Vahab Mirrokni

157 papers A* 58A 2B 1Journal 93Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Accelerating Scientific Research with Gemini: Case Studies and Common Techniques. CoRR David P. Woodruff, Vincent Cohen-Addad, Lalit Jain, Jieming Mao, Song Zuo, MohammadHossein Bateni, Simina Brânzei, Michael P. Brenner, Lin Chen, Ying Feng, Lance Fortnow, Gang Fu, Ziyi Guan, Zahra Hadizadeh, Mohammad T. Hajiaghayi, Mahdi JafariRaviz, Adel Javanmard, Karthik C. S., Ken-ichi Kawarabayashi, Ravi Kumar, Silvio Lattanzi, Euiwoong Lee, Yi Li, Ioannis Panageas, Dimitris Paparas, Benjamin Przybocki, Bernardo Subercaseaux, Ola Svensson, Shayan Taherijam, Xuan Wu, Eylon Yogev, Morteza Zadimoghaddam, Samson Zhou, Yossi Matias, James Manyika, Vahab Mirrokni
2026	J	`jnl`	Aletheia tackles FirstProof autonomously. CoRR Tony Feng, Junehyuk Jung, Sang-hyun Kim, Carlo Pagano, Sergei Gukov, Cheng-Chiang Tsai, David P. Woodruff, Adel Javanmard, Aryan Mokhtari, Dawsen Hwang, Yuri Chervonyi, Jonathan N. Lee, Garrett Bingham, Trieu H. Trinh, Vahab Mirrokni, Quoc V. Le, Thang Luong
2026	J	`jnl`	ECO: Quantized Training without Full-Precision Master Weights. CoRR Mahdi Nikdan, Amir Zandieh, Dan Alistarh, Vahab Mirrokni
2026	J	`jnl`	Less is More: Convergence Benefits of Fewer Data Weight Updates over Longer Horizon. CoRR Rudrajit Das, Neel Patel, Meisam Razaviyayn, Vahab Mirrokni
2026	J	`jnl`	Memory Caching: RNNs with Growing Memory. CoRR Ali Behrouz, Zeman Li, Yuan Deng, Peilin Zhong, Meisam Razaviyayn, Vahab Mirrokni
2026	A*	`conf`	Position Auctions in AI-Generated Content. WWW Santiago R. Balseiro, Kshipra Bhawalkar, Yuan Deng, Zhe Feng, Jieming Mao, Aranyak Mehta, Vahab Mirrokni, Renato Paes Leme, Di Wang, Song Zuo
2026	J	`jnl`	Theoretical Perspectives on Data Quality and Synergistic Effects in Pre- and Post-Training Reasoning Models. CoRR Adel Javanmard, Baharan Mirzasoleiman, Vahab Mirrokni
2025	J	`jnl`	ATLAS: Learning to Optimally Memorize the Context at Test Time. CoRR Ali Behrouz, Zeman Li, Praneeth Kacham, Majid Daliri, Yuan Deng, Peilin Zhong, Meisam Razaviyayn, Vahab Mirrokni
2025	A*	`conf`	Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models. ICLR Zeman Li, Xinwei Zhang, Peilin Zhong, Yuan Deng, Meisam Razaviyayn, Vahab Mirrokni
2025	A*	`conf`	Best of Both Worlds: Advantages of Hybrid Graph Sequence Models. ICML Ali Behrouz, Ali Parviz, Mahdi Karami, Clayton Sanford, Bryan Perozzi, Vahab Mirrokni
2025	J	`jnl`	Data Selection for Fine-tuning Vision Language Models via Cross Modal Alignment Trajectories. CoRR Nilay Naharas, Dang Nguyen, Neslihan Bulut, MohammadHossein Bateni, Vahab Mirrokni, Baharan Mirzasoleiman
2025	A*	`conf`	DeepCrossAttention: Supercharging Transformer Residual Connections. ICML Mike Heddes, Adel Javanmard, Kyriakos Axiotis, Gang Fu, MohammadHossein Bateni, Vahab Mirrokni
2025	J	`jnl`	DeepCrossAttention: Supercharging Transformer Residual Connections. CoRR Mike Heddes, Adel Javanmard, Kyriakos Axiotis, Gang Fu, MohammadHossein Bateni, Vahab Mirrokni
2025	A*	`conf`	DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction. ICLR Xinwei Zhang, Zhiqi Bu, Borja Balle, Mingyi Hong, Meisam Razaviyayn, Vahab Mirrokni
2025	—	`conf`	Differentially Private Synthetic Data Release for Topics API Outputs. KDD (2) Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Andrés Muñoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, Peilin Zhong
