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ORCIDJennifer Gillenwater
Person information
- affiliation: University of Washington, Department of Electrical Engineering
- affiliation: University of Pennsylvania, Computer and Information Science
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2020 – today
- 2023
[c19]Travis Dick, Jennifer Gillenwater, Matthew Joseph:
Better Private Linear Regression Through Better Private Feature Selection. NeurIPS 2023- [i10]Travis Dick, Jennifer Gillenwater, Matthew Joseph:
Better Private Linear Regression Through Better Private Feature Selection. CoRR abs/2306.00920 (2023) - 2022
- [c18]Insu Han, Mike Gartrell, Jennifer Gillenwater, Elvis Dohmatob, Amin Karbasi:
Scalable Sampling for Nonsymmetric Determinantal Point Processes. ICLR 2022 - [c17]Jennifer Gillenwater, Matthew Joseph, Andres Muñoz Medina, Mónica Ribero Diaz:
A Joint Exponential Mechanism For Differentially Private Top-k. ICML 2022: 7570-7582 - [i9]Insu Han, Mike Gartrell, Jennifer Gillenwater, Elvis Dohmatob, Amin Karbasi:
Scalable Sampling for Nonsymmetric Determinantal Point Processes. CoRR abs/2201.08417 (2022) - [i8]Kareem Amin, Jennifer Gillenwater, Matthew Joseph, Alex Kulesza, Sergei Vassilvitskii:
Plume: Differential Privacy at Scale. CoRR abs/2201.11603 (2022) - [i7]Jennifer Gillenwater, Matthew Joseph, Andrés Muñoz Medina, Mónica Ribero:
A Joint Exponential Mechanism For Differentially Private Top-k. CoRR abs/2201.12333 (2022) - 2021
- [c16]Maruan Al-Shedivat, Jennifer Gillenwater, Eric P. Xing, Afshin Rostamizadeh:
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms. ICLR 2021 - [c15]Mike Gartrell, Insu Han, Elvis Dohmatob, Jennifer Gillenwater, Victor-Emmanuel Brunel:
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes. ICLR 2021 - [c14]Jennifer Gillenwater, Matthew Joseph, Alex Kulesza:
Differentially Private Quantiles. ICML 2021: 3713-3722 - [i6]Jennifer Gillenwater, Matthew Joseph, Alex Kulesza:
Differentially Private Quantiles. CoRR abs/2102.08244 (2021) - [i5]Cecilia Ferrando, Jennifer Gillenwater, Alex Kulesza:
Combining Public and Private Data. CoRR abs/2111.00115 (2021) - 2020
- [c13]Insu Han, Jennifer Gillenwater:
MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search. AISTATS 2020: 2797-2807 - [i4]Mike Gartrell, Insu Han, Elvis Dohmatob, Jennifer Gillenwater, Victor-Emmanuel Brunel:
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes. CoRR abs/2006.09862 (2020) - [i3]Maruan Al-Shedivat, Jennifer Gillenwater, Eric P. Xing, Afshin Rostamizadeh:
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms. CoRR abs/2010.05273 (2020)
2010 – 2019
- 2019
- [c12]Jennifer Gillenwater, Alex Kulesza, Zelda Mariet, Sergei Vassilvitskii:
A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes. ICML 2019: 2260-2268 - 2018
- [c11]Mark Wilhelm, Ajith Ramanathan, Alexander Bonomo, Sagar Jain, Ed H. Chi, Jennifer Gillenwater:
Practical Diversified Recommendations on YouTube with Determinantal Point Processes. CIKM 2018: 2165-2173 - [c10]Jennifer A. Gillenwater, Alex Kulesza, Sergei Vassilvitskii, Zelda E. Mariet:
Maximizing Induced Cardinality Under a Determinantal Point Process. NeurIPS 2018: 6911-6920 - 2015
- [c9]Jennifer Gillenwater, Rishabh K. Iyer, Bethany Lusch, Rahul Kidambi, Jeff A. Bilmes:
Submodular Hamming Metrics. NIPS 2015: 3141-3149 - [i2]Jennifer Gillenwater, Rishabh K. Iyer, Bethany Lusch, Rahul Kidambi, Jeff A. Bilmes:
Submodular Hamming Metrics. CoRR abs/1511.02163 (2015) - 2014
- [c8]Jennifer Gillenwater, Alex Kulesza, Emily B. Fox, Benjamin Taskar:
Expectation-Maximization for Learning Determinantal Point Processes. NIPS 2014: 3149-3157 - [i1]Jennifer Gillenwater, Alex Kulesza, Emily B. Fox, Ben Taskar:
Expectation-Maximization for Learning Determinantal Point Processes. CoRR abs/1411.1088 (2014) - 2013
- [c7]Luheng He, Jennifer Gillenwater, Ben Taskar:
Graph-Based Posterior Regularization for Semi-Supervised Structured Prediction. CoNLL 2013: 38-46 - [c6]Jennifer Gillenwater, Xiaodong He, Jianfeng Gao, Li Deng:
End-to-end learning of parsing models for information retrieval. ICASSP 2013: 3312-3316 - 2012
- [c5]Jennifer Gillenwater, Alex Kulesza, Ben Taskar:
Discovering Diverse and Salient Threads in Document Collections. EMNLP-CoNLL 2012: 710-720 - [c4]Jennifer Gillenwater, Alex Kulesza, Ben Taskar:
Near-Optimal MAP Inference for Determinantal Point Processes. NIPS 2012: 2744-2752 - 2011
- [j3]Jennifer Gillenwater, Kuzman Ganchev, João Graça, Fernando Pereira, Ben Taskar:
Posterior Sparsity in Unsupervised Dependency Parsing. J. Mach. Learn. Res. 12: 455-490 (2011) - 2010
- [j2]Kuzman Ganchev, João Graça, Jennifer Gillenwater, Ben Taskar:
Posterior Regularization for Structured Latent Variable Models. J. Mach. Learn. Res. 11: 2001-2049 (2010) - [j1]Jennifer Gillenwater, Gregory Malecha
, Cherif R. Salama, Angela Yun Zhu, Walid Taha, Jim Grundy, John O'Leary:
Synthesizable High Level Hardware Descriptions. New Gener. Comput. 28(4): 339-369 (2010) - [c3]Jennifer Gillenwater, Kuzman Ganchev, João Graça, Fernando Pereira, Ben Taskar:
Sparsity in Dependency Grammar Induction. ACL (2) 2010: 194-199
2000 – 2009
- 2009
- [c2]Kuzman Ganchev, Jennifer Gillenwater, Ben Taskar:
Dependency Grammar Induction via Bitext Projection Constraints. ACL/IJCNLP 2009: 369-377 - 2008
- [c1]Jennifer Gillenwater, Gregory Malecha, Cherif R. Salama, Angela Yun Zhu, Walid Taha, Jim Grundy, John O'Leary:
Synthesizable high level hardware descriptions: using statically typed two-level languages to guarantee verilog synthesizability. PEPM 2008: 41-50
Coauthor Index
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