Papers
4,122 papers found
PAMI: An Open-Source Python Library for Pattern Mining
Uday Kiran Rage, Veena Pamalla, Masashi Toyoda et al.
PAPAL: A Provable PArticle-based Primal-Dual ALgorithm for Mixed Nash Equilibrium
Shihong Ding, Hanze Dong, Cong Fang et al.
Parallel-in-Time Probabilistic Numerical ODE Solvers
Nathanael Bosch, Adrien Corenflos, Fatemeh Yaghoobi et al.
Pareto Smoothed Importance Sampling
Aki Vehtari, Daniel Simpson, Andrew Gelman et al.
Pearl: A Production-Ready Reinforcement Learning Agent
Zheqing Zhu, Rodrigo de Salvo Braz, Jalaj Bhandari et al.
Penalized Overdamped and Underdamped Langevin Monte Carlo Algorithms for Constrained Sampling
Mert Gurbuzbalaban, Yuanhan Hu, Lingjiong Zhu
Permuted and Unlinked Monotone Regression in R^d: an approach based on mixture modeling and optimal transport
Martin Slawski, Bodhisattva Sen
Personalized PCA: Decoupling Shared and Unique Features
Naichen Shi, Raed Al Kontar
PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX
Guangyao Zhou, Antoine Dedieu, Nishanth Kumar et al.
pgmpy: A Python Toolkit for Bayesian Networks
Ankur Ankan, Johannes Textor
PhAST: Physics-Aware, Scalable, and Task-Specific GNNs for Accelerated Catalyst Design
Alexandre Duval, Victor Schmidt, Santiago Miret et al.
PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks
Sifan Wang, Bowen Li, Yuhan Chen et al.
Policy Gradient Methods in the Presence of Symmetries and State Abstractions
Prakash Panangaden, Sahand Rezaei-Shoshtari, Rosie Zhao et al.
Polygonal Unadjusted Langevin Algorithms: Creating stable and efficient adaptive algorithms for neural networks
Dong-Young Lim, Sotirios Sabanis
Post-Regularization Confidence Bands for Ordinary Differential Equations
Xiaowu Dai, Lexin Li
Power of knockoff: The impact of ranking algorithm, augmented design, and symmetric statistic
Zheng Tracy Ke, Jun S. Liu, Yucong Ma
Predictive Inference with Weak Supervision
Maxime Cauchois, Suyash Gupta, Alnur Ali et al.
Pre-trained Gaussian Processes for Bayesian Optimization
Zi Wang, George E. Dahl, Kevin Swersky et al.
Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization
Lorenzo Pacchiardi, Rilwan A. Adewoyin, Peter Dueben et al.
PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates
Zachary Frangella, Pratik Rathore, Shipu Zhao et al.
PromptBench: A Unified Library for Evaluation of Large Language Models
Kaijie Zhu, Qinlin Zhao, Hao Chen et al.
ptwt - The PyTorch Wavelet Toolbox
Moritz Wolter, Felix Blanke, Jochen Garcke et al.
Pure Differential Privacy for Functional Summaries with a Laplace-like Process
Haotian Lin, Matthew Reimherr
Pursuit of the Cluster Structure of Network Lasso: Recovery Condition and Non-convex Extension
Shotaro Yagishita, Jun-ya Gotoh
PyDMD: A Python Package for Robust Dynamic Mode Decomposition
Sara M. Ichinaga, Francesco Andreuzzi, Nicola Demo et al.