Papers
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig
Post-hoc estimators for learning to defer to an expert
Harikrishna Narasimhan, Wittawat Jitkrittum, Aditya K Menon et al.
Power and limitations of single-qubit native quantum neural networks
Zhan Yu, Hongshun Yao, Mujin Li et al.
Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models
Fan LIU, Hao Liu, Wenzhao Jiang
Practical Adversarial Multivalid Conformal Prediction
Osbert Bastani, Varun Gupta, Christopher Jung et al.
Pragmatically Learning from Pedagogical Demonstrations in Multi-Goal Environments
Hugo Caselles-Dupré, Olivier Sigaud, Mohamed CHETOUANI
Pre-activation Distributions Expose Backdoor Neurons
Runkai Zheng, Rongjun Tang, Jianze Li et al.
Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression
Lechao Xiao, Hong Hu, Theodor Misiakiewicz et al.
Precise Regret Bounds for Log-loss via a Truncated Bayesian Algorithm
Changlong Wu, Mohsen Heidari, Ananth Grama et al.
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution
Leon Hetzel, Simon Boehm, Niki Kilbertus et al.
Predicting Label Distribution from Multi-label Ranking
Yunan Lu, Xiuyi Jia
Predictive Coding beyond Gaussian Distributions
Luca Pinchetti, Tommaso Salvatori, Yordan Yordanov et al.
Predictive Querying for Autoregressive Neural Sequence Models
Alex Boyd, Samuel Showalter, Stephan Mandt et al.
Preservation of the Global Knowledge by Not-True Distillation in Federated Learning
Gihun Lee, Minchan Jeong, Yongjin Shin et al.
Pre-trained Adversarial Perturbations
Yuanhao Ban, Yinpeng Dong
Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning
Zhecheng Yuan, Zhengrong Xue, Bo Yuan et al.
Pre-Trained Language Models for Interactive Decision-Making
Shuang Li, Xavier Puig, Chris Paxton et al.
Pre-Trained Model Reusability Evaluation for Small-Data Transfer Learning
Yao-Xiang Ding, Xi-Zhu Wu, Kun Zhou et al.
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors
Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri et al.
Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation
Kristian Georgiev, Samuel Hopkins
Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss
Jason Altschuler, Kunal Talwar
Private and Communication-Efficient Algorithms for Entropy Estimation
Gecia Bravo-Hermsdorff, Róbert Busa-Fekete, Mohammad Ghavamzadeh et al.
Private Estimation with Public Data
Alex Bie, Gautam Kamath, Vikrant Singhal
Private Graph All-Pairwise-Shortest-Path Distance Release with Improved Error Rate
Chenglin Fan, Ping Li, Xiaoyun Li
Private Isotonic Regression
Badih Ghazi, Pritish Kamath, Ravi Kumar et al.