2025	J	`jnl`	Differentially Private Synthetic Data Release for Topics API Outputs. CoRR Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Andrés Muñoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, Peilin Zhong
2025	A*	`conf`	Distributed Load Balancing with Workload-Dependent Service Rates. EC Wenxin Zhang, Santiago R. Balseiro, Robert Kleinberg, Vahab Mirrokni, Balasubramanian Sivan, Bartek Wydrowski
2025	J	`jnl`	Efficient Data Selection at Scale via Influence Distillation. CoRR Mahdi Nikdan, Vincent Cohen-Addad, Dan Alistarh, Vahab Mirrokni
2025	A*	`conf`	Improving the Variance of Differentially Private Randomized Experiments through Clustering. ICML Adel Javanmard, Vahab Mirrokni, Jean Pouget-Abadie
2025	J	`jnl`	It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization. CoRR Ali Behrouz, Meisam Razaviyayn, Peilin Zhong, Vahab Mirrokni
2025	J	`jnl`	Lattice: Learning to Efficiently Compress the Memory. CoRR Mahdi Karami, Vahab Mirrokni
2025	J	`jnl`	MS-SSM: A Multi-Scale State Space Model for Efficient Sequence Modeling. CoRR Mahdi Karami, Ali Behrouz, Peilin Zhong, Razvan Pascanu, Vahab Mirrokni
2025	A*	`conf`	Massively Parallel Minimum Spanning Tree in General Metric Spaces. SODA Amir Azarmehr, Soheil Behnezhad, Rajesh Jayaram, Jakub Lacki, Vahab Mirrokni, Peilin Zhong
2025	A*	`conf`	Maximum Coverage in Turnstile Streams with Applications to Fingerprinting Measures. ICML Alina Ene, Alessandro Epasto, Vahab Mirrokni, Hoai-An Nguyen, Huy L. Nguyen, David P. Woodruff, Peilin Zhong
2025	J	`jnl`	Maximum Coverage in Turnstile Streams with Applications to Fingerprinting Measures. CoRR Alina Ene, Alessandro Epasto, Vahab Mirrokni, Hoai-An Nguyen, Huy L. Nguyen, David P. Woodruff, Peilin Zhong
2025	A*	`conf`	Mechanism Design for Large Language Models (Extended Abstract). IJCAI Paul Dütting, Vahab Mirrokni, Renato Paes Leme, Haifeng Xu, Song Zuo
2025	J	`jnl`	Nested Learning: The Illusion of Deep Learning Architectures. CoRR Ali Behrouz, Meisam Razaviyayn, Peilin Zhong, Vahab Mirrokni
2025	J	`jnl`	PiKE: Adaptive Data Mixing for Multi-Task Learning Under Low Gradient Conflicts. CoRR Zeman Li, Yuan Deng, Peilin Zhong, Meisam Razaviyayn, Vahab Mirrokni
2025	J	`jnl`	PolarQuant: Quantizing KV Caches with Polar Transformation. CoRR Insu Han, Praneeth Kacham, Amin Karbasi, Vahab Mirrokni, Amir Zandieh
2025	J	`jnl`	Position Auctions in AI-Generated Content. CoRR Santiago R. Balseiro, Kshipra Bhawalkar, Yuan Deng, Zhe Feng, Jieming Mao, Aranyak Mehta, Vahab Mirrokni, Renato Paes Leme, Di Wang, Song Zuo
2025	J	`jnl`	Private Training & Data Generation by Clustering Embeddings. CoRR Felix Zhou, Samson Zhou, Vahab Mirrokni, Alessandro Epasto, Vincent Cohen-Addad
2025	A*	`conf`	Procurement Auctions via Approximately Optimal Submodular Optimization. ICML Yuan Deng, Amin Karbasi, Vahab Mirrokni, Renato Paes Leme, Grigoris Velegkas, Song Zuo
2025	J	`jnl`	Regularized Online Allocation Problems: Fairness and Beyond. Manuf. Serv. Oper. Manag. Santiago R. Balseiro, Haihao Lu, Vahab Mirrokni
2025	A*	`conf`	Retraining with Predicted Hard Labels Provably Increases Model Accuracy. ICML Rudrajit Das, Inderjit S. Dhillon, Alessandro Epasto, Adel Javanmard, Jieming Mao, Vahab Mirrokni, Sujay Sanghavi, Peilin Zhong
