Ravi Kumar

ORCID

Publication Activity (10 Years)

Years Active: 1993-2024
Publications (10 Years): 174

Top Venues

Publications

John Delaney, Badih Ghazi, Charlie Harrison, Christina Ilvento, Ravi Kumar, Pasin Manurangsi, Martin Pál, Karthik Prabhakar, Mariana Raykova
Differentially Private Ad Conversion Measurement. Proc. Priv. Enhancing Technol. 2024 (2) (2024)
Hidayet Aksu, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon, Avinash V. Varadarajan
Summary Reports Optimization in the Privacy Sandbox Attribution Reporting API. Proc. Priv. Enhancing Technol. 2024 (4) (2024)
Ravi Kumar, Manish Purohit, Zoya Svitkina
Improving Online Algorithms via ML Predictions. CoRR (2024)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang
On Convex Optimization with Semi-Sensitive Features. COLT (2024)
Sungjin Im, Ravi Kumar, Shi Li, Aditya Petety, Manish Purohit
Online Load and Graph Balancing for Random Order Inputs. CoRR (2024)
Lynn Chua, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang
How Private is DP-SGD? CoRR (2024)
Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang
LabelDP-Pro: Learning with Label Differential Privacy via Projections. ICLR (2024)
John Delaney, Badih Ghazi, Charlie Harrison, Christina Ilvento, Ravi Kumar, Pasin Manurangsi, Martin Pal, Karthik Prabhakar, Mariana Raykova
Differentially Private Ad Conversion Measurement. CoRR (2024)
Badih Ghazi, Ravi Kumar, Pasin Manurangsi
Privacy in Web Advertising: Analytics and Modeling. WWW (Companion Volume) (2024)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang
On Convex Optimization with Semi-Sensitive Features. CoRR (2024)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon
Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization. CoRR (2024)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon
On Computing Pairwise Statistics with Local Differential Privacy. CoRR (2024)
Lynn Chua, Qiliang Cui, Badih Ghazi, Charlie Harrison, Pritish Kamath, Walid Krichene, Ravi Kumar, Pasin Manurangsi, Krishna Giri Narra, Amer Sinha, Avinash V. Varadarajan, Chiyuan Zhang
Training Differentially Private Ad Prediction Models with Semi-Sensitive Features. CoRR (2024)
Lynn Chua, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chulin Xie, Chiyuan Zhang
Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models. CoRR (2024)
Flavio Chierichetti, Ravi Kumar, Andrew Tomkins
Discrete Choice and Applications. WWW (Companion Volume) (2024)
Badih Ghazi, Cristóbal Guzmán, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Differentially Private Optimization with Sparse Gradients. CoRR (2024)
Sungjin Im, Ravi Kumar, Shi Li, Aditya Petety, Manish Purohit
Online Load and Graph Balancing for Random Order Inputs. SPAA (2024)
Badih Ghazi, Ravi Kumar, Pasin Manurangsi
Pure-DP Aggregation in the Shuffle Model: Error-Optimal and Communication-Efficient. ITC (2024)
Badih Ghazi, Junfeng He, Kai Kohlhoff, Ravi Kumar, Pasin Manurangsi, Vidhya Navalpakkam, Nachiappan Valliappan
Differentially Private Heatmaps. AAAI (2023)
Badih Ghazi, Rahul Ilango, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Separating Computational and Statistical Differential Privacy (Under Plausible Assumptions). CoRR (2023)
Badih Ghazi, Xiao Hu, Ravi Kumar, Pasin Manurangsi
On Differentially Private Sampling from Gaussian and Product Distributions. CoRR (2023)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Raghu Meka, Pasin Manurangsi, Chiyuan Zhang
On User-Level Private Convex Optimization. CoRR (2023)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang
Ticketed Learning-Unlearning Schemes. COLT (2023)
Ashwinkumar Badanidiyuru Varadaraja, Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash V. Varadarajan, Chiyuan Zhang
Optimal Unbiased Randomizers for Regression with Label Differential Privacy. NeurIPS (2023)
Flavio Chierichetti, Mirko Giacchini, Ravi Kumar, Alessandro Panconesi, Andrew Tomkins
Approximating a RUM from Distributions on k-Slates. AISTATS (2023)
Carson Denison, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Krishna Giri Narra, Amer Sinha, Avinash V. Varadarajan, Chiyuan Zhang
Private Ad Modeling with DP-SGD. AdKDD@KDD (2023)
Badih Ghazi, Xiao Hu, Ravi Kumar, Pasin Manurangsi
Differentially Private Data Release over Multiple Tables. PODS (2023)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash Varadarajan, Chiyuan Zhang
Regression with Label Differential Privacy. ICLR (2023)
Badih Ghazi, Ravi Kumar, Pasin Manurangsi
Privacy in Advertising: Analytics and Modeling. KDD (2023)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang
On User-Level Private Convex Optimization. ICML (2023)
Sungjin Im, Ravi Kumar, Mahshid Montazer Qaem, Manish Purohit
Non-clairvoyant Scheduling with Predictions. ACM Trans. Parallel Comput. 10 (4) (2023)
Sungjin Im, Ravi Kumar, Silvio Lattanzi, Benjamin Moseley, Sergei Vassilvitskii
Massively Parallel Computation: Algorithms and Applications. Found. Trends Optim. 5 (4) (2023)
