Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDRavi Kumar 0001
S. Ravi Kumar 0001
Person information
- affiliation: Google
- affiliation: Yahoo! Research
- affiliation: IBM Almaden Research Center
- affiliation: Cornell University, Department of Computer Science
Other persons with the same name
- Ravi Kumar — disambiguation page
- Ravi Kumar 0002
![[0000-0001-9922-9064]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0001-9922-9064")
— Jaypee Institute of Engineering & Technology, India - Ravi Kumar 0003 — General Motors Corporation, Packard Electric Division, USA
- Ravi Kumar 0004 — Department of Mechanical & Aerospace Engineering, University at Buffalo, Buffalo, NY, USA
- Ravi Kumar 0005 — Indian Institute of Information Technology, Kharagpur, India
- Ravi Kumar 0006 — Thapar University, Patiala, Punjab, India
- Ravi Kumar 0007 — Samsung Research Institute Delhi, India
- Ravi Kumar 0008 — CSIR-Institute of Microbial Technology, Chandigarh, India
- Ravi Kumar 0009 — SRM University AP - Amaravati, Mangalagiri, India (and 4 more)
- Ravi Kumar 0010 — Western Digital, Milpitas, CA, USA (and 2 more)
- M. Ravi Kumar 0002 (aka: Ravi Kumar 0011) — Indian Institute of Technology Hyderabad, Department of Computer Science and Engineering, India
- S. Ravi Kumar 0002 — Vel Tech University, Chennai, India
- S. Ravi Kumar 0003 — Krishna University, Machilipatnam, India
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2023
[c246]Badih Ghazi, Junfeng He, Kai Kohlhoff, Ravi Kumar, Pasin Manurangsi, Vidhya Navalpakkam, Nachiappan Valliappan:
Differentially Private Heatmaps. AAAI 2023: 7696-7704- [c245]Flavio Chierichetti, Mirko Giacchini, Ravi Kumar, Alessandro Panconesi, Andrew Tomkins:
Approximating a RUM from Distributions on k-Slates. AISTATS 2023: 4757-4767 - [c244]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Jelani Nelson, Samson Zhou:
Differentially Private Aggregation via Imperfect Shuffling. ITC 2023: 17:1-17:22 - [c243]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang:
Ticketed Learning-Unlearning Schemes. COLT 2023: 5110-5139 - [c242]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Kewen Wu:
On Differentially Private Counting on Trees. ICALP 2023: 66:1-66:18 - [c241]Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash Varadarajan, Chiyuan Zhang:
Regression with Label Differential Privacy. ICLR 2023 - [c240]Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit:
Bandit Online Linear Optimization with Hints and Queries. ICML 2023: 2313-2336 - [c239]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Raghu Meka, Chiyuan Zhang:
On User-Level Private Convex Optimization. ICML 2023: 11283-11299 - [c238]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thomas Steinke:
Algorithms with More Granular Differential Privacy Guarantees. ITCS 2023: 54:1-54:24 - [c237]Badih Ghazi, Ravi Kumar, Jelani Nelson, Pasin Manurangsi:
Private Counting of Distinct and k-Occurring Items in Time Windows. ITCS 2023: 55:1-55:24 - [c236]Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Privacy in Advertising: Analytics and Modeling. KDD 2023: 5802 - [c235]Badih Ghazi, Xiao Hu, Ravi Kumar, Pasin Manurangsi:
Differentially Private Data Release over Multiple Tables. PODS 2023: 207-219 - [c234]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: 5040-5067 - [e9]Ambuj K. Singh, Yizhou Sun, Leman Akoglu, Dimitrios Gunopulos, Xifeng Yan, Ravi Kumar, Fatma Ozcan, Jieping Ye:
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, Long Beach, CA, USA, August 6-10, 2023. ACM 2023 [contents] - [i62]Badih Ghazi, Rahul Ilango, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Separating Computational and Statistical Differential Privacy (Under Plausible Assumptions). CoRR abs/2301.00104 (2023) - [i61]Badih Ghazi, Pritish Kamath, Ravi Kumar, Raghu Meka, Pasin Manurangsi, Chiyuan Zhang:
On User-Level Private Convex Optimization. CoRR abs/2305.04912 (2023) - [i60]Flavio Chierichetti, Mirko Giacchini, Ravi Kumar, Alessandro Panconesi, Andrew Tomkins:
Approximating a RUM from Distributions on k-Slates. CoRR abs/2305.13283 (2023) - [i59]Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Pure-DP Aggregation in the Shuffle Model: Error-Optimal and Communication-Efficient. CoRR abs/2305.17634 (2023) - [i58]Badih Ghazi, Xiao Hu, Ravi Kumar, Pasin Manurangsi:
On Differentially Private Sampling from Gaussian and Product Distributions. CoRR abs/2306.12549 (2023) - [i57]Badih Ghazi, Xiao Hu, Ravi Kumar, Pasin Manurangsi:
Differentially Private Data Release over Multiple Tables. CoRR abs/2306.15201 (2023) - [i56]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Ayush Sekhari, Chiyuan Zhang:
