Papers
Post-training Iterative Hierarchical Data Augmentation for Deep Networks
Adil Khan, Khadija Fraz
Practical Low-Rank Communication Compression in Decentralized Deep Learning
Thijs Vogels, Sai Praneeth Karimireddy, Martin Jaggi
Practical No-box Adversarial Attacks against DNNs
Qizhang Li, Yiwen Guo, Hao Chen
Practical Quasi-Newton Methods for Training Deep Neural Networks
Donald Goldfarb, Yi Ren, Achraf Bahamou
PRANK: motion Prediction based on RANKing
Yuriy Biktairov, Maxim Stebelev, Irina Rudenko et al.
Precise expressions for random projections: Low-rank approximation and randomized Newton
Michal Derezinski, Feynman T Liang, Zhenyu Liao et al.
Predicting Training Time Without Training
Luca Zancato, Alessandro Achille, Avinash Ravichandran et al.
Prediction with Corrupted Expert Advice
Idan Amir, Idan Attias, Tomer Koren et al.
Predictive coding in balanced neural networks with noise, chaos and delays
Jonathan Kadmon, Jonathan Timcheck, Surya Ganguli
Predictive inference is free with the jackknife+-after-bootstrap
Byol Kim, Chen Xu, Rina Barber
Predictive Information Accelerates Learning in RL
Kuang-Huei Lee, Ian Fischer, Anthony Liu et al.
Preference-based Reinforcement Learning with Finite-Time Guarantees
Yichong Xu, Ruosong Wang, Lin Yang et al.
Preference learning along multiple criteria: A game-theoretic perspective
Kush Bhatia, Ashwin Pananjady, Peter L. Bartlett et al.
Pre-training via Paraphrasing
Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh et al.
Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm
Adil Salim, Peter Richtarik
Primal-Dual Mesh Convolutional Neural Networks
Francesco Milano, Antonio Loquercio, Antoni Rosinol et al.
Principal Neighbourhood Aggregation for Graph Nets
Gabriele Corso, Luca Cavalleri, Dominique Beaini et al.
Privacy Amplification via Random Check-Ins
Borja Balle, Peter Kairouz, Brendan McMahan et al.
Private Identity Testing for High-Dimensional Distributions
Clément L Canonne, Gautam Kamath, Audra McMillan et al.
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity
Haim Kaplan, Yishay Mansour, Uri Stemmer et al.
Probabilistic Active Meta-Learning
Jean Kaddour, Steindor Saemundsson, Marc Deisenroth (he/him)
Probabilistic Circuits for Variational Inference in Discrete Graphical Models
Andy Shih, Stefano Ermon
Probabilistic Fair Clustering
Seyed Esmaeili, Brian Brubach, Leonidas Tsepenekas et al.
Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations
Zhe Zeng, Paolo Morettin, Fanqi Yan et al.
Probabilistic Linear Solvers for Machine Learning
Jonathan Wenger, Philipp Hennig