Jennifer Gillenwater

Computer Science · Physical Sciences

12h-index1.2kcitations32works0.02yr avg
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Research Topics

Topic Modeling(8), Natural Language Processing Techniques(7), Privacy-Preserving Technologies in Data(7), Data Management and Algorithms(5), Stochastic Gradient Optimization Techniques(5)

Publications32 total

Baby Registry Dataset
TIB Data Manager·2024Open Access
Better Private Linear Regression Through Better Private Feature Selection
arXiv (Cornell University)·2023Open Access
A Joint Exponential Mechanism For Differentially Private Top-$k$
arXiv (Cornell University)·2022Open Access
Plume: Differential Privacy at Scale
arXiv (Cornell University)·2022· 3 citedOpen Access
Scalable Sampling for Nonsymmetric Determinantal Point Processes
arXiv (Cornell University)·2022Open Access
Combining Public and Private Data
arXiv (Cornell University)·2021Open Access
Subset Searching Algorithm Using DPP Greedy MAP [R package rDppDiversity version 0.0.2]
2021
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
International Conference on Learning Representations·2021· 9 cited
Differentially Private Quantiles
arXiv (Cornell University)·2021· 2 citedOpen Access
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms
arXiv (Cornell University)·2020· 38 citedOpen Access
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes
arXiv (Cornell University)·2020· 5 citedOpen Access
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point\n Processes
arXiv (Cornell University)·2020· 3 citedOpen Access
MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search.
International Conference on Artificial Intelligence and Statistics·2020· 6 cited
A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes
International Conference on Machine Learning·2019· 12 cited
Practical Diversified Recommendations on YouTube with Determinantal Point Processes
2018· 121 citedOpen Access
Maximizing Induced Cardinality Under a Determinantal Point Process
Neural Information Processing Systems·2018· 4 cited
Submodular hamming metrics
arXiv (Cornell University)·2015· 4 citedOpen Access
Submodular Hamming Metrics
arXiv (Cornell University)·2015· 8 citedOpen Access
Expectation-Maximization for Learning Determinantal Point Processes
arXiv (Cornell University)·2014· 36 citedOpen Access
Maximization of Non-Monotone Submodular Functions
ScholarlyCommons (University of Pennsylvania)·2014· 4 citedOpen Access
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Frequent Co-authors

Ben Taskar(9), Alex Kulesza(8), Kuzman Ganchev(5), Joäo Graça(4), In‐Su Han(4), Sergei Vassilvitskii(3), Mike Gartrell(3), Elvis Dohmatob(3), Matthew Joseph(3), Mark Wilhelm(2), Ajith Ramanathan(2), Alexander Bonomo(2), Sagar Jain(2), Ed H.(2), Fernando Pereira(2), Maruan Al-Shedivat(2), Eric P. Xing(2), Afshin Rostamizadeh(2), Gregory Malecha(2), Cherif Salama(2)