Badih Ghazi, Rahul Ilango, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Towards Separating Computational and Statistical Differential Privacy. FOCS (2023)
Matthew Dawson, Badih Ghazi, Pritish Kamath, Kapil Kumar, Ravi Kumar, Bo Luan, Pasin Manurangsi, Nishanth Mundru, Harikesh Nair, Adam Sealfon, Shengyu Zhu
Optimizing Hierarchical Queries for the Attribution Reporting API. CoRR (2023)
Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang
Sparsity-Preserving Differentially Private Training of Large Embedding Models. NeurIPS (2023)
Badih Ghazi, Ravi Kumar, Jelani Nelson, Pasin Manurangsi
Private Counting of Distinct and k-Occurring Items in Time Windows. ITCS (2023)
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
Bandit Online Linear Optimization with Hints and Queries. ICML (2023)
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Jelani Nelson, Samson Zhou
Differentially Private Aggregation via Imperfect Shuffling. CoRR (2023)
Hidayet Aksu, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon, Avinash V. Varadarajan
Summary Reports Optimization in the Privacy Sandbox Attribution Reporting API. CoRR (2023)
Badih Ghazi, Ravi Kumar, Pasin Manurangsi
Pure-DP Aggregation in the Shuffle Model: Error-Optimal and Communication-Efficient. CoRR (2023)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang
User-Level Differential Privacy With Few Examples Per User. CoRR (2023)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Kewen Wu
On Differentially Private Counting on Trees. ICALP (2023)
Flavio Chierichetti, Mirko Giacchini, Ravi Kumar, Alessandro Panconesi, Andrew Tomkins
Approximating a RUM from Distributions on k-Slates. CoRR (2023)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon
On Computing Pairwise Statistics with Local Differential Privacy. NeurIPS (2023)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang
Ticketed Learning-Unlearning Schemes. CoRR (2023)
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Jelani Nelson, Samson Zhou
Differentially Private Aggregation via Imperfect Shuffling. ITC (2023)
Justin Y. Chen, Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Shyam Narayanan, Jelani Nelson, Yinzhan Xu
Differentially Private All-Pairs Shortest Path Distances: Improved Algorithms and Lower Bounds. SODA (2023)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang
User-Level Differential Privacy With Few Examples Per User. NeurIPS (2023)
Badih Ghazi, Xiao Hu, Ravi Kumar, Pasin Manurangsi
Differentially Private Data Release over Multiple Tables. CoRR (2023)
Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang
Sparsity-Preserving Differentially Private Training of Large Embedding Models. CoRR (2023)
Badih Ghazi, Xiao Hu, Ravi Kumar, Pasin Manurangsi
On Differentially Private Sampling from Gaussian and Product Distributions. NeurIPS (2023)
Ashwinkumar Badanidiyuru, Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash V. Varadarajan, Chiyuan Zhang
Optimal Unbiased Randomizers for Regression with Label Differential Privacy. CoRR (2023)
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thomas Steinke
Algorithms with More Granular Differential Privacy Guarantees. ITCS (2023)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Faster Privacy Accounting via Evolving Discretization. ICML (2022)
Andy Drucker, Ravi Kumar, Amit Sahai, Mohit Singh
Special Section on the Forty-Ninth Annual ACM Symposium on the Theory of Computing (STOC 2017). SIAM J. Comput. 51 (2) (2022)
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Jelani Nelson
Private Counting of Distinct and k-Occurring Items in Time Windows. CoRR (2022)
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thomas Steinke
Algorithms with More Granular Differential Privacy Guarantees. CoRR (2022)
Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi
The Gibbs-Rand Model. PODS (2022)
Vadym Doroshenko, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Connect the Dots: Tighter Discrete Approximations of Privacy Loss Distributions. CoRR (2022)
Matteo Almanza, Flavio Chierichetti, Ravi Kumar, Alessandro Panconesi, Andrew Tomkins
RUMs from Head-to-Head Contests. ICML (2022)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Anonymized Histograms in Intermediate Privacy Models. CoRR (2022)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Faster Privacy Accounting via Evolving Discretization. CoRR (2022)
Daniel Alabi, Badih Ghazi, Ravi Kumar, Pasin Manurangsi
Private Rank Aggregation in Central and Local Models. AAAI (2022)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Kewen Wu
On Differentially Private Counting on Trees. CoRR (2022)
Badih Ghazi, Neel Kamal, Ravi Kumar, Pasin Manurangsi, Annika Zhang
Private Aggregation of Trajectories. Proc. Priv. Enhancing Technol. 2022 (4) (2022)
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Jelani Nelson
Differentially Private All-Pairs Shortest Path Distances: Improved Algorithms and Lower Bounds. CoRR (2022)
Badih Ghazi, Ben Kreuter, Ravi Kumar, Pasin Manurangsi, Jiayu Peng, Evgeny Skvortsov, Yao Wang, Craig Wright