Ticketed Learning-Unlearning Schemes. CoRR abs/2306.15744 (2023) - [i55]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 abs/2308.13510 (2023) - [i54]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Jelani Nelson, Samson Zhou:
Differentially Private Aggregation via Imperfect Shuffling. CoRR abs/2308.14733 (2023) - 2022
- [j62]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): 373-395 (2022) - [j61]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): 552-570 (2022) - [j60]Badih Ghazi, Neel Kamal, Ravi Kumar, Pasin Manurangsi, Annika Zhang:
Private Aggregation of Trajectories. Proc. Priv. Enhancing Technol. 2022(4): 626-644 (2022) - [j59]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): 17- (2022) - [j58]Flavio Chierichetti, Anirban Dasgupta, Ravi Kumar:
On additive approximate submodularity. Theor. Comput. Sci. 922: 346-360 (2022) - [c233]Daniel Alabi, Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Private Rank Aggregation in Central and Local Models. AAAI 2022: 5984-5991 - [c232]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: 307-321 - [c231]Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, Pasin Manurangsi:
Large-Scale Differentially Private BERT. EMNLP (Findings) 2022: 6481-6491 - [c230]Matteo Almanza, Flavio Chierichetti, Ravi Kumar, Alessandro Panconesi, Andrew Tomkins:
RUMs from Head-to-Head Contests. ICML 2022: 452-467 - [c229]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Faster Privacy Accounting via Evolving Discretization. ICML 2022: 7470-7483 - [c228]Sungjin Im, Ravi Kumar, Aditya Petety, Manish Purohit:
Parsimonious Learning-Augmented Caching. ICML 2022: 9588-9601 - [c227]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Anonymized Histograms in Intermediate Privacy Models. NeurIPS 2022 - [c226]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Private Isotonic Regression. NeurIPS 2022 - [c225]Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi:
The Gibbs-Rand Model. PODS 2022: 151-163 - [c224]Nikhil Bansal, Christian Coester, Ravi Kumar, Manish Purohit, Erik Vee:
Learning-Augmented Weighted Paging. SODA 2022: 67-89 - [i53]Sungjin Im, Ravi Kumar, Aditya Petety, Manish Purohit:
Parsimonious Learning-Augmented Caching. CoRR abs/2202.04262 (2022) - [i52]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Jelani Nelson:
Differentially Private All-Pairs Shortest Path Distances: Improved Algorithms and Lower Bounds. CoRR abs/2203.16476 (2022) - [i51]Vadym Doroshenko, Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Connect the Dots: Tighter Discrete Approximations of Privacy Loss Distributions. CoRR abs/2207.04380 (2022) - [i50]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Faster Privacy Accounting via Evolving Discretization. CoRR abs/2207.04381 (2022) - [i49]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thomas Steinke:
Algorithms with More Granular Differential Privacy Guarantees. CoRR abs/2209.04053 (2022) - [i48]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Private Isotonic Regression. CoRR abs/2210.15175 (2022) - [i47]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi:
Anonymized Histograms in Intermediate Privacy Models. CoRR abs/2210.15178 (2022) - [i46]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Jelani Nelson:
Private Counting of Distinct and k-Occurring Items in Time Windows. CoRR abs/2211.11718 (2022) - [i45]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 abs/2211.11896 (2022) - [i44]Badih Ghazi, Junfeng He, Kai Kohlhoff, Ravi Kumar, Pasin Manurangsi, Vidhya Navalpakkam, Nachiappan Valliappan:
Differentially Private Heatmaps. CoRR abs/2211.13454 (2022) - [i43]Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Leeman, Pasin Manurangsi, Avinash Varadarajan, Chiyuan Zhang:
Regression with Label Differential Privacy. CoRR abs/2212.06074 (2022) - [i42]Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Kewen Wu:
On Differentially Private Counting on Trees. CoRR abs/2212.11967 (2022) - [i41]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: 920 (2022) - 2021
- [c223]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thao Nguyen:
Robust and Private Learning of Halfspaces. AISTATS 2021: 1603-1611 - [c222]Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit:
Power of Hints for Online Learning with Movement Costs. AISTATS 2021: 2818-2826 - [c221]Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi:
Near-tight closure b ounds for the Littlestone and threshold dimensions. ALT 2021: 686-696 - [c220]Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