2025	J	`jnl`	Sampling and Loss Weights in Multi-Domain Training. CoRR Mahdi Salmani, Pratik Worah, Meisam Razaviyayn, Vahab Mirrokni
2025	J	`jnl`	Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing. CoRR Adel Javanmard, Rudrajit Das, Alessandro Epasto, Vahab Mirrokni
2025	A*	`conf`	Synthetic Text Generation for Training Large Language Models via Gradient Matching. ICML Dang Nguyen, Zeman Li, MohammadHossein Bateni, Vahab Mirrokni, Meisam Razaviyayn, Baharan Mirzasoleiman
2025	J	`jnl`	Synthetic Text Generation for Training Large Language Models via Gradient Matching. CoRR Dang Nguyen, Zeman Li, MohammadHossein Bateni, Vahab Mirrokni, Meisam Razaviyayn, Baharan Mirzasoleiman
2025	J	`jnl`	TNT: Improving Chunkwise Training for Test-Time Memorization. CoRR Zeman Li, Ali Behrouz, Yuan Deng, Peilin Zhong, Praneeth Kacham, Mahdi Karami, Meisam Razaviyayn, Vahab Mirrokni
2025	J	`jnl`	Titans: Learning to Memorize at Test Time. CoRR Ali Behrouz, Peilin Zhong, Vahab Mirrokni
2025	J	`jnl`	Trellis: Learning to Compress Key-Value Memory in Attention Models. CoRR Mahdi Karami, Ali Behrouz, Praneeth Kacham, Vahab Mirrokni
2025	J	`jnl`	TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate. CoRR Amir Zandieh, Majid Daliri, Majid Hadian, Vahab Mirrokni
2025	J	`jnl`	Understanding the Role of Training Data in Test-Time Scaling. CoRR Adel Javanmard, Baharan Mirzasoleiman, Vahab Mirrokni
2024	A*	`conf`	A Field Guide for Pacing Budget and ROS Constraints. ICML Santiago R. Balseiro, Kshipra Bhawalkar, Zhe Feng, Haihao Lu, Vahab Mirrokni, Balasubramanian Sivan, Di Wang
2024	J	`jnl`	Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models. CoRR Zeman Li, Xinwei Zhang, Peilin Zhong, Yuan Deng, Meisam Razaviyayn, Vahab Mirrokni
2024	J	`jnl`	Auto-Bidding and Auctions in Online Advertising: A Survey. SIGecom Exch. 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
2024	J	`jnl`	Auto-bidding and Auctions in Online Advertising: A Survey. CoRR 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
2024	A*	`conf`	Autobidder's Dilemma: Why More Sophisticated Autobidders Lead to Worse Auction Efficiency. NeurIPS Yuan Deng, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo
2024	J	`jnl`	Best of Both Worlds: Advantages of Hybrid Graph Sequence Models. CoRR Ali Behrouz, Ali Parviz, Mahdi Karami, Clayton Sanford, Bryan Perozzi, Vahab Mirrokni
2024	A*	`conf`	Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond. ICML Kyriakos Axiotis, Vincent Cohen-Addad, Monika Henzinger, Sammy Jerome, Vahab Mirrokni, David Saulpic, David P. Woodruff, Michael Wunder
2024	J	`jnl`	Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond. CoRR Kyriakos Axiotis, Vincent Cohen-Addad, Monika Henzinger, Sammy Jerome, Vahab Mirrokni, David Saulpic, David P. Woodruff, Michael Wunder
2024	J	`jnl`	DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction. CoRR Xinwei Zhang, Zhiqi Bu, Borja Balle, Mingyi Hong, Meisam Razaviyayn, Vahab Mirrokni
2024	A*	`conf`	Efficiency of the First-Price Auction in the Autobidding World. NeurIPS Yuan Deng, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo
2024	A*	`conf`	Efficiency of the Generalized Second-Price Auction for Value Maximizers. WWW Yuan Deng, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo
2024	A*	`conf`	High-Dimensional Geometric Streaming for Nearly Low Rank Data. ICML Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni, David P. Woodruff, Peilin Zhong
2024	J	`jnl`	High-Dimensional Geometric Streaming for Nearly Low Rank Data. CoRR Hossein Esfandiari, Vahab Mirrokni, Praneeth Kacham, David P. Woodruff, Peilin Zhong
2024	A*	`conf`	HyperAttention: Long-context Attention in Near-Linear Time. ICLR Insu Han, Rajesh Jayaram, Amin Karbasi, Vahab Mirrokni, David P. Woodruff, Amir Zandieh
2024	A*	`conf`	Individual Welfare Guarantees in the Autobidding World with Machine-learned Advice. WWW Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni
2024	A*	`conf`	Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions. ICLR Adel Javanmard, Lin Chen, Vahab Mirrokni, Ashwinkumar Badanidiyuru, Gang Fu
2024	J	`jnl`	Learning from Aggregate responses: Instance Level versus Bag Level Loss Functions. CoRR Adel Javanmard, Lin Chen, Vahab Mirrokni, Ashwinkumar Badanidiyuru, Gang Fu
2024	A*	`conf`	MUVERA: Multi-Vector Retrieval via Fixed Dimensional Encoding. NeurIPS Laxman Dhulipala, Majid Hadian, Rajesh Jayaram, Jason Lee, Vahab Mirrokni
2024	J	`jnl`	MUVERA: Multi-Vector Retrieval via Fixed Dimensional Encodings. CoRR Laxman Dhulipala, Majid Hadian, Rajesh Jayaram, Jason Lee, Vahab Mirrokni
2024	A*	`conf`	Massively Parallel Algorithms for High-Dimensional Euclidean Minimum Spanning Tree. SODA Rajesh Jayaram, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong
2024	J	`jnl`	Massively Parallel Minimum Spanning Tree in General Metric Spaces. CoRR Amir Azarmehr, Soheil Behnezhad, Rajesh Jayaram, Jakub Lacki, Vahab Mirrokni, Peilin Zhong
2024	A*	`conf`	Mechanism Design for Large Language Models. WWW Paul Dütting, Vahab Mirrokni, Renato Paes Leme, Haifeng Xu, Song Zuo
2024	A*	`conf`	Non-uniform Bid-scaling and Equilibria for Different Auctions: An Empirical Study. WWW Yuan Deng, Jieming Mao, Vahab Mirrokni, Yifeng Teng, Song Zuo
2024	A*	`conf`	Optimal Communication Bounds for Classic Functions in the Coordinator Model and Beyond. STOC Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni, David P. Woodruff, Peilin Zhong
2024	J	`jnl`	Optimal Communication for Classic Functions in the Coordinator Model and Beyond. CoRR Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni, David P. Woodruff, Peilin Zhong
2024	A*	`conf`	Optimal Mechanisms for a Value Maximizer: The Futility of Screening Targets. EC Santiago R. Balseiro, Yuan Deng, Jieming Mao, Vahab Mirrokni, Song Zuo
2024	J	`jnl`	Optimal and Stable Distributed Bipartite Load Balancing. CoRR Wenxin Zhang, Santiago R. Balseiro, Robert Kleinberg, Vahab Mirrokni, Balasubramanian Sivan, Bartek Wydrowski
2024	A*	`conf`	Optimistic Rates for Learning from Label Proportions. COLT Gene Li, Lin Chen, Adel Javanmard, Vahab Mirrokni
2024	J	`jnl`	Optimistic Rates for Learning from Label Proportions. CoRR Gene Li, Lin Chen, Adel Javanmard, Vahab Mirrokni
2024	A*	`conf`	Perturb-and-Project: Differentially Private Similarities and Marginals. ICML Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto, Vahab Mirrokni, Peilin Zhong
2024	J	`jnl`	Perturb-and-Project: Differentially Private Similarities and Marginals. CoRR Vincent Cohen-Addad, Tommaso d'Orsi, Alessandro Epasto, Vahab Mirrokni, Peilin Zhong
