Kumar, Ravi

73 publications

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

ICLR 2025 Descent with Misaligned Gradients and Applications to Hidden Convexity Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit

ICML 2025 LAuReL: Learned Augmented Residual Layer Gaurav Menghani, Ravi Kumar, Sanjiv Kumar

NeurIPS 2025 Length Generalization via Auxiliary Tasks Pranjal Awasthi, Anupam Gupta, Ravi Kumar

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

NeurIPS 2025 Private Hyperparameter Tuning with Ex-Post Guarantee Badih Ghazi, Pritish Kamath, Alexander Knop, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang

NeurIPS 2025 Quantifying Cross-Modality Memorization in Vision-Language Models Yuxin Wen, Yangsibo Huang, Tom Goldstein, Ravi Kumar, Badih Ghazi, Chiyuan Zhang

NeurIPS 2025 Scaling Embedding Layers in Language Models Da Yu, Edith Cohen, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Daogao Liu, Chiyuan Zhang

ICML 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, Ruibo Liu, Da Yu, Chiyuan Zhang

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

NeurIPSW 2024 Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models Lynn Chua, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chulin Xie, Chiyuan Zhang

NeurIPS 2024 Differentially Private Optimization with Sparse Gradients Badih Ghazi, Cristóbal Guzmán, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

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

ICML 2024 Individualized Privacy Accounting via Subsampling with Applications in Combinatorial Optimization Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon

ICMLW 2024 LAuReL: Learned Augmented Residual Layer Gaurav Menghani, Ravi Kumar, Sanjiv Kumar

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

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

NeurIPSW 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, Badih Ghazi, Ravi Kumar

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

NeurIPS 2024 Tight Bounds for Learning RUMs from Small Slates Flavio Chierichetti, Mirko Giacchini, Ravi Kumar, Alessandro Panconesi, Andrew Tomkins

AISTATS 2023 Approximating a RUM from Distributions on $k$-Slates Flavio Chierichetti, Mirko Giacchini, Ravi Kumar, Alessandro Panconesi, Andrew Tomkins

ICML 2023 Bandit Online Linear Optimization with Hints and Queries Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit

AAAI 2023 Differentially Private Heatmaps Badih Ghazi, Junfeng He, Kai Kohlhoff, Ravi Kumar, Pasin Manurangsi, Vidhya Navalpakkam, Nachiappan Valliappan

NeurIPS 2023 On Computing Pairwise Statistics with Local Differential Privacy Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon

NeurIPS 2023 On Differentially Private Sampling from Gaussian and Product Distributions Badih Ghazi, Xiao Hu, Ravi Kumar, Pasin Manurangsi

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

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

ICLR 2023 Regression with Label Differential Privacy Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash Varadarajan, Chiyuan Zhang

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

COLT 2023 Ticketed Learning–Unlearning Schemes Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang

NeurIPS 2023 User-Level Differential Privacy with Few Examples per User Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang

NeurIPS 2022 Anonymized Histograms in Intermediate Privacy Models Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

ICML 2022 Faster Privacy Accounting via Evolving Discretization Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

ICML 2022 Parsimonious Learning-Augmented Caching Sungjin Im, Ravi Kumar, Aditya Petety, Manish Purohit

NeurIPS 2022 Private Isotonic Regression Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi

AAAI 2022 Private Rank Aggregation in Central and Local Models Daniel Alabi, Badih Ghazi, Ravi Kumar, Pasin Manurangsi

ICML 2022 RUMs from Head-to-Head Contests Matteo Almanza, Flavio Chierichetti, Ravi Kumar, Alessandro Panconesi, Andrew Tomkins

AISTATS 2021 Power of Hints for Online Learning with Movement Costs Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit

AISTATS 2021 Robust and Private Learning of Halfspaces Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thao Nguyen

NeurIPS 2021 Deep Learning with Label Differential Privacy Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang

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

ICML 2021 Light RUMs Flavio Chierichetti, Ravi Kumar, Andrew Tomkins

ICML 2021 Locally Private K-Means in One Round Alisa Chang, Badih Ghazi, Ravi Kumar, Pasin Manurangsi

NeurIPS 2021 Logarithmic Regret from Sublinear Hints Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit

ALT 2021 Near-Tight Closure Bounds for the Littlestone and Threshold Dimensions Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi

COLT 2021 On Avoiding the Union Bound When Answering Multiple Differentially Private Queries Badih Ghazi, Ravi Kumar, Pasin Manurangsi

NeurIPS 2021 Online Knapsack with Frequency Predictions Sungjin Im, Ravi Kumar, Mahshid Montazer Qaem, Manish Purohit

NeurIPS 2021 User-Level Differentially Private Learning via Correlated Sampling Badih Ghazi, Ravi Kumar, Pasin Manurangsi

NeurIPS 2020 Differentially Private Clustering: Tight Approximation Ratios Badih Ghazi, Ravi Kumar, Pasin Manurangsi

AISTATS 2020 Fair Correlation Clustering Sara Ahmadian, Alessandro Epasto, Ravi Kumar, Mohammad Mahdian

NeurIPS 2020 Fair Hierarchical Clustering Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang

ICML 2020 Online Learning with Imperfect Hints Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit

NeurIPS 2020 Online Linear Optimization with Many Hints Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit

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

NeurIPS 2019 Efficient Rematerialization for Deep Networks Ravi Kumar, Manish Purohit, Zoya Svitkina, Erik Vee, Joshua Wang

ICML 2019 Faster Algorithms for Binary Matrix Factorization Ravi Kumar, Rina Panigrahy, Ali Rahimi, David Woodruff

AISTATS 2019 Matroids, Matchings, and Fairness Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvtiskii

COLT 2019 Testing Mixtures of Discrete Distributions Maryam Aliakbarpour, Ravi Kumar, Ronitt Rubinfeld

NeurIPS 2018 Improving Online Algorithms via ML Predictions Manish Purohit, Zoya Svitkina, Ravi Kumar

ICML 2018 Learning a Mixture of Two Multinomial Logits Flavio Chierichetti, Ravi Kumar, Andrew Tomkins

NeurIPS 2018 Mallows Models for Top-K Lists Flavio Chierichetti, Anirban Dasgupta, Shahrzad Haddadan, Ravi Kumar, Silvio Lattanzi

ICML 2017 Algorithms for $\ell_p$ Low-Rank Approximation Flavio Chierichetti, Sreenivas Gollapudi, Ravi Kumar, Silvio Lattanzi, Rina Panigrahy, David P. Woodruff

NeurIPS 2017 Fair Clustering Through Fairlets Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvitskii

NeurIPS 2016 On Mixtures of Markov Chains Rishi Gupta, Ravi Kumar, Sergei Vassilvitskii

AISTATS 2016 Sketching, Embedding and Dimensionality Reduction in Information Theoretic Spaces Amirali Abdullah, Ravi Kumar, Andrew McGregor, Sergei Vassilvitskii, Suresh Venkatasubramanian

AAAI 2015 Refer-to-as Relations as Semantic Knowledge Song Feng, Sujith Ravi, Ravi Kumar, Polina Kuznetsova, Wei Liu, Alexander C. Berg, Tamara L. Berg, Yejin Choi

ICML 2013 Near-Optimal Bounds for Cross-Validation via Loss Stability Ravi Kumar, Daniel Lokshtanov, Sergei Vassilvitskii, Andrea Vattani

NeurIPS 2012 Selecting Diverse Features via Spectral Regularization Abhimanyu Das, Anirban Dasgupta, Ravi Kumar

NeurIPS 2008 Mortal Multi-Armed Bandits Deepayan Chakrabarti, Ravi Kumar, Filip Radlinski, Eli Upfal

ICCV 1998 Spatial Color Indexing and Applications Jing Huang, Ravi Kumar, Mandar Mitra, Wei-Jing Zhu

CVPR 1997 Image Indexing Using Color Correlograms Jing Huang, Ravi Kumar, Mandar Mitra, Wei-Jing Zhu, Ramin Zabih

COLT 1997 Learning Distributions from Random Walks Funda Ergün, Ravi Kumar, Ronitt Rubinfeld

COLT 1995 On Learning Bounded-Width Branching Programs Funda Ergün, Ravi Kumar, Ronitt Rubinfeld