirrokni

ICMLW 2023 K-Means Clustering with Distance-Based Privacy Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong

ICML 2023 Learning Rate Schedules in the Presence of Distribution Shift Matthew Fahrbach, Adel Javanmard, Vahab Mirrokni, Pratik Worah

ICML 2023 Multi-Channel Autobidding with Budget and ROI Constraints Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni

AISTATS 2023 Pricing Against a Budget and ROI Constrained Buyer Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni

ICLR 2023 Replicable Bandits Hossein Esfandiari, Alkis Kalavasis, Amin Karbasi, Andreas Krause, Vahab Mirrokni, Grigoris Velegkas

NeurIPS 2023 Replicable Clustering Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni, Grigoris Velegkas, Felix Zhou

ICML 2023 Robust Budget Pacing with a Single Sample Santiago R. Balseiro, Rachitesh Kumar, Vahab Mirrokni, Balasubramanian Sivan, Di Wang

JMLR 2023 Robust Load Balancing with Machine Learned Advice Sara Ahmadian, Hossein Esfandiari, Vahab Mirrokni, Binghui Peng

ICML 2023 Robust and Private Stochastic Linear Bandits Vasileios Charisopoulos, Hossein Esfandiari, Vahab Mirrokni

ICLR 2023 Sequential Attention for Feature Selection Taisuke Yasuda, Mohammadhossein Bateni, Lin Chen, Matthew Fahrbach, Gang Fu, Vahab Mirrokni

ICMLW 2023 Sequential Attention for Feature Selection Taisuke Yasuda, Mohammadhossein Bateni, Lin Chen, Matthew Fahrbach, Gang Fu, Vahab Mirrokni

TMLR 2023 Tackling Provably Hard Representative Selection via Graph Neural Networks Mehran Kazemi, Anton Tsitsulin, Hossein Esfandiari, Mohammadhossein Bateni, Deepak Ramachandran, Bryan Perozzi, Vahab Mirrokni

ICMLW 2023 Tackling Provably Hard Representative Selection viaGraph Neural Networks Mehran Kazemi, Anton Tsitsulin, Hossein Esfandiari, Mohammadhossein Bateni, Deepak Ramachandran, Bryan Perozzi, Vahab Mirrokni

AISTATS 2022 Label Differential Privacy via Clustering Hossein Esfandiari, Vahab Mirrokni, Umar Syed, Sergei Vassilvitskii

NeurIPS 2022 Anonymous Bandits for Multi-User Systems Hossein Esfandiari, Vahab Mirrokni, Jon Schneider

NeurIPS 2022 Cluster Randomized Designs for One-Sided Bipartite Experiments Jennifer Brennan, Vahab Mirrokni, Jean Pouget-Abadie

NeurIPS 2022 Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong

NeurIPSW 2022 Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong

NeurIPS 2022 Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic Depth Laxman Dhulipala, David Eisenstat, Jakub Lacki, Vahab Mirrokni, Jessica Shi

COLT 2022 Hierarchical Clustering in Graph Streams: Single-Pass Algorithms and Space Lower Bounds Sepehr Assadi, Vaggos Chatziafratis, Jakub Łącki, Vahab Mirrokni, Chen Wang

ALT 2022 Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab Mirrokni

ICML 2022 Massively Parallel $k$-Means Clustering for Perturbation Resilient Instances Vincent Cohen-Addad, Vahab Mirrokni, Peilin Zhong

NeurIPS 2022 Near-Optimal Private and Scalable $k$-Clustering Vincent Cohen-Addad, Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong

NeurIPS 2022 Posted Pricing and Dynamic Prior-Independent Mechanisms with Value Maximizers Yuan Deng, Vahab Mirrokni, Hanrui Zhang

NeurIPS 2022 Stars: Tera-Scale Graph Building for Clustering and Learning Cj Carey, Jonathan Halcrow, Rajesh Jayaram, Vahab Mirrokni, Warren Schudy, Peilin Zhong

ICML 2022 Tight and Robust Private Mean Estimation with Few Users Shyam Narayanan, Vahab Mirrokni, Hossein Esfandiari

COLT 2021 Adaptivity in Adaptive Submodularity Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni

ICML 2021 Hierarchical Agglomerative Graph Clustering in Nearly-Linear Time Laxman Dhulipala, David Eisenstat, Jakub Łącki, Vahab Mirrokni, Jessica Shi

NeurIPS 2021 Parallelizing Thompson Sampling Amin Karbasi, Vahab Mirrokni, Mohammad Shadravan

ICML 2021 Regularized Online Allocation Problems: Fairness and Beyond Santiago Balseiro, Haihao Lu, Vahab Mirrokni

ICML 2021 Revenue-Incentive Tradeoffs in Dynamic Reserve Pricing Yuan Deng, Sebastien Lahaie, Vahab Mirrokni, Song Zuo

NeurIPS 2021 Robust Auction Design in the Auto-Bidding World Santiago Balseiro, Yuan Deng, Jieming Mao, Vahab Mirrokni, Song Zuo