2024	A*	`conf`	PolySketchFormer: Fast Transformers via Sketching Polynomial Kernels. ICML Praneeth Kacham, Vahab Mirrokni, Peilin Zhong
2024	A*	`conf`	PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses. ICML Adel Javanmard, Matthew Fahrbach, Vahab Mirrokni
2024	J	`jnl`	PriorBoost: An Adaptive Algorithm for Learning from Aggregate Responses. CoRR Adel Javanmard, Matthew Fahrbach, Vahab Mirrokni
2024	J	`jnl`	Procurement Auctions via Approximately Optimal Submodular Optimization. CoRR Yuan Deng, Amin Karbasi, Vahab Mirrokni, Renato Paes Leme, Grigoris Velegkas, Song Zuo
2024	J	`jnl`	Retraining with Predicted Hard Labels Provably Increases Model Accuracy. CoRR Rudrajit Das, Inderjit S. Dhillon, Alessandro Epasto, Adel Javanmard, Jieming Mao, Vahab Mirrokni, Sujay Sanghavi, Peilin Zhong
2024	A*	`conf`	SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization. NeurIPS Taisuke Yasuda, Kyriakos Axiotis, Gang Fu, Mohammad Hossein Bateni, Vahab Mirrokni
2024	J	`jnl`	SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization. CoRR Taisuke Yasuda, Kyriakos Axiotis, Gang Fu, MohammadHossein Bateni, Vahab Mirrokni
2024	—	`conf`	Smooth Anonymity for Sparse Graphs. WWW (Companion Volume) Alessandro Epasto, Hossein Esfandiari, Vahab Mirrokni, Andrés Muñoz Medina
2024	J	`jnl`	SubGen: Token Generation in Sublinear Time and Memory. CoRR Amir Zandieh, Insu Han, Vahab Mirrokni, Amin Karbasi
2024	—	`conf`	TeraHAC: Hierarchical Agglomerative Clustering of Trillion-Edge Graphs (Abstract). HOPC@SPAA Laxman Dhulipala, Jakub Lacki, Jason Lee, Vahab Mirrokni
2024	J	`jnl`	The ParClusterers Benchmark Suite (PCBS): A Fine-Grained Analysis of Scalable Graph Clustering. CoRR Shangdi Yu, Jessica Shi, Jamison Meindl, David Eisenstat, Xiaoen Ju, Sasan Tavakkol, Laxman Dhulipala, Jakub Lacki, Vahab Mirrokni, Julian Shun
2024	J	`jnl`	The ParClusterers Benchmark Suite (PCBS): A Fine-Grained Analysis of Scalable Graph Clustering. Proc. VLDB Endow. Shangdi Yu, Jessica Shi, Jamison Meindl, David Eisenstat, Xiaoen Ju, Sasan Tavakkol, Laxman Dhulipala, Jakub Lacki, Vahab Mirrokni, Julian Shun
2024	A*	`conf`	Understanding Transformer Reasoning Capabilities via Graph Algorithms. NeurIPS Clayton Sanford, Bahare Fatemi, Ethan Hall, Anton Tsitsulin, Mehran Kazemi, Jonathan Halcrow, Bryan Perozzi, Vahab Mirrokni
2024	J	`jnl`	Understanding Transformer Reasoning Capabilities via Graph Algorithms. CoRR Clayton Sanford, Bahare Fatemi, Ethan Hall, Anton Tsitsulin, Seyed Mehran Kazemi, Jonathan Halcrow, Bryan Perozzi, Vahab Mirrokni
2023	A*	`conf`	Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization. NeurIPS Adel Javanmard, Vahab Mirrokni
2023	J	`jnl`	Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization. CoRR Adel Javanmard, Vahab Mirrokni
2023	A*	`conf`	Approximately Optimal Core Shapes for Tensor Decompositions. ICML Mehrdad Ghadiri, Matthew Fahrbach, Gang Fu, Vahab Mirrokni
2023	J	`jnl`	Approximately Optimal Core Shapes for Tensor Decompositions. CoRR Mehrdad Ghadiri, Matthew Fahrbach, Gang Fu, Vahab Mirrokni
2023	A*	`conf`	Autobidding Auctions in the Presence of User Costs. WWW Yuan Deng, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo
2023	J	`jnl`	Autobidding Auctions in the Presence of User Costs. CoRR Yuan Deng, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo
2023	B	`conf`	Brief Announcement: Streaming Balanced Clustering. SPAA Hossein Esfandiari, Vahab Mirrokni, Peilin Zhong
