Papers
Predicting Future Actions of Reinforcement Learning Agents
Stephen Chung, Scott Niekum, David Krueger
Predicting Ground State Properties: Constant Sample Complexity and Deep Learning Algorithms
Marc Wanner, Laura Lewis, Chiranjib Bhattacharyya et al.
Predicting Label Distribution from Ternary Labels
Yunan Lu, Xiuyi Jia
Predicting the Performance of Foundation Models via Agreement-on-the-Line
Rahul Saxena, Taeyoun Kim, Aman Mehra et al.
Prediction-Powered Ranking of Large Language Models
Ivi Chatzi, Eleni Straitouri, Suhas Thejaswi et al.
Prediction with Action: Visual Policy Learning via Joint Denoising Process
Yanjiang Guo, Yucheng Hu, Jianke Zhang et al.
Predictive Attractor Models
Ramy Mounir, Sudeep Sarkar
Predictor-Corrector Enhanced Transformers with Exponential Moving Average Coefficient Learning
Bei Li, Tong Zheng, Rui Wang et al.
Preference Alignment with Flow Matching
Minu Kim, Yongsik Lee, Sehyeok Kang et al.
Preference-based Pure Exploration
Apurv Shukla, Debabrota Basu
Preference Learning Algorithms Do Not Learn Preference Rankings
Angelica Chen, Sadhika Malladi, Lily H. Zhang et al.
Preference Learning of Latent Decision Utilities with a Human-like Model of Preferential Choice
Sebastiaan De Peuter, Shibei Zhu, Yujia Guo et al.
Preferential Normalizing Flows
Petrus Mikkola, Luigi Acerbi, Arto Klami
PrefPaint: Aligning Image Inpainting Diffusion Model with Human Preference
Kendong Liu, Zhiyu Zhu, Chuanhao Li et al.
Pre-trained Large Language Models Use Fourier Features to Compute Addition
Tianyi Zhou, Deqing Fu, Vatsal Sharan et al.
Pre-Trained Multi-Goal Transformers with Prompt Optimization for Efficient Online Adaptation
Haoqi Yuan, Yuhui Fu, Feiyang Xie et al.
Pretrained Optimization Model for Zero-Shot Black Box Optimization
Xiaobin Li, Kai Wu, Yujian Betterrest Li et al.
Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control
Gunshi Gupta, Karmesh Yadav, Yarin Gal et al.
Pretrained Transformer Efficiently Learns Low-Dimensional Target Functions In-Context
Kazusato Oko, Yujin Song, Taiji Suzuki et al.
Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs
Md Ashiqur Rahman, Robert Joseph George, Mogab Elleithy et al.
Pre-training Differentially Private Models with Limited Public Data
Zhiqi Bu, Xinwei Zhang, Sheng Zha et al.
Pretraining with Random Noise for Fast and Robust Learning without Weight Transport
Jeonghwan Cheon, Sang Wan Lee, Se-Bum Paik
Preventing Dimensional Collapse in Self-Supervised Learning via Orthogonality Regularization
Junlin He, Jinxiao Du, Wei Ma
Preventing Model Collapse in Deep Canonical Correlation Analysis by Noise Regularization
Junlin He, Jinxiao Du, Susu Xu et al.
Pricing and Competition for Generative AI
Rafid Mahmood