Gillenwater, Jennifer

13 publications

NeurIPS 2023 Better Private Linear Regression Through Better Private Feature Selection Travis Dick, Jennifer Gillenwater, Matthew Joseph

ICML 2022 A Joint Exponential Mechanism for Differentially Private Top-$k$ Jennifer Gillenwater, Matthew Joseph, Andres Munoz, Monica Ribero Diaz

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

NeurIPSW 2021 A Joint Exponential Mechanism for Differentially Private Top-K Set Andres Munoz Medina, Matthew Joseph, Jennifer Gillenwater, Mónica Ribero

NeurIPSW 2021 Combining Public and Private Data Cecilia Ferrando, Jennifer Gillenwater, Alex Kulesza

ICML 2021 Differentially Private Quantiles Jennifer Gillenwater, Matthew Joseph, Alex Kulesza

ICLR 2021 Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms Maruan Al-Shedivat, Jennifer Gillenwater, Eric 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

AISTATS 2020 MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search Insu Han, Jennifer Gillenwater

ICML 2019 A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes Jennifer Gillenwater, Alex Kulesza, Zelda Mariet, Sergei Vassilvtiskii

NeurIPS 2012 Near-Optimal MAP Inference for Determinantal Point Processes Jennifer Gillenwater, Alex Kulesza, Ben Taskar

JMLR 2011 Posterior Sparsity in Unsupervised Dependency Parsing Jennifer Gillenwater, Kuzman Ganchev, João Graça, Fernando Pereira, Ben Taskar

JMLR 2010 Posterior Regularization for Structured Latent Variable Models Kuzman Ganchev, João Graça, Jennifer Gillenwater, Ben Taskar