Synthesizable high level hardware descriptions: using statically typed two-level languages to guarantee verilog synthesizability. - Jennifer Gillenwater; Gregory Malecha; Cherif R. Salama; Angela Yun Zhu; Walid Taha; Jim Grundy; John O'Leary (PEPM, 2008)

Dependency Grammar Induction via Bitext Projection Constraints. - Kuzman Ganchev; Jennifer Gillenwater; Ben Taskar (ACL/IJCNLP, 2009)

Sparsity in Dependency Grammar Induction. - Jennifer Gillenwater; Kuzman Ganchev; João Graça; Fernando Pereira 003; Ben Taskar (ACL (2), 2010)

Posterior Regularization for Structured Latent Variable Models. - Kuzman Ganchev; João Graça; Jennifer Gillenwater; Ben Taskar (J. Mach. Learn. Res., 2010)

Synthesizable High Level Hardware Descriptions. - Jennifer Gillenwater; Gregory Malecha; Cherif R. Salama; Angela Yun Zhu; Walid Taha; Jim Grundy; John O'Leary (New Gener. Comput., 2010)

Posterior Sparsity in Unsupervised Dependency Parsing. - Jennifer Gillenwater; Kuzman Ganchev; João Graça; Fernando Pereira 003; Ben Taskar (J. Mach. Learn. Res., 2011)

Discovering Diverse and Salient Threads in Document Collections. - Jennifer Gillenwater; Alex Kulesza; Ben Taskar (EMNLP-CoNLL, 2012)

Near-Optimal MAP Inference for Determinantal Point Processes. - Jennifer Gillenwater; Alex Kulesza; Ben Taskar (NIPS, 2012)

Graph-Based Posterior Regularization for Semi-Supervised Structured Prediction. - Luheng He; Jennifer Gillenwater; Ben Taskar (CoNLL, 2013)

End-to-end learning of parsing models for information retrieval. - Jennifer Gillenwater; Xiaodong He 001; Jianfeng Gao 001; Li Deng 001 (ICASSP, 2013)

Expectation-Maximization for Learning Determinantal Point Processes. - Jennifer Gillenwater; Alex Kulesza; Emily B. Fox; Benjamin Taskar (NIPS, 2014)

Submodular Hamming Metrics. - Jennifer Gillenwater; Rishabh K. Iyer; Bethany Lusch; Rahul Kidambi; Jeff A. Bilmes (NIPS, 2015)

Practical Diversified Recommendations on YouTube with Determinantal Point Processes. - Mark Wilhelm; Ajith Ramanathan; Alexander Bonomo; Sagar Jain; Ed H. Chi; Jennifer Gillenwater (CIKM, 2018)

Maximizing Induced Cardinality Under a Determinantal Point Process. - Jennifer A. Gillenwater; Alex Kulesza; Sergei Vassilvitskii; Zelda E. Mariet (NeurIPS, 2018)

A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes. - Jennifer Gillenwater; Alex Kulesza; Zelda Mariet; Sergei Vassilvitskii (ICML, 2019)

MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search. - Insu Han; Jennifer Gillenwater (AISTATS, 2020)

Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms. - Maruan Al-Shedivat; Jennifer Gillenwater; Eric P. Xing; Afshin Rostamizadeh (ICLR, 2021)

Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes. - Mike Gartrell; Insu Han; Elvis Dohmatob; Jennifer Gillenwater; Victor-Emmanuel Brunel (ICLR, 2021)

Differentially Private Quantiles. - Jennifer Gillenwater; Matthew Joseph; Alex Kulesza (ICML, 2021)

Combining Public and Private Data. - Cecilia Ferrando; Jennifer Gillenwater; Alex Kulesza (CoRR, 2021)

Scalable Sampling for Nonsymmetric Determinantal Point Processes. - Insu Han; Mike Gartrell; Jennifer Gillenwater; Elvis Dohmatob; Amin Karbasi (ICLR, 2022)

A Joint Exponential Mechanism For Differentially Private Top-k. - Jennifer Gillenwater; Matthew Joseph; Andres Muñoz Medina; Mónica Ribero Diaz (ICML, 2022)

Plume: Differential Privacy at Scale. - Kareem Amin 002; Jennifer Gillenwater; Matthew Joseph; Alex Kulesza; Sergei Vassilvitskii (CoRR, 2022)

Better Private Linear Regression Through Better Private Feature Selection. - Travis Dick; Jennifer Gillenwater; Matthew Joseph (NeurIPS, 2023)