Sample-optimal average-case sparse Fourier Transform in two dimensions. - Badih Ghazi; Haitham Hassanieh; Piotr Indyk; Dina Katabi; Eric Price 001; Lixin Shi (Allerton, 2013)

The Information Complexity of Hamming Distance. - Eric Blais; Joshua Brody; Badih Ghazi (APPROX-RANDOM, 2014)

Linear Programming Decoding of Spatially Coupled Codes. - Louay Bazzi; Badih Ghazi; Rüdiger L. Urbanke (IEEE Trans. Inf. Theory, 2014)

On the NP-hardness of bounded distance decoding of Reed-Solomon codes. - Venkata Gandikota; Badih Ghazi; Elena Grigorescu (ISIT, 2015)

Communication Complexity of Permutation-Invariant Functions. - Badih Ghazi; Pritish Kamath; Madhu Sudan 001 (Electron. Colloquium Comput. Complex., 2015)

The Optimality of Correlated Sampling. - Mohammad Bavarian; Badih Ghazi; Elad Haramaty; Pritish Kamath; Ronald L. Rivest; Madhu Sudan 001 (Electron. Colloquium Comput. Complex., 2016)

Decidability of Non-Interactive Simulation of Joint Distributions. - Badih Ghazi; Pritish Kamath; Madhu Sudan 001 (Electron. Colloquium Comput. Complex., 2016)

On the Power of Learning from k-Wise Queries. - Vitaly Feldman; Badih Ghazi (ITCS, 2017)

Compression in a Distributed Setting. - Badih Ghazi; Elad Haramaty; Pritish Kamath; Madhu Sudan 001 (ITCS, 2017)

Resource-Efficient Common Randomness and Secret-Key Schemes. - Badih Ghazi; T. S. Jayram (Electron. Colloquium Comput. Complex., 2017)

Dimension Reduction for Polynomials over Gaussian Space and Applications. - Badih Ghazi; Pritish Kamath; Prasad Raghavendra (Electron. Colloquium Comput. Complex., 2017)

The Power of Shared Randomness in Uncertain Communication. - Badih Ghazi; Madhu Sudan 001 (Electron. Colloquium Comput. Complex., 2017)

Communication with Contextual Uncertainty. - Badih Ghazi; Ilan Komargodski; Pravesh K. Kothari; Madhu Sudan 001 (Comput. Complex., 2018)

Communication-Rounds Tradeoffs for Common Randomness and Secret Key Generation. - Mitali Bafna; Badih Ghazi; Noah Golowich; Madhu Sudan 001 (Electron. Colloquium Comput. Complex., 2018)

NP-Hardness of Reed-Solomon Decoding, and the Prouhet-Tarry-Escott Problem. - Venkata Gandikota; Badih Ghazi; Elena Grigorescu (SIAM J. Comput., 2018)

LP/SDP Hierarchy Lower Bounds for Decoding Random LDPC Codes. - Badih Ghazi; Euiwoong Lee (IEEE Trans. Inf. Theory, 2018)

Computational aspects of communication amid uncertainty. - Badih Ghazi (2018)

Recursive Sketches for Modular Deep Learning. - Badih Ghazi; Rina Panigrahy; Joshua R. Wang (ICML, 2019)

Scalable and Differentially Private Distributed Aggregation in the Shuffled Model. - Badih Ghazi; Rasmus Pagh; Ameya Velingker (CoRR, 2019)

Private Heavy Hitters and Range Queries in the Shuffled Model. - Badih Ghazi; Noah Golowich; Ravi Kumar 001; Rasmus Pagh; Ameya Velingker (CoRR, 2019)

Advances and Open Problems in Federated Learning. - Peter Kairouz; H. Brendan McMahan; Brendan Avent; Aurélien Bellet; Mehdi Bennis; Arjun Nitin Bhagoji; Kallista A. Bonawitz; Zachary Charles; Graham Cormode; Rachel Cummings; Rafael G. L. D'Oliveira; Salim El Rouayheb; et al. (CoRR, 2019)

On the Power of Multiple Anonymous Messages. - Badih Ghazi; Noah Golowich; Ravi Kumar 001; Rasmus Pagh; Ameya Velingker (IACR Cryptol. ePrint Arch., 2019)

Private Aggregation from Fewer Anonymous Messages. - Badih Ghazi; Pasin Manurangsi; Rasmus Pagh; Ameya Velingker (EUROCRYPT (2), 2020)

Pure Differentially Private Summation from Anonymous Messages. - Badih Ghazi; Noah Golowich; Ravi Kumar 001; Pasin Manurangsi; Rasmus Pagh; Ameya Velingker (ITC, 2020)

Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi; Rasmus Pagh (ICML, 2020)

Differentially Private Clustering: Tight Approximation Ratios. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi (NeurIPS, 2020)