2023	A*	`conf`	Causal Estimation of User Learning in Personalized Systems. EC Evan Munro, David Jones, Jennifer Brennan, Roland Nelet, Vahab Mirrokni, Jean Pouget-Abadie
2023	J	`jnl`	Causal Estimation of User Learning in Personalized Systems. CoRR Evan Munro, David Jones, Jennifer Brennan, Roland Nelet, Vahab Mirrokni, Jean Pouget-Abadie
2023	J	`jnl`	Causal Inference with Differentially Private (Clustered) Outcomes. CoRR Adel Javanmard, Vahab Mirrokni, Jean Pouget-Abadie
2023	J	`jnl`	Contextual Bandits with Cross-Learning. Math. Oper. Res. Santiago R. Balseiro, Negin Golrezaei, Mohammad Mahdian, Vahab Mirrokni, Jon Schneider
2023	A	`conf`	Differentially Private Continual Releases of Streaming Frequency Moment Estimations. ITCS Alessandro Epasto, Jieming Mao, Andres Muñoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, Peilin Zhong
2023	J	`jnl`	Differentially Private Continual Releases of Streaming Frequency Moment Estimations. CoRR Alessandro Epasto, Jieming Mao, Andres Muñoz Medina, Vahab Mirrokni, Sergei Vassilvitskii, Peilin Zhong
2023	A*	`conf`	Differentially Private Hierarchical Clustering with Provable Approximation Guarantees. ICML Jacob Imola, Alessandro Epasto, Mohammad Mahdian, Vincent Cohen-Addad, Vahab Mirrokni
2023	J	`jnl`	Differentially-Private Hierarchical Clustering with Provable Approximation Guarantees. CoRR Jacob Imola, Alessandro Epasto, Mohammad Mahdian, Vincent Cohen-Addad, Vahab Mirrokni
2023	J	`jnl`	Efficiency of the Generalized Second-Price Auction for Value Maximizers. CoRR Yuan Deng, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo
2023	J	`jnl`	HyperAttention: Long-context Attention in Near-Linear Time. CoRR Insu Han, Rajesh Jayaram, Amin Karbasi, Vahab Mirrokni, David P. Woodruff, Amir Zandieh
2023	J	`jnl`	Joint Feedback Loop for Spend and Return-On-Spend Constraints. CoRR Santiago R. Balseiro, Kshipra Bhawalkar, Zhe Feng, Haihao Lu, Vahab Mirrokni, Balasubramanian Sivan, Di Wang
2023	A*	`conf`	Learning Rate Schedules in the Presence of Distribution Shift. ICML Matthew Fahrbach, Adel Javanmard, Vahab Mirrokni, Pratik Worah
2023	J	`jnl`	Learning Rate Schedules in the Presence of Distribution Shift. CoRR Matthew Fahrbach, Adel Javanmard, Vahab Mirrokni, Pratik Worah
2023	J	`jnl`	Learning across Data Owners with Joint Differential Privacy. CoRR Yangsibo Huang, Haotian Jiang, Daogao Liu, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni
2023	J	`jnl`	Learning from Aggregated Data: Curated Bags versus Random Bags. CoRR Lin Chen, Gang Fu, Amin Karbasi, Vahab Mirrokni
2023	J	`jnl`	Massively Parallel Algorithms for High-Dimensional Euclidean Minimum Spanning Tree. CoRR Rajesh Jayaram, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong
2023	J	`jnl`	Measuring Re-identification Risk. CoRR CJ Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andrés Muñoz Medina, Vahab Mirrokni, Gabriel Henrique Nunes, Sergei Vassilvitskii, Peilin Zhong
2023	J	`jnl`	Measuring Re-identification Risk. Proc. ACM Manag. Data CJ Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andres Muñoz Medina, Vahab Mirrokni, Gabriel Henrique Nunes, Sergei Vassilvitskii, Peilin Zhong
2023	J	`jnl`	Mechanism Design for Large Language Models. CoRR Paul Duetting, Vahab Mirrokni, Renato Paes Leme, Haifeng Xu, Song Zuo
