Papers
Private Set Generation with Discriminative Information
Dingfan Chen, Raouf Kerkouche, Mario Fritz
Private Synthetic Data for Multitask Learning and Marginal Queries
Giuseppe Vietri, Cedric Archambeau, Sergul Aydore et al.
Probabilistic Missing Value Imputation for Mixed Categorical and Ordered Data
Yuxuan Zhao, Alex Townsend, Madeleine Udell
Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design
Jörg Franke, Frederic Runge, Frank Hutter
Probable Domain Generalization via Quantile Risk Minimization
Cian Eastwood, Alexander Robey, Shashank Singh et al.
Probing Classifiers are Unreliable for Concept Removal and Detection
Abhinav Kumar, Chenhao Tan, Amit Sharma
Procedural Image Programs for Representation Learning
Manel Baradad, Richard Chen, Jonas Wulff et al.
🏘️ ProcTHOR: Large-Scale Embodied AI Using Procedural Generation
Matt Deitke, Eli VanderBilt, Alvaro Herrasti et al.
Product Ranking for Revenue Maximization with Multiple Purchases
Renzhe Xu, Xingxuan Zhang, Bo Li et al.
projUNN: efficient method for training deep networks with unitary matrices
Bobak Kiani, Randall Balestriero, Yann LeCun et al.
Prompt Certified Machine Unlearning with Randomized Gradient Smoothing and Quantization
Zijie Zhang, Yang Zhou, Xin Zhao et al.
Proppo: a Message Passing Framework for Customizable and Composable Learning Algorithms
Paavo Parmas, Takuma Seno
PROSPECT: Labeled Tandem Mass Spectrometry Dataset for Machine Learning in Proteomics
Omar Shouman, Wassim Gabriel, Victor-George Giurcoiu et al.
Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection
Shizhen Zhao, Xiaojuan Qi
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model
Srishti Gautam, Ahcène Boubekki, Stine Hansen et al.
ProtoX: Explaining a Reinforcement Learning Agent via Prototyping
Ronilo Ragodos, Tong Wang, Qihang Lin et al.
Provable Benefit of Multitask Representation Learning in Reinforcement Learning
Yuan Cheng, Songtao Feng, Jing Yang et al.
Provable Defense against Backdoor Policies in Reinforcement Learning
Shubham Bharti, Xuezhou Zhang, Adish Singla et al.
Provable General Function Class Representation Learning in Multitask Bandits and MDP
Rui Lu, Andrew Zhao, Simon S Du et al.
Provable Generalization of Overparameterized Meta-learning Trained with SGD
Yu Huang, Yingbin Liang, Longbo Huang
Provable Subspace Identification Under Post-Nonlinear Mixtures
Qi Lyu, Xiao Fu
Provably Adversarially Robust Detection of Out-of-Distribution Data (Almost) for Free
Alexander Meinke, Julian Bitterwolf, Matthias Hein
Provably Efficient Model-Free Constrained RL with Linear Function Approximation
Arnob Ghosh, Xingyu Zhou, Ness Shroff
Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus
Qiwen Cui, Simon S Du