Dy, Jennifer

37 publications

TMLR 2025 ADAPT to Robustify Prompt Tuning Vision Transformers Masih Eskandar, Tooba Imtiaz, Zifeng Wang, Jennifer Dy

AISTATS 2025 Axiomatic Explainer Globalness via Optimal Transport Davin Hill, Joshua Bone, Aria Masoomi, Max Torop, Jennifer Dy

NeurIPS 2025 DISCO: Disentangled Communication Steering for Large Language Models Max Torop, Aria Masoomi, Masih Eskandar, Jennifer Dy

TMLR 2025 Dependency-Aware Maximum Likelihood Estimation for Active Learning Beyza Kalkanli, Tales Imbiriba, Stratis Ioannidis, Deniz Erdogmus, Jennifer Dy

NeurIPS 2025 H-SPLID: HSIC-Based Saliency Preserving Latent Information Decomposition Lukas Miklautz, Chengzhi Shi, Andrii Shkabrii, Theodoros Thirimachos Davarakis, Prudence Lam, Claudia Plant, Jennifer Dy, Stratis Ioannidis

NeurIPS 2025 OrdShap: Feature Position Importance for Sequential Black-Box Models Davin Hill, Brian L. Hill, Aria Masoomi, Vijay S Nori, Robert E. Tillman, Jennifer Dy

TMLR 2025 SAIF: Sparse Adversarial and Imperceptible Attack Framework Tooba Imtiaz, Morgan R Kohler, Jared F Miller, Zifeng Wang, Masih Eskandar, Mario Sznaier, Octavia Camps, Jennifer Dy

ICLR 2025 STAR: Stability-Inducing Weight Perturbation for Continual Learning Masih Eskandar, Tooba Imtiaz, Davin Hill, Zifeng Wang, Jennifer Dy

AISTATS 2024 Analyzing Explainer Robustness via Probabilistic Lipschitzness of Prediction Functions Zulqarnain Q. Khan, Davin Hill, Aria Masoomi, Joshua T. Bone, Jennifer Dy

AISTATS 2024 Boundary-Aware Uncertainty for Feature Attribution Explainers Davin Hill, Aria Masoomi, Max Torop, Sandesh Ghimire, Jennifer Dy

ICML 2023 DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning Zifeng Wang, Zheng Zhan, Yifan Gong, Yucai Shao, Stratis Ioannidis, Yanzhi Wang, Jennifer Dy

NeurIPS 2023 SmoothHess: ReLU Network Feature Interactions via Stein's Lemma Max Torop, Aria Masoomi, Davin Hill, Kivanc Kose, Stratis Ioannidis, Jennifer Dy

AISTATS 2022 Deep Layer-Wise Networks Have Closed-Form Weights Chieh Tzu Wu, Aria Masoomi, Arthur Gretton, Jennifer Dy

ECCV 2022 DualPrompt: Complementary Prompting for Rehearsal-Free Continual Learning Zifeng Wang, Zizhao Zhang, Sayna Ebrahimi, Ruoxi Sun, Han Zhang, Chen-Yu Lee, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, Tomas Pfister

ICLR 2022 Explanations of Black-Box Models Based on Directional Feature Interactions Aria Masoomi, Davin Hill, Zhonghui Xu, Craig P Hersh, Edwin K. Silverman, Peter J. Castaldi, Stratis Ioannidis, Jennifer Dy

CVPR 2022 Learning to Prompt for Continual Learning Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, Tomas Pfister

NeurIPS 2022 SparCL: Sparse Continual Learning on the Edge Zifeng Wang, Zheng Zhan, Yifan Gong, Geng Yuan, Wei Niu, Tong Jian, Bin Ren, Stratis Ioannidis, Yanzhi Wang, Jennifer Dy

AISTATS 2021 Deep Spectral Ranking Ilkay Yildiz, Jennifer Dy, Deniz Erdogmus, Susan Ostmo, J. Peter Campbell, Michael F. Chiang, Stratis Ioannidis

AISTATS 2021 Faster & More Reliable Tuning of Neural Networks: Bayesian Optimization with Importance Sampling Setareh Ariafar, Zelda Mariet, Dana Brooks, Jennifer Dy, Jasper Snoek

AISTATS 2021 Rate-Regularization and Generalization in Variational Autoencoders Alican Bozkurt, Babak Esmaeili, Jean-Baptiste Tristan, Dana Brooks, Jennifer Dy, Jan-Willem van de Meent

NeurIPS 2021 Reliable Estimation of KL Divergence Using a Discriminator in Reproducing Kernel Hilbert Space Sandesh Ghimire, Aria Masoomi, Jennifer Dy

NeurIPS 2021 Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness Zifeng Wang, Tong Jian, Aria Masoomi, Stratis Ioannidis, Jennifer Dy

AISTATS 2020 Fast and Accurate Ranking Regression Ilkay Yildiz, Jennifer Dy, Deniz Erdogmus, Jayashree Kalpathy-Cramer, Susan Ostmo, J. Peter Campbell, Michael F. Chiang, Stratis Ioannidis

NeurIPS 2020 Instance-Wise Feature Grouping Aria Masoomi, Chieh Wu, Tingting Zhao, Zifeng Wang, Peter Castaldi, Jennifer Dy

NeurIPS 2020 Neural Topographic Factor Analysis for fMRI Data Eli Sennesh, Zulqarnain Khan, Yiyu Wang, J Benjamin Hutchinson, Ajay Satpute, Jennifer Dy, Jan-Willem van de Meent

JMLR 2019 ADMMBO: Bayesian Optimization with Unknown Constraints Using ADMM Setareh Ariafar, Jaume Coll-Font, Dana Brooks, Jennifer Dy

NeurIPS 2019 Solving Interpretable Kernel Dimensionality Reduction Chieh Wu, Jared Miller, Yale Chang, Mario Sznaier, Jennifer Dy

AISTATS 2019 Structured Disentangled Representations Babak Esmaeili, Hao Wu, Sarthak Jain, Alican Bozkurt, N Siddharth, Brooks Paige, Dana H. Brooks, Jennifer Dy, Jan-Willem Meent

ACML 2019 Variational Inference from Ranked Samples with Features Yuan Guo, Jennifer Dy, Deniz Erdoğmuş, Jayashree Kalpathy-Cramer, Susan Ostmo, J. Peter Campbell, Michael F. Chiang, Stratis Ioannidis

ACML 2017 Rate Optimal Estimation for High Dimensional Spatial Covariance Matrices Yi Li, Aidong Adam Ding, Jennifer Dy

MLHC 2016 Multi-Task Learning with Weak Class Labels: Leveraging iEEG to Detect Cortical Lesions in Cryptogenic Epilepsy Bilal Ahmed, Thomas Thesen, Karen Blackmon, Ruben Kuzniecky, Orrin Devinsky, Jennifer Dy, Carla Brodley

ICML 2013 Nonparametric Mixture of Gaussian Processes with Constraints James Ross, Jennifer Dy

AISTATS 2012 A Nonparametric Bayesian Model for Multiple Clustering with Overlapping Feature Views Donglin Niu, Jennifer Dy, Zoubin Ghahramani

AISTATS 2012 Active Learning from Multiple Knowledge Sources Yan Yan, Romer Rosales, Glenn Fung, Faisal Farooq, Bharat Rao, Jennifer Dy

AISTATS 2011 Dimensionality Reduction for Spectral Clustering Donglin Niu, Jennifer Dy, Michael I. Jordan

AISTATS 2010 Modeling Annotator Expertise: Learning When Everybody Knows a Bit of Something Yan Yan, Romer Rosales, Glenn Fung, Mark Schmidt, Gerardo Hermosillo, Luca Bogoni, Linda Moy, Jennifer Dy

AISTATS 2009 Sparse Probabilistic Principal Component Analysis Yue Guan, Jennifer Dy