2023	A*	`conf`	Multi-channel Autobidding with Budget and ROI Constraints. ICML Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni
2023	J	`jnl`	Multi-channel Autobidding with Budget and ROI Constraints. CoRR Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni
2023	J	`jnl`	Non-uniform Bid-scaling and Equilibria for Different Auctions: An Empirical Study. CoRR Yuan Deng, Jieming Mao, Vahab Mirrokni, Yifeng Teng, Song Zuo
2023	A*	`conf`	Optimal Fully Dynamic SODA MohammadHossein Bateni, Hossein Esfandiari, Hendrik Fichtenberger, Monika Henzinger, Rajesh Jayaram, Vahab Mirrokni, Andreas Wiese
2023	J	`jnl`	Optimal Fully Dynamic k-Center Clustering for Adaptive and Oblivious Adversaries. CoRR MohammadHossein Bateni, Hossein Esfandiari, Hendrik Fichtenberger, Monika Henzinger, Rajesh Jayaram, Vahab Mirrokni, Andreas Wiese
2023	J	`jnl`	PolySketchFormer: Fast Transformers via Sketches for Polynomial Kernels. CoRR Praneeth Kacham, Vahab Mirrokni, Peilin Zhong
2023	A	`conf`	Pricing against a Budget and ROI Constrained Buyer. AISTATS Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni
2023	A*	`conf`	Replicable Bandits. ICLR Hossein Esfandiari, Alkis Kalavasis, Amin Karbasi, Andreas Krause, Vahab Mirrokni, Grigoris Velegkas
2023	A*	`conf`	Replicable Clustering. NeurIPS Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni, Grigoris Velegkas, Felix Zhou
2023	J	`jnl`	Replicable Clustering. CoRR Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni, Grigoris Velegkas, Felix Zhou
2023	A*	`conf`	Robust Budget Pacing with a Single Sample. ICML Santiago R. Balseiro, Rachitesh Kumar, Vahab Mirrokni, Balasubramanian Sivan, Di Wang
2023	J	`jnl`	Robust Budget Pacing with a Single Sample. CoRR Santiago R. Balseiro, Rachitesh Kumar, Vahab Mirrokni, Balasubramanian Sivan, Di Wang
2023	J	`jnl`	Robust Load Balancing with Machine Learned Advice. J. Mach. Learn. Res. Sara Ahmadian, Hossein Esfandiari, Vahab Mirrokni, Binghui Peng
2023	J	`jnl`	Robust and differentially private stochastic linear bandits. CoRR Vasileios Charisopoulos, Hossein Esfandiari, Vahab Mirrokni
2023	A*	`conf`	Robust and private stochastic linear bandits. ICML Vasileios Charisopoulos, Hossein Esfandiari, Vahab Mirrokni
2023	A*	`conf`	Sequential Attention for Feature Selection. ICLR Taisuke Yasuda, Mohammad Hossein Bateni, Lin Chen, Matthew Fahrbach, Gang Fu, Vahab Mirrokni
2023	J	`jnl`	Tackling Provably Hard Representative Selection via Graph Neural Networks. Trans. Mach. Learn. Res. Mehran Kazemi, Anton Tsitsulin, Hossein Esfandiari, MohammadHossein Bateni, Deepak Ramachandran, Bryan Perozzi, Vahab Mirrokni
2023	J	`jnl`	TeraHAC: Hierarchical Agglomerative Clustering of Trillion-Edge Graphs. CoRR Laxman Dhulipala, Jason Lee, Jakub Lacki, Vahab Mirrokni
2023	J	`jnl`	TeraHAC: Hierarchical Agglomerative Clustering of Trillion-Edge Graphs. Proc. ACM Manag. Data Laxman Dhulipala, Jakub Lacki, Jason Lee, Vahab Mirrokni
2023	J	`jnl`	The Best of Many Worlds: Dual Mirror Descent for Online Allocation Problems. Oper. Res. Santiago R. Balseiro, Haihao Lu, Vahab Mirrokni
2023	J	`jnl`	The landscape of the proximal point method for nonconvex-nonconcave minimax optimization. Math. Program. Benjamin Grimmer, Haihao Lu, Pratik Worah, Vahab Mirrokni
2023	A*	`conf`	k-Means Clustering with Distance-Based Privacy. NeurIPS Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong
2022	A*	`conf`	Anonymous Bandits for Multi-User Systems. NeurIPS Hossein Esfandiari, Vahab Mirrokni, Jon Schneider
2022	J	`jnl`	Anonymous Bandits for Multi-User Systems. CoRR Hossein Esfandiari, Vahab Mirrokni, Jon Schneider
2022	A*	`conf`	Cluster Randomized Designs for One-Sided Bipartite Experiments. NeurIPS Jennifer Brennan, Vahab Mirrokni, Jean Pouget-Abadie
2022	J	`jnl`	Constant Approximation for Normalized Modularity and Associations Clustering. CoRR Jakub Lacki, Vahab Mirrokni, Christian Sohler
2022	A*	`conf`	Design and Analysis of Bipartite Experiments Under a Linear Exposure-response Model. EC Christopher Harshaw, Fredrik Sävje, David Eisenstat, Vahab Mirrokni, Jean Pouget-Abadie
2022	A*	`conf`	Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank. NeurIPS Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong
2022	J	`jnl`	Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank. CoRR Alessandro Epasto, Vahab Mirrokni, Bryan Perozzi, Anton Tsitsulin, Peilin Zhong
2022	J	`jnl`	Dynamic Double Auctions: Toward First Best. Oper. Res. Santiago R. Balseiro, Vahab Mirrokni, Renato Paes Leme, Song Zuo
2022	J	`jnl`	Efficiency of the First-Price Auction in the Autobidding World. CoRR Yuan Deng, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo
2022	J	`jnl`	Fairness in the Autobidding World with Machine-learned Advice. CoRR Yuan Deng, Negin Golrezaei, Patrick Jaillet, Jason Cheuk Nam Liang, Vahab Mirrokni
2022	A*	`conf`	Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic Depth. NeurIPS Laxman Dhulipala, David Eisenstat, Jakub Lacki, Vahab Mirrokni, Jessica Shi
2022	J	`jnl`	Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic Depth. CoRR Laxman Dhulipala, David Eisenstat, Jakub Lacki, Vahab Mirrokni, Jessica Shi
2022	A*	`conf`	Hierarchical Clustering in Graph Streams: Single-Pass Algorithms and Space Lower Bounds. COLT Sepehr Assadi, Vaggos Chatziafratis, Jakub Lacki, Vahab Mirrokni, Chen Wang
2022	A*	`conf`	Near-Optimal Private and Scalable $k$-Clustering. NeurIPS Vincent Cohen-Addad, Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong
2022	A*	`conf`	Posted Pricing and Dynamic Prior-independent Mechanisms with Value Maximizers. NeurIPS Yuan Deng, Vahab Mirrokni, Hanrui Zhang
2022	J	`jnl`	Product Ranking on Online Platforms. Manag. Sci. Mahsa Derakhshan, Negin Golrezaei, Vahideh H. Manshadi, Vahab Mirrokni
2022	J	`jnl`	Reproducible Bandits. CoRR Hossein Esfandiari, Alkis Kalavasis, Amin Karbasi, Andreas Krause, Vahab Mirrokni, Grigoris Velegkas
2022	A*	`conf`	Scalable Differentially Private Clustering via Hierarchically Separated Trees. KDD Vincent Cohen-Addad, Alessandro Epasto, Silvio Lattanzi, Vahab Mirrokni, Andres Muñoz Medina, David Saulpic, Chris Schwiegelshohn, Sergei Vassilvitskii
2022	J	`jnl`	Sequential Attention for Feature Selection. CoRR MohammadHossein Bateni, Lin Chen, Matthew Fahrbach, Gang Fu, Vahab Mirrokni, Taisuke Yasuda
2022	J	`jnl`	Stars: Tera-Scale Graph Building for Clustering and Graph Learning. CoRR CJ Carey, Jonathan Halcrow, Rajesh Jayaram, Vahab Mirrokni, Warren Schudy, Peilin Zhong
2022	A*	`conf`	Stars: Tera-Scale Graph Building for Clustering and Learning. NeurIPS CJ Carey, Jonathan Halcrow, Rajesh Jayaram, Vahab Mirrokni, Warren Schudy, Peilin Zhong