On Avoiding the Union Bound When Answering Multiple Differentially Private Queries. COLT 2021: 2133-2146 - [c219]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: 463-488 - [c218]Alisa Chang, Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Locally Private k-Means in One Round. ICML 2021: 1441-1451 - [c217]Flavio Chierichetti, Ravi Kumar, Andrew Tomkins:
Light RUMs. ICML 2021: 1888-1897 - [c216]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: 3692-3701 - [c215]Lijie Chen, Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
On Distributed Differential Privacy and Counting Distinct Elements. ITCS 2021: 56:1-56:18 - [c214]Sungjin Im, Ravi Kumar, Mahshid Montazer Qaem, Manish Purohit:
Online Knapsack with Frequency Predictions. NeurIPS 2021: 2733-2743 - [c213]Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
User-Level Differentially Private Learning via Correlated Sampling. NeurIPS 2021: 20172-20184 - [c212]Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang:
Deep Learning with Label Differential Privacy. NeurIPS 2021: 27131-27145 - [c211]Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit:
Logarithmic Regret from Sublinear Hints. NeurIPS 2021: 28222-28232 - [c210]Sungjin Im, Ravi Kumar, Mahshid Montazer Qaem, Manish Purohit:
Non-Clairvoyant Scheduling with Predictions. SPAA 2021: 285-294 - [c209]Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi:
Sample-efficient proper PAC learning with approximate differential privacy. STOC 2021: 183-196 - [i40]Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang:
On Deep Learning with Label Differential Privacy. CoRR abs/2102.06062 (2021) - [i39]Alisa Chang, Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Locally Private k-Means in One Round. CoRR abs/2104.09734 (2021) - [i38]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh:
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead. CoRR abs/2106.04247 (2021) - [i37]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 abs/2107.01179 (2021) - [i36]Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, Pasin Manurangsi:
Large-Scale Differentially Private BERT. CoRR abs/2108.01624 (2021) - [i35]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 abs/2109.13158 (2021) - [i34]Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
User-Level Private Learning via Correlated Sampling. CoRR abs/2110.11208 (2021) - [i33]Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit:
Logarithmic Regret from Sublinear Hints. CoRR abs/2111.05257 (2021) - [i32]Daniel Alabi, Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Private Rank Aggregation in Central and Local Models. CoRR abs/2112.14652 (2021) - 2020
- [c208]Sara Ahmadian, Alessandro Epasto, Ravi Kumar, Mohammad Mahdian:
Fair Correlation Clustering. AISTATS 2020: 4195-4205 - [c207]Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Ameya Velingker:
Pure Differentially Private Summation from Anonymous Messages. ITC 2020: 15:1-15:23 - [c206]Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit:
Online Learning with Imperfect Hints. ICML 2020: 822-831 - [c205]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh:
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead. ICML 2020: 3505-3514 - [c204]Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang:
Fair Hierarchical Clustering. NeurIPS 2020 - [c203]Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit:
Online Linear Optimization with Many Hints. NeurIPS 2020 - [c202]Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Differentially Private Clustering: Tight Approximation Ratios. NeurIPS 2020 - [c201]Abhimanyu Das, Sreenivas Gollapudi, Ravi Kumar, Rina Panigrahy:
On the Learnability of Random Deep Networks. SODA 2020: 398-410 - [c200]Ravi Kumar, Manish Purohit, Zoya Svitkina, Erik Vee:
Interleaved Caching with Access Graphs. SODA 2020: 1846-1858 - [c199]Flavio Chierichetti, Ravi Kumar, Andrew Tomkins:
Asymptotic Behavior of Sequence Models. WWW 2020: 2824-2830 - [i31]Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Ameya Velingker:
Pure Differentially Private Summation from Anonymous Messages. CoRR abs/2002.01919 (2020) - [i30]Sara Ahmadian, Alessandro Epasto, Ravi Kumar, Mohammad Mahdian:
Fair Correlation Clustering. CoRR abs/2002.02274 (2020) - [i29]Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit:
Online Learning with Imperfect Hints. CoRR abs/2002.04726 (2020) - [i28]Andrei Z. Broder, Ravi Kumar:
A Note on Double Pooling Tests. CoRR abs/2004.01684 (2020) - [i27]Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang:
