Papers
Planning to Explore via Self-Supervised World Models
Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis et al.
p-Norm Flow Diffusion for Local Graph Clustering
Kimon Fountoulakis, Di Wang, Shenghao Yang
Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates
Jeff Calder, Brendan Cook, Matthew Thorpe et al.
Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning
Amin Rakhsha, Goran Radanovic, Rati Devidze et al.
PolyGen: An Autoregressive Generative Model of 3D Meshes
Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami et al.
Polynomial Tensor Sketch for Element-wise