Near-tight closure bounds for Littlestone and threshold dimensions. - Badih Ghazi; Noah Golowich; Ravi Kumar 001; Pasin Manurangsi (CoRR, 2020)

Optimality of Correlated Sampling Strategies. - Mohammad Bavarian; Badih Ghazi; Elad Haramaty; Pritish Kamath; Ronald L. Rivest; Madhu Sudan 001 (Theory Comput., 2020)

Robust and Private Learning of Halfspaces. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi; Thao Nguyen (AISTATS, 2021)

Near-tight closure b ounds for the Littlestone and threshold dimensions. - Badih Ghazi; Noah Golowich; Ravi Kumar 001; Pasin Manurangsi (ALT, 2021)

On Avoiding the Union Bound When Answering Multiple Differentially Private Queries. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi (COLT, 2021)

On the Power of Multiple Anonymous Messages: Frequency Estimation and Selection in the Shuffle Model of Differential Privacy. - Badih Ghazi; Noah Golowich; Ravi Kumar 001; Rasmus Pagh; Ameya Velingker (EUROCRYPT (3), 2021)

Locally Private k-Means in One Round. - Alisa Chang; Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi (ICML, 2021)

Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi; Rasmus Pagh; Amer Sinha (ICML, 2021)

On Distributed Differential Privacy and Counting Distinct Elements. - Lijie Chen 001; Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi (ITCS, 2021)

Deep Learning with Label Differential Privacy. - Badih Ghazi; Noah Golowich; Ravi Kumar 001; Pasin Manurangsi; Chiyuan Zhang (NeurIPS, 2021)

User-Level Differentially Private Learning via Correlated Sampling. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi (NeurIPS, 2021)

Sample-efficient proper PAC learning with approximate differential privacy. - Badih Ghazi; Noah Golowich; Ravi Kumar 001; Pasin Manurangsi (STOC, 2021)

Google COVID-19 Vaccination Search Insights: Anonymization Process Description. - Shailesh Bavadekar; Adam Boulanger; John Davis; Damien Desfontaines; Evgeniy Gabrilovich; Krishna Gadepalli; Badih Ghazi; Tague Griffith; Jai Prakash Gupta 001; Chaitanya Kamath; Dennis Kraft; Ravi Kumar 001; et al. (CoRR, 2021)

User-Level Private Learning via Correlated Sampling. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi (CoRR, 2021)

Advances and Open Problems in Federated Learning. - Peter Kairouz; H. Brendan McMahan; Brendan Avent; Aurélien Bellet; Mehdi Bennis; Arjun Nitin Bhagoji; Kallista A. Bonawitz; Zachary Charles; Graham Cormode; Rachel Cummings; Rafael G. L. D'Oliveira; Hubert Eichner; et al. (Found. Trends Mach. Learn., 2021)

Private Rank Aggregation in Central and Local Models. - Daniel Alabi; Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi (AAAI, 2022)

Large-Scale Differentially Private BERT. - Rohan Anil; Badih Ghazi; Vineet Gupta 001; Ravi Kumar 001; Pasin Manurangsi (EMNLP (Findings), 2022)

Faster Privacy Accounting via Evolving Discretization. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi (ICML, 2022)

Anonymized Histograms in Intermediate Privacy Models. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi (NeurIPS, 2022)

Private Isotonic Regression. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi (NeurIPS, 2022)

Differentially Private All-Pairs Shortest Path Distances: Improved Algorithms and Lower Bounds. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi; Jelani Nelson (CoRR, 2022)

Distributed, Private, Sparse Histograms in the Two-Server Model. - James Bell 001; Adrià Gascón; Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi; Mariana Raykova 001; Phillipp Schoppmann (IACR Cryptol. ePrint Arch., 2022)

Connect the Dots: Tighter Discrete Approximations of Privacy Loss Distributions. - Vadym Doroshenko; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi (Proc. Priv. Enhancing Technol., 2022)

Multiparty Reach and Frequency Histogram: Private, Secure, and Practical. - Badih Ghazi; Ben Kreuter; Ravi Kumar 001; Pasin Manurangsi; Jiayu Peng; Evgeny Skvortsov; Yao Wang; Craig Wright (Proc. Priv. Enhancing Technol., 2022)

Private Aggregation of Trajectories. - Badih Ghazi; Neel Kamal; Ravi Kumar 001; Pasin Manurangsi; Annika Zhang (Proc. Priv. Enhancing Technol., 2022)

Differentially Private Heatmaps. - Badih Ghazi; Junfeng He; Kai Kohlhoff; Ravi Kumar 001; Pasin Manurangsi; Vidhya Navalpakkam; Nachiappan Valliappan (AAAI, 2023)