NeurIPS 2021 Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls Nick Doudchenko, Khashayar Khosravi, Jean Pouget-Abadie, Sébastien Lahaie, Miles Lubin, Vahab Mirrokni, Jann Spiess, Guido Imbens

AISTATS 2020 Accelerating Gradient Boosting Machines Haihao Lu, Sai Praneeth Karimireddy, Natalia Ponomareva, Vahab Mirrokni

ICML 2020 Bandits with Adversarial Scaling Thodoris Lykouris, Vahab Mirrokni, Renato Paes Leme

NeurIPS 2020 Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming Joey Huchette, Haihao Lu, Hossein Esfandiari, Vahab Mirrokni

ICML 2020 Dual Mirror Descent for Online Allocation Problems Santiago Balseiro, Haihao Lu, Vahab Mirrokni

NeurIPS 2020 Optimal Approximation - Smoothness Tradeoffs for Soft-Max Functions Alessandro Epasto, Mohammad Mahdian, Vahab Mirrokni, Emmanouil Zampetakis

ICML 2020 Robust Pricing in Dynamic Mechanism Design Yuan Deng, Sebastien Lahaie, Vahab Mirrokni

NeurIPS 2020 Smoothly Bounding User Contributions in Differential Privacy Alessandro Epasto, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, Lijie Ren

NeurIPS 2019 A Robust Non-Clairvoyant Dynamic Mechanism for Contextual Auctions Yuan Deng, Sébastien Lahaie, Vahab Mirrokni

ICML 2019 Categorical Feature Compression via Submodular Optimization Mohammadhossein Bateni, Lin Chen, Hossein Esfandiari, Thomas Fu, Vahab Mirrokni, Afshin Rostamizadeh

NeurIPS 2019 Contextual Bandits with Cross-Learning Santiago Balseiro, Negin Golrezaei, Mohammad Mahdian, Vahab Mirrokni, Jon Schneider

ICML 2019 Distributed Weighted Matching via Randomized Composable Coresets Sepehr Assadi, Mohammadhossein Bateni, Vahab Mirrokni

NeurIPS 2019 Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions Negin Golrezaei, Adel Javanmard, Vahab Mirrokni

NeurIPS 2019 Locality-Sensitive Hashing for F-Divergences: Mutual Information Loss and Beyond Lin Chen, Hossein Esfandiari, Gang Fu, Vahab Mirrokni

ICML 2019 Non-Monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity Matthew Fahrbach, Vahab Mirrokni, Morteza Zadimoghaddam

NeurIPS 2019 Variance Reduction in Bipartite Experiments Through Correlation Clustering Jean Pouget-Abadie, Kevin Aydin, Warren Schudy, Kay Brodersen, Vahab Mirrokni

ICML 2018 Accelerating Greedy Coordinate Descent Methods Haihao Lu, Robert Freund, Vahab Mirrokni

ICML 2018 Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions Shuaiwen Wang, Wenda Zhou, Haihao Lu, Arian Maleki, Vahab Mirrokni

ICML 2018 Parallel and Streaming Algorithms for K-Core Decomposition Hossein Esfandiari, Silvio Lattanzi, Vahab Mirrokni

ICML 2018 Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy Shipra Agrawal, Morteza Zadimoghaddam, Vahab Mirrokni

NeurIPS 2017 Affinity Clustering: Hierarchical Clustering at Scale Mohammadhossein Bateni, Soheil Behnezhad, Mahsa Derakhshan, MohammadTaghi Hajiaghayi, Raimondas Kiveris, Silvio Lattanzi, Vahab Mirrokni

NeurIPS 2017 Dynamic Revenue Sharing Santiago Balseiro, Max Lin, Vahab Mirrokni, Renato Leme, IIIS Song Zuo

ICML 2017 Tight Bounds for Approximate Carathéodory and Beyond Vahab Mirrokni, Renato Paes Leme, Adrian Vladu, Sam Chiu-wai Wong

NeurIPS 2016 Bi-Objective Online Matching and Submodular Allocations Hossein Esfandiari, Nitish Korula, Vahab Mirrokni

ICML 2016 Greedy Column Subset Selection: New Bounds and Distributed Algorithms Jason Altschuler, Aditya Bhaskara, Gang Fu, Vahab Mirrokni, Afshin Rostamizadeh, Morteza Zadimoghaddam

NeurIPS 2016 Linear Relaxations for Finding Diverse Elements in Metric Spaces Aditya Bhaskara, Mehrdad Ghadiri, Vahab Mirrokni, Ola Svensson

NeurIPS 2014 Distributed Balanced Clustering via Mapping Coresets Mohammadhossein Bateni, Aditya Bhaskara, Silvio Lattanzi, Vahab Mirrokni

ICML 2013 A Local Algorithm for Finding Well-Connected Clusters Zeyuan Allen Zhu, Silvio Lattanzi, Vahab Mirrokni