Multiparty Reach and Frequency Histogram: Private, Secure, and Practical. Proc. Priv. Enhancing Technol. 2022 (1) (2022)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash Varadarajan, Chiyuan Zhang
Regression with Label Differential Privacy. CoRR (2022)
James Bell, Adrià Gascón, Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Mariana Raykova, Phillipp Schoppmann
Distributed, Private, Sparse Histograms in the Two-Server Model. IACR Cryptol. ePrint Arch. 2022 (2022)
Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, Pasin Manurangsi
Large-Scale Differentially Private BERT. EMNLP (Findings) (2022)
Badih Ghazi, Junfeng He, Kai Kohlhoff, Ravi Kumar, Pasin Manurangsi, Vidhya Navalpakkam, Nachiappan Valliappan
Differentially Private Heatmaps. CoRR (2022)
James Bell, Adrià Gascón, Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Mariana Raykova, Phillipp Schoppmann
Distributed, Private, Sparse Histograms in the Two-Server Model. CCS (2022)
Vadym Doroshenko, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Connect the Dots: Tighter Discrete Approximations of Privacy Loss Distributions. Proc. Priv. Enhancing Technol. 2022 (4) (2022)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Private Isotonic Regression. NeurIPS (2022)
Carson Denison, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Krishna Giri Narra, Amer Sinha, Avinash Varadarajan, Chiyuan Zhang
Private Ad Modeling with DP-SGD. CoRR (2022)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Anonymized Histograms in Intermediate Privacy Models. NeurIPS (2022)
Nikhil Bansal, Christian Coester, Ravi Kumar, Manish Purohit, Erik Vee
Learning-Augmented Weighted Paging. SODA (2022)
Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi
Private Isotonic Regression. CoRR (2022)
Sungjin Im, Ravi Kumar, Aditya Petety, Manish Purohit
Parsimonious Learning-Augmented Caching. ICML (2022)
Flavio Chierichetti, Anirban Dasgupta, Ravi Kumar
On additive approximate submodularity. Theor. Comput. Sci. 922 (2022)
Sungjin Im, Ravi Kumar, Aditya Petety, Manish Purohit
Parsimonious Learning-Augmented Caching. CoRR (2022)
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Amer Sinha
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message. ICML (2021)
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thao Nguyen
Robust and Private Learning of Halfspaces. AISTATS (2021)
Badih Ghazi, Ravi Kumar, Pasin Manurangsi
User-Level Differentially Private Learning via Correlated Sampling. NeurIPS (2021)
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
Logarithmic Regret from Sublinear Hints. NeurIPS (2021)
Badih Ghazi, Ravi Kumar, Pasin Manurangsi
On Avoiding the Union Bound When Answering Multiple Differentially Private Queries. COLT (2021)
Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi
Near-tight closure b ounds for the Littlestone and threshold dimensions. ALT (2021)
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Amer Sinha
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message. CoRR (2021)
Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang
On Deep Learning with Label Differential Privacy. CoRR (2021)
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
Power of Hints for Online Learning with Movement Costs. AISTATS (2021)
Lijie Chen, Badih Ghazi, Ravi Kumar, Pasin Manurangsi
On Distributed Differential Privacy and Counting Distinct Elements. ITCS (2021)
Sungjin Im, Ravi Kumar, Mahshid Montazer Qaem, Manish Purohit
Non-Clairvoyant Scheduling with Predictions. SPAA (2021)
Shailesh Bavadekar, Adam Boulanger, John Davis, Damien Desfontaines, Evgeniy Gabrilovich, Krishna Gadepalli, Badih Ghazi, Tague Griffith, Jai Prakash Gupta, Chaitanya Kamath, Dennis Kraft, Ravi Kumar, Akim Kumok, Yael Mayer, Pasin Manurangsi, Arti Patankar, Irippuge Milinda Perera, Chris Scott, Tomer Shekel, Benjamin Miller, Karen Smith, Charlotte Stanton, Mimi Sun, Mark Young, Gregory Wellenius
Google COVID-19 Vaccination Search Insights: Anonymization Process Description. CoRR (2021)
Badih Ghazi, Ravi Kumar, Pasin Manurangsi
User-Level Private Learning via Correlated Sampling. CoRR (2021)
Flavio Chierichetti, Ravi Kumar, Andrew Tomkins
Light RUMs. ICML (2021)
Sungjin Im, Ravi Kumar, Mahshid Montazer Qaem, Manish Purohit
Online Knapsack with Frequency Predictions. NeurIPS (2021)
Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, Ameya Velingker
On the Power of Multiple Anonymous Messages: Frequency Estimation and Selection in the Shuffle Model of Differential Privacy. EUROCRYPT (3) (2021)
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
Logarithmic Regret from Sublinear Hints. CoRR (2021)
Alisa Chang, Badih Ghazi, Ravi Kumar, Pasin Manurangsi
Locally Private k-Means in One Round. CoRR (2021)
Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, Pasin Manurangsi
Large-Scale Differentially Private BERT. CoRR (2021)

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Publication Activity (10 Years)

Top Topics

Top Venues

Ravi Kumar