Optimizing Hierarchical Queries for the Attribution Reporting API. - Matthew Dawson; Badih Ghazi; Pritish Kamath; Kapil Kumar; Ravi Kumar 001; Bo Luan; Pasin Manurangsi; Nishanth Mundru; Harikesh Nair; Adam Sealfon; Shengyu Zhu 004 (AdKDD@KDD, 2023)

Private Ad Modeling with DP-SGD. - Carson Denison; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Krishna Giri Narra; Amer Sinha; Avinash V. Varadarajan; Chiyuan Zhang (AdKDD@KDD, 2023)

Differentially Private Aggregation via Imperfect Shuffling. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi; Jelani Nelson; Samson Zhou (ITC, 2023)

Ticketed Learning-Unlearning Schemes. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Ayush Sekhari; Chiyuan Zhang (COLT, 2023)

Towards Separating Computational and Statistical Differential Privacy. - Badih Ghazi; Rahul Ilango; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi (FOCS, 2023)

On Differentially Private Counting on Trees. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Kewen Wu 001 (ICALP, 2023)

Regression with Label Differential Privacy. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Ethan Leeman; Pasin Manurangsi; Avinash V. Varadarajan; Chiyuan Zhang (ICLR, 2023)

On User-Level Private Convex Optimization. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Raghu Meka; Chiyuan Zhang (ICML, 2023)

Algorithms with More Granular Differential Privacy Guarantees. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi; Thomas Steinke 002 (ITCS, 2023)

Private Counting of Distinct and k-Occurring Items in Time Windows. - Badih Ghazi; Ravi Kumar 001; Jelani Nelson; Pasin Manurangsi (ITCS, 2023)

Privacy in Advertising: Analytics and Modeling. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi (KDD, 2023)

On Differentially Private Sampling from Gaussian and Product Distributions. - Badih Ghazi; Xiao Hu 005; Ravi Kumar 001; Pasin Manurangsi (NeurIPS, 2023)

Sparsity-Preserving Differentially Private Training of Large Embedding Models. - Badih Ghazi; Yangsibo Huang; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (NeurIPS, 2023)

User-Level Differential Privacy With Few Examples Per User. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Raghu Meka; Chiyuan Zhang (NeurIPS, 2023)

On Computing Pairwise Statistics with Local Differential Privacy. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Adam Sealfon (NeurIPS, 2023)

Optimal Unbiased Randomizers for Regression with Label Differential Privacy. - Ashwinkumar Badanidiyuru Varadaraja; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Ethan Leeman; Pasin Manurangsi; Avinash V. Varadarajan; Chiyuan Zhang (NeurIPS, 2023)

Differentially Private Data Release over Multiple Tables. - Badih Ghazi; Xiao Hu 005; Ravi Kumar 001; Pasin Manurangsi (PODS, 2023)

Differentially Private All-Pairs Shortest Path Distances: Improved Algorithms and Lower Bounds. - Justin Y. Chen; Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi; Shyam Narayanan; Jelani Nelson; Yinzhan Xu (SODA, 2023)

Separating Computational and Statistical Differential Privacy (Under Plausible Assumptions). - Badih Ghazi; Rahul Ilango; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi (CoRR, 2023)

Training Differentially Private Ad Prediction Models With Semi-Sensitive Features. - Lynn Chua; Qiliang Cui; Badih Ghazi; Charlie Harrison; Pritish Kamath; Walid Krichene; Ravi Kumar 001; Pasin Manurangsi; Nicolas Mayoraz; Hema Venkata Krishna Giri Narra; Steffen Rendle; Amer Sinha; et al. (AdKDD@KDD, 2024)

Pure-DP Aggregation in the Shuffle Model: Error-Optimal and Communication-Efficient. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi (ITC, 2024)

On Convex Optimization with Semi-Sensitive Features. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Raghu Meka; Chiyuan Zhang (COLT, 2024)

LabelDP-Pro: Learning with Label Differential Privacy via Projections. - Badih Ghazi; Yangsibo Huang; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Chiyuan Zhang (ICLR, 2024)

How Private are DP-SGD Implementations? - Lynn Chua; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (ICML, 2024)

Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization. - Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Adam Sealfon (ICML, 2024)

Scalable DP-SGD: Shuffling vs. Poisson Subsampling. - Lynn Chua; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (NeurIPS, 2024)

Differentially Private Optimization with Sparse Gradients. - Badih Ghazi; Cristóbal Guzmán; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi (NeurIPS, 2024)

Privacy in Web Advertising: Analytics and Modeling. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi (WWW (Companion Volume), 2024)

Training Differentially Private Ad Prediction Models with Semi-Sensitive Features. - Lynn Chua; Qiliang Cui; Badih Ghazi; Charlie Harrison; Pritish Kamath; Walid Krichene; Ravi Kumar 001; Pasin Manurangsi; Krishna Giri Narra; Amer Sinha; Avinash V. Varadarajan; Chiyuan Zhang (CoRR, 2024)