Fair Hierarchical Clustering. CoRR abs/2006.10221 (2020) - [i26]Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi:
Near-tight closure bounds for Littlestone and threshold dimensions. CoRR abs/2007.03668 (2020) - [i25]Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Differentially Private Clustering: Tight Approximation Ratios. CoRR abs/2008.08007 (2020) - [i24]Lijie Chen, Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
On Distributed Differential Privacy and Counting Distinct Elements. CoRR abs/2009.09604 (2020) - [i23]Flavio Chierichetti, Anirban Dasgupta, Ravi Kumar:
On Additive Approximate Submodularity. CoRR abs/2010.02912 (2020) - [i22]Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit:
Online Linear Optimization with Many Hints. CoRR abs/2010.03082 (2020) - [i21]Nikhil Bansal, Christian Coester, Ravi Kumar, Manish Purohit, Erik Vee:
Scale-Free Allocation, Amortized Convexity, and Myopic Weighted Paging. CoRR abs/2011.09076 (2020) - [i20]Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Thao Nguyen:
Robust and Private Learning of Halfspaces. CoRR abs/2011.14580 (2020) - [i19]Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi:
Sample-efficient proper PAC learning with approximate differential privacy. CoRR abs/2012.03893 (2020) - [i18]Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
On Avoiding the Union Bound When Answering Multiple Differentially Private Queries. CoRR abs/2012.09116 (2020)
2010 – 2019
- 2019
- [j57]Flavio Chierichetti, Ravi Kumar, Alessandro Panconesi, Erisa Terolli:
On the Distortion of Locality Sensitive Hashing. SIAM J. Comput. 48(2): 350-372 (2019) - [c198]Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvitskii:
Matroids, Matchings, and Fairness. AISTATS 2019: 2212-2220 - [c197]Maryam Aliakbarpour, Ravi Kumar, Ronitt Rubinfeld:
Testing Mixtures of Discrete Distributions. COLT 2019: 83-114 - [c196]Ravi Kumar, Rina Panigrahy, Ali Rahimi, David P. Woodruff:
Faster Algorithms for Binary Matrix Factorization. ICML 2019: 3551-3559 - [c195]Ravi Kumar, Manish Purohit, Aaron Schild, Zoya Svitkina, Erik Vee:
Semi-Online Bipartite Matching. ITCS 2019: 50:1-50:20 - [c194]Sara Ahmadian, Alessandro Epasto, Ravi Kumar, Mohammad Mahdian:
Clustering without Over-Representation. KDD 2019: 267-275 - [c193]Neha Arora, James Cook, Ravi Kumar, Ivan Kuznetsov, Yechen Li, Huai-Jen Liang, Andrew Miller, Andrew Tomkins, Iveel Tsogsuren, Yi Wang:
Hard to Park?: Estimating Parking Difficulty at Scale. KDD 2019: 2296-2304 - [c192]Ravi Kumar, Manish Purohit, Zoya Svitkina, Erik Vee, Joshua R. Wang:
Efficient Rematerialization for Deep Networks. NeurIPS 2019: 15146-15155 - [i17]Abhimanyu Das, Sreenivas Gollapudi, Ravi Kumar, Rina Panigrahy:
On the Learnability of Deep Random Networks. CoRR abs/1904.03866 (2019) - [i16]Sara Ahmadian, Alessandro Epasto, Ravi Kumar, Mohammad Mahdian:
Clustering without Over-Representation. CoRR abs/1905.12753 (2019) - [i15]Maryam Aliakbarpour, Ravi Kumar, Ronitt Rubinfeld:
Testing Mixtures of Discrete Distributions. CoRR abs/1907.03190 (2019) - [i14]Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, Ameya Velingker:
Private Heavy Hitters and Range Queries in the Shuffled Model. CoRR abs/1908.11358 (2019) - [i13]Benjamin Spector, Ravi Kumar, Andrew Tomkins:
Preventing Adversarial Use of Datasets through Fair Core-Set Construction. CoRR abs/1910.10871 (2019) - [i12]Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, Ameya Velingker:
On the Power of Multiple Anonymous Messages. IACR Cryptol. ePrint Arch. 2019: 1382 (2019) - 2018
- [j56]Marco Bressan, Flavio Chierichetti, Ravi Kumar, Stefano Leucci, Alessandro Panconesi:
Motif Counting Beyond Five Nodes. ACM Trans. Knowl. Discov. Data 12(4): 48:1-48:25 (2018) - [c191]Flavio Chierichetti, Ravi Kumar, Andrew Tomkins:
Learning a Mixture of Two Multinomial Logits. ICML 2018: 960-968 - [c190]Austin R. Benson, Ravi Kumar, Andrew Tomkins:
Sequences of Sets. KDD 2018: 1148-1157 - [c189]Flavio Chierichetti, Anirban Dasgupta, Shahrzad Haddadan, Ravi Kumar, Silvio Lattanzi:
Mallows Models for Top-k Lists. NeurIPS 2018: 4387-4397 - [c188]Manish Purohit, Zoya Svitkina, Ravi Kumar:
Improving Online Algorithms via ML Predictions. NeurIPS 2018: 9684-9693 - [c187]Flavio Chierichetti, Ravi Kumar, Andrew Tomkins:
Discrete Choice, Permutations, and Reconstruction. SODA 2018: 576-586 - [c186]Austin R. Benson, Ravi Kumar, Andrew Tomkins:
A Discrete Choice Model for Subset Selection. WSDM 2018: 37-45 - [r2]Ravi Kumar:
Web Page Quality Metrics. Encyclopedia of Database Systems (2nd ed.) 2018 - [i11]