Papers
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems
Masatoshi Uehara, Ayush Sekhari, Jason Lee et al.
Provably expressive temporal graph networks
Amauri Souza, Diego Mesquita, Samuel Kaski et al.
Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning
Dingwen Kong, Lin Yang
Provably sample-efficient RL with side information about latent dynamics
Yao Liu, Dipendra Misra, Miro Dudik et al.
Provably tuning the ElasticNet across instances
Maria-Florina F Balcan, Misha Khodak, Dravyansh Sharma et al.
Proximal Learning With Opponent-Learning Awareness
Stephen Zhao, Chris Lu, Roger B Grosse et al.
Proximal Point Imitation Learning
Luca Viano, Angeliki Kamoutsi, Gergely Neu et al.
Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networks
Paolo Muratore, Sina Tafazoli, Eugenio Piasini et al.
Pruning has a disparate impact on model accuracy
Cuong Tran, Ferdinando Fioretto, Jung-Eun Kim et al.
Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions
Murad Tukan, Loay Mualem, Alaa Maalouf
Pruning’s Effect on Generalization Through the Lens of Training and Regularization
Tian Jin, Michael Carbin, Dan Roy et al.
Pseudo-Riemannian Graph Convolutional Networks
Bo Xiong, Shichao Zhu, Nico Potyka et al.
Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social Text Classification
Karish Grover, S M Phaneendra Angara, Md Shad Akhtar et al.
PulseImpute: A Novel Benchmark Task for Pulsative Physiological Signal Imputation
Maxwell Xu, Alexander Moreno, Supriya Nagesh et al.
Pure Transformers are Powerful Graph Learners
Jinwoo Kim, Dat Nguyen, Seonwoo Min et al.
Pushing the limits of fairness impossibility: Who's the fairest of them all?
Brian Hsu, Rahul Mazumder, Preetam Nandy et al.
pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models
Zitao Liu, Qiongqiong Liu, Jiahao Chen et al.
Pyramid Attention For Source Code Summarization
Lei Chai, Ming LI
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining
Yuting Gao, Jinfeng Liu, Zihan Xu et al.
Pythae: Unifying Generative Autoencoders in Python - A Benchmarking Use Case
Clément Chadebec, Louis Vincent, Stephanie Allassonniere
QC-StyleGAN - Quality Controllable Image Generation and Manipulation
Dat Viet Thanh Nguyen, Phong Tran The, Tan M. Dinh et al.
Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP
Thao Nguyen, Gabriel Ilharco, Mitchell Wortsman et al.
Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference
Vo Nguyen Le Duy, Shogo Iwazaki, Ichiro Takeuchi
Quantile Constrained Reinforcement Learning: A Reinforcement Learning Framework Constraining Outage Probability
Whiyoung Jung, Myungsik Cho, Jongeui Park et al.
Quantized Training of Gradient Boosting Decision Trees
Yu Shi, Guolin Ke, Zhuoming Chen et al.