How Private is DP-SGD? - Lynn Chua; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (CoRR, 2024)

Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning. - Lynn Chua; Badih Ghazi; Yangsibo Huang; Pritish Kamath; Daogao Liu; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (CoRR, 2024)

Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models. - Lynn Chua; Badih Ghazi; Yangsibo Huang; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Amer Sinha; Chulin Xie; Chiyuan Zhang (CoRR, 2024)

On Memorization of Large Language Models in Logical Reasoning. - Chulin Xie; Yangsibo Huang; Chiyuan Zhang; Da Yu; Xinyun Chen; Bill Yuchen Lin; Bo Li 026; Badih Ghazi; Ravi Kumar 001 (CoRR, 2024)

On the Differential Privacy and Interactivity of Privacy Sandbox Reports. - Badih Ghazi; Charlie Harrison; Arpana Hosabettu; Pritish Kamath; Alexander Knop; S. Ravi Kumar 001; Ethan Leeman; Pasin Manurangsi; Vikas Sahu (CoRR, 2024)

Summary Reports Optimization in the Privacy Sandbox Attribution Reporting API. - Hidayet Aksu; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Adam Sealfon; Avinash V. Varadarajan (Proc. Priv. Enhancing Technol., 2024)

Differentially Private Ad Conversion Measurement. - John Delaney; Badih Ghazi; Charlie Harrison; Christina Ilvento; Ravi Kumar 001; Pasin Manurangsi; Martin Pál; Karthik Prabhakar; Mariana Raykova 001 (Proc. Priv. Enhancing Technol., 2024)

Balls-and-Bins Sampling for DP-SGD. - Lynn Chua; Badih Ghazi; Charlie Harrison; Pritish Kamath; Ravi Kumar 001; Ethan Leeman; Pasin Manurangsi; Amer Sinha; Chiyuan Zhang (AISTATS, 2025)

PREM: Privately Answering Statistical Queries with Relative Error. - Badih Ghazi; Cristóbal Guzmán; Pritish Kamath; Alexander Knop; Ravi Kumar 001; Pasin Manurangsi; Sushant Sachdeva (COLT, 2025)

Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy. - Yangsibo Huang; Daogao Liu; Lynn Chua; Badih Ghazi; Pritish Kamath; Ravi Kumar 001; Pasin Manurangsi; Milad Nasr; Amer Sinha; Chiyuan Zhang (ICLR, 2025)

Differential Privacy on Trust Graphs. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi; Serena Wang 001 (ITCS, 2025)

How Unique is Whose Web Browser? The role of demographics in browser fingerprinting among US users. - Alex Berke; Badih Ghazi; Enrico Bacis; Pritish Kamath; Ravi Kumar 001; Robin Lassonde; Pasin Manurangsi; Umar Syed (Proc. Priv. Enhancing Technol., 2025)

On the Differential Privacy and Interactivity of Privacy Sandbox Reports. - Badih Ghazi; Charlie Harrison; Arpana Hosabettu; Pritish Kamath; Alexander Knop; Ravi Kumar 001; Ethan Leeman; Pasin Manurangsi; Mariana Raykova 001; Vikas Sahu; Phillipp Schoppmann (Proc. Priv. Enhancing Technol., 2025)

Scaling Laws for Differentially Private Language Models. - Ryan McKenna; Yangsibo Huang; Amer Sinha; Borja Balle; Zachary Charles; Christopher A. Choquette-Choo; Badih Ghazi; Georgios Kaissis; Ravi Kumar 001; Ruibo Liu; Da Yu; Chiyuan Zhang (CoRR, 2025)

Scaling Embedding Layers in Language Models. - Da Yu; Edith Cohen; Badih Ghazi; Yangsibo Huang; Pritish Kamath; Ravi Kumar 001; Daogao Liu; Chiyuan Zhang (CoRR, 2025)

Linear-Time User-Level DP-SCO via Robust Statistics. - Badih Ghazi; Ravi Kumar 001; Daogao Liu; Pasin Manurangsi (CoRR, 2025)

Urania: Differentially Private Insights into AI Use. - Daogao Liu; Edith Cohen; Badih Ghazi; Peter Kairouz; Pritish Kamath; Alexander Knop; Ravi Kumar 001; Pasin Manurangsi; Adam Sealfon; Da Yu; Chiyuan Zhang (CoRR, 2025)

Quantifying Cross-Modality Memorization in Vision-Language Models. - Yuxin Wen; Yangsibo Huang; Tom Goldstein; Ravi Kumar 001; Badih Ghazi; Chiyuan Zhang (CoRR, 2025)

Private Hyperparameter Tuning with Ex-Post Guarantee. - Badih Ghazi; Pritish Kamath; Alexander Knop; Ravi Kumar 001; Pasin Manurangsi; Chiyuan Zhang (CoRR, 2025)