Papers
11,015 papers found
Post-hoc bias scoring is optimal for fair classification
Wenlong Chen, Yegor Klochkov, Yang Liu
Predicting Emergent Abilities with Infinite Resolution Evaluation
Shengding Hu, Xin Liu, Xu Han et al.
Prediction Error-based Classification for Class-Incremental Learning
Michał Zając, Tinne Tuytelaars, Gido M van de Ven
Prediction without Preclusion: Recourse Verification with Reachable Sets
Avni Kothari, Bogdan Kulynych, Tsui-Wei Weng et al.
Predictive auxiliary objectives in deep RL mimic learning in the brain
Ching Fang, Kim Stachenfeld
Predictive, scalable and interpretable knowledge tracing on structured domains
Hanqi Zhou, Robert Bamler, Charley M Wu et al.
PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks
Junwei Su, Difan Zou, Chuan Wu
Pre-Training and Fine-Tuning Generative Flow Networks
Ling Pan, Moksh Jain, Kanika Madan et al.
Pre-Training Goal-based Models for Sample-Efficient Reinforcement Learning
Haoqi Yuan, Zhancun Mu, Feiyang Xie et al.
Pre-training LiDAR-based 3D Object Detectors through Colorization
Tai-Yu Pan, Chenyang Ma, Tianle Chen et al.
Pre-training Sequence, Structure, and Surface Features for Comprehensive Protein Representation Learning
Youhan Lee, Hasun Yu, Jaemyung Lee et al.
Pre-training with Random Orthogonal Projection Image Modeling
Maryam Haghighat, Peyman Moghadam, Shaheer Mohamed et al.
Pre-training with Synthetic Data Helps Offline Reinforcement Learning
Zecheng Wang, Che Wang, Zixuan Dong et al.
PRIME: Prioritizing Interpretability in Failure Mode Extraction
Keivan Rezaei, Mehrdad Saberi, Mazda Moayeri et al.
Principled Architecture-aware Scaling of Hyperparameters
Wuyang Chen, Junru Wu, Zhangyang Wang et al.
Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting
Enyi Jiang, Yibo Jacky Zhang, Sanmi Koyejo
Prioritized Soft Q-Decomposition for Lexicographic Reinforcement Learning
Finn Rietz, Erik Schaffernicht, Stefan Heinrich et al.
Privacy Amplification for Matrix Mechanisms
Christopher A. Choquette-Choo, Arun Ganesh, Thomas Steinke et al.
Privacy-Preserving In-Context Learning for Large Language Models
Tong Wu, Ashwinee Panda, Jiachen T. Wang et al.
Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
Xinyu Tang, Richard Shin, Huseyin A Inan et al.
Privately Aligning Language Models with Reinforcement Learning
Fan Wu, Huseyin A Inan, Arturs Backurs et al.
Private Zeroth-Order Nonsmooth Nonconvex Optimization
Qinzi Zhang, Hoang Tran, Ashok Cutkosky
Privileged Sensing Scaffolds Reinforcement Learning
Edward S. Hu, James Springer, Oleh Rybkin et al.
Probabilistic Adaptation of Black-Box Text-to-Video Models
Sherry Yang, Yilun Du, Bo Dai et al.
Probabilistically Rewired Message-Passing Neural Networks
Chendi Qian, Andrei Manolache, Kareem Ahmed et al.