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Logarithmic Regret from Sublinear Hints


Nov 09, 2021
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit


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User-Level Private Learning via Correlated Sampling


Oct 21, 2021
Badih Ghazi, Ravi Kumar, Pasin Manurangsi

* To appear in NeurIPS 2021 

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Large-Scale Differentially Private BERT


Aug 03, 2021
Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, Pasin Manurangsi

* 12 pages, 6 figures 

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Locally Private k-Means in One Round


May 15, 2021
Alisa Chang, Badih Ghazi, Ravi Kumar, Pasin Manurangsi

* 35 pages. To appear in ICML'21 

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Performance Dependency of LSTM and NAR Beamformers With Respect to Sensor Array Properties in V2I Scenario


Feb 17, 2021
Prateek Bhadauria, Ravi Kumar, Sanjay Sharma

* 18 pages , 29 references 

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On Deep Learning with Label Differential Privacy


Feb 11, 2021
Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang

* 26 pages, 4 figures 

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On Avoiding the Union Bound When Answering Multiple Differentially Private Queries


Dec 16, 2020
Badih Ghazi, Ravi Kumar, Pasin Manurangsi

* 12 pages 

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Sample-efficient proper PAC learning with approximate differential privacy


Dec 07, 2020
Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi

* 40 pages 

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Robust and Private Learning of Halfspaces


Nov 30, 2020
Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thao Nguyen


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On Additive Approximate Submodularity


Oct 07, 2020
Flavio Chierichetti, Anirban Dasgupta, Ravi Kumar


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Online Linear Optimization with Many Hints


Oct 06, 2020
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit

* Accepted at Neurips 2020 

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On Distributed Differential Privacy and Counting Distinct Elements


Sep 21, 2020
Lijie Chen, Badih Ghazi, Ravi Kumar, Pasin Manurangsi

* 68 pages, 4 algorithms 

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Differentially Private Clustering: Tight Approximation Ratios


Aug 18, 2020
Badih Ghazi, Ravi Kumar, Pasin Manurangsi

* 60 pages, 1 table 

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Near-tight closure bounds for Littlestone and threshold dimensions


Jul 07, 2020
Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi

* 7 pages 

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Fair Hierarchical Clustering


Jun 19, 2020
Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang


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Fair Correlation Clustering


Mar 02, 2020
Sara Ahmadian, Alessandro Epasto, Ravi Kumar, Mohammad Mahdian


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Online Learning with Imperfect Hints


Feb 11, 2020
Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit


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Preventing Adversarial Use of Datasets through Fair Core-Set Construction


Oct 24, 2019
Benjamin Spector, Ravi Kumar, Andrew Tomkins

* 6 pages, 2 figures, NeurIPS 2019 Privacy In Machine Learning Workshop (PriML 2019) 

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Private Heavy Hitters and Range Queries in the Shuffled Model


Aug 29, 2019
Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, Ameya Velingker

* 30 pages 

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Testing Mixtures of Discrete Distributions


Jul 06, 2019
Maryam Aliakbarpour, Ravi Kumar, Ronitt Rubinfeld

* Appeared in COLT 2019 

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Clustering without Over-Representation


May 29, 2019
Sara Ahmadian, Alessandro Epasto, Ravi Kumar, Mohammad Mahdian

* in Proceedings of The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2019 
* 10 pages, 6 figures, in KDD 2019 

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On the Learnability of Deep Random Networks


Apr 08, 2019
Abhimanyu Das, Sreenivas Gollapudi, Ravi Kumar, Rina Panigrahy


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Fair Clustering Through Fairlets


Feb 15, 2018
Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvitskii

* NIPS 2017: 5036-5044 

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Linear Additive Markov Processes


Apr 05, 2017
Ravi Kumar, Maithra Raghu, Tamas Sarlos, Andrew Tomkins

* Accepted to WWW 2017 

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Conversational flow in Oxford-style debates


Apr 11, 2016
Justine Zhang, Ravi Kumar, Sujith Ravi, Cristian Danescu-Niculescu-Mizil

* To appear at NAACL 2016. 5 pp, 1 fig. Data and other info available at http://www.cs.cornell.edu/~cristian/debates 

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