Papers
8,340 papers found
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.
Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems
Chawin Sitawarin, Florian Tramèr, Nicholas Carlini
Pre-training for Speech Translation: CTC Meets Optimal Transport
Phuong-Hang Le, Hongyu Gong, Changhan Wang et al.
Pretraining Language Models with Human Preferences
Tomasz Korbak, Kejian Shi, Angelica Chen et al.
Pricing Experimental Design: Causal Effect, Expected Revenue and Tail Risk
David Simchi-Levi, Chonghuan Wang
Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems
Atsushi Nitanda, Kazusato Oko, Denny Wu et al.
Principled Acceleration of Iterative Numerical Methods Using Machine Learning
Sohei Arisaka, Qianxiao Li
Principled Offline RL in the Presence of Rich Exogenous Information
Riashat Islam, Manan Tomar, Alex Lamb et al.
Principled Reinforcement Learning with Human Feedback from Pairwise or K-wise Comparisons
Banghua Zhu, Michael Jordan, Jiantao Jiao
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design
Chuan Guo, Kamalika Chaudhuri, Pierre Stock et al.
Private Federated Learning with Autotuned Compression
Enayat Ullah, Christopher A. Choquette-Choo, Peter Kairouz et al.
Private Statistical Estimation of Many Quantiles
Clément Lalanne, Aurélien Garivier, Rémi Gribonval
Probabilistic Attention-to-Influence Neural Models for Event Sequences
Xiao Shou, Debarun Bhattacharjya, Tian Gao et al.
Probabilistic Categorical Adversarial Attack and Adversarial Training
Han Xu, Pengfei He, Jie Ren et al.
Probabilistic Concept Bottleneck Models
Eunji Kim, Dahuin Jung, Sangha Park et al.
Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs
Michael Kirchhof, Enkelejda Kasneci, Seong Joon Oh