Papers
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation
Eli Chien, Jiong Zhang, Cho-Jui Hsieh et al.
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding
Kenton Lee, Mandar Joshi, Iulia Raluca Turc et al.
PixelAsParam: A Gradient View on Diffusion Sampling with Guidance
Anh-Dung Dinh, Daochang Liu, Chang Xu
PLay: Parametrically Conditioned Layout Generation using Latent Diffusion
Chin-Yi Cheng, Forrest Huang, Gang Li et al.
Poisoning Generative Replay in Continual Learning to Promote Forgetting
Siteng Kang, Zhan Shi, Xinhua Zhang
Poisoning Language Models During Instruction Tuning
Alexander Wan, Eric Wallace, Sheng Shen et al.
Polarity Is All You Need to Learn and Transfer Faster
Qingyang Wang, Michael Alan Powell, Eric W Bridgeford et al.
Policy Contrastive Imitation Learning
Jialei Huang, Zhao-Heng Yin, Yingdong Hu et al.
Policy Gradient in Robust MDPs with Global Convergence Guarantee
Qiuhao Wang, Chin Pang Ho, Marek Petrik
Policy Mirror Ascent for Efficient and Independent Learning in Mean Field Games
Batuhan Yardim, Semih Cayci, Matthieu Geist et al.
Policy Regularization with Dataset Constraint for Offline Reinforcement Learning
Yuhang Ran, Yi-Chen Li, Fuxiang Zhang et al.
Polynomial Preconditioning for Gradient Methods
Nikita Doikov, Anton Rodomanov
Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models
Jamil Arbas, Hassan Ashtiani, Christopher Liaw
Posterior Sampling for Deep Reinforcement Learning
Remo Sasso, Michelangelo Conserva, Paulo Rauber
POUF: Prompt-Oriented Unsupervised Fine-tuning for Large Pre-trained Models
Korawat Tanwisuth, Shujian Zhang, Huangjie Zheng et al.
PPG Reloaded: An Empirical Study on What Matters in Phasic Policy Gradient
Kaixin Wang, Daquan Zhou, Jiashi Feng et al.
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference
Kyurae Kim, Kaiwen Wu, Jisu Oh et al.
Pre-computed memory or on-the-fly encoding? A hybrid approach to retrieval augmentation makes the most of your compute
Michiel De Jong, Yury Zemlyanskiy, Nicholas Fitzgerald et al.
Predictable MDP Abstraction for Unsupervised Model-Based RL
Seohong Park, Sergey Levine
Predicting Ordinary Differential Equations with Transformers
Sören Becker, Michal Klein, Alexander Neitz et al.
Predicting Rare Events by Shrinking Towards Proportional Odds
Gregory Faletto, Jacob Bien
Predictive Flows for Faster Ford-Fulkerson
Sami Davies, Benjamin Moseley, Sergei Vassilvitskii et al.
Prefer to Classify: Improving Text Classifiers via Auxiliary Preference Learning
Jaehyung Kim, Jinwoo Shin, Dongyeop Kang
PreNAS: Preferred One-Shot Learning Towards Efficient Neural Architecture Search
Haibin Wang, Ce Ge, Hesen Chen et al.