Papers
Principled Bayesian Optimization in Collaboration with Human Experts
Wenjie Xu, Masaki Adachi, Colin N. Jones et al.
Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors
Zihui Wu, Yu Sun, Yifan Chen et al.
Prior-itizing Privacy: A Bayesian Approach to Setting the Privacy Budget in Differential Privacy
Zeki Kazan, Jerome P. Reiter
Prism: A Framework for Decoupling and Assessing the Capabilities of VLMs
Yuxuan Qiao, Haodong Duan, Xinyu Fang et al.
Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models
Yuxin Wen, Leo Marchyok, Sanghyun Hong et al.
PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action
Yijia Shao, Tianshi Li, Weiyan Shi et al.
Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training
Kristjan Greenewald, Yuancheng Yu, Hao Wang et al.
Private Algorithms for Stochastic Saddle Points and Variational Inequalities: Beyond Euclidean Geometry
Raef Bassily, Cristóbal Guzmán, Michael Menart
Private and Personalized Frequency Estimation in a Federated Setting
Amrith Setlur, Vitaly Feldman, Kunal Talwar
Private Attribute Inference from Images with Vision-Language Models
Batuhan Tömekçe, Mark Vero, Robin Staab et al.
Private Edge Density Estimation for Random Graphs: Optimal, Efficient and Robust
Hongjie Chen, Jingqiu Ding, Yiding Hua et al.
Private Geometric Median
Mahdi Haghifam, Thomas Steinke, Jonathan Ullman
Private Online Learning via Lazy Algorithms
Hilal Asi, Tomer Koren, Daogao Liu et al.
Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions
Hilal Asi, Daogao Liu, Kevin Tian
PrivAuditor: Benchmarking Data Protection Vulnerabilities in LLM Adaptation Techniques
Derui Zhu, Dingfan Chen, Xiongfei Wu et al.
PrivCirNet: Efficient Private Inference via Block Circulant Transformation
Tianshi Xu, Lemeng Wu, Runsheng Wang et al.
Probabilistic Conformal Distillation for Enhancing Missing Modality Robustness
Mengxi Chen, Fei Zhang, Zihua Zhao et al.
Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics
Yenho Chen, Noga Mudrik, Kyle A. Johnsen et al.
Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data
Pei-Yau Weng, Minh Hoang, Lam M. Nguyen et al.
Probabilistic Graph Rewiring via Virtual Nodes
Chendi Qian, Andrei Manolache, Christopher Morris et al.
Probabilistic size-and-shape functional mixed models
Fangyi Wang, Karthik Bharath, Oksana Chkrebtii et al.
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks
Joel Oskarsson, Tomas Landelius, Marc Peter Deisenroth et al.
Probablistic Emulation of a Global Climate Model with Spherical DYffusion
Salva Rühling Cachay, Brian Henn, Oliver Watt-Meyer et al.
Probing Social Bias in Labor Market Text Generation by ChatGPT: A Masked Language Model Approach
Lei Ding, Yang Hu, Nicole Denier et al.
Probing the Decision Boundaries of In-context Learning in Large Language Models
Siyan Zhao, Tung Nguyen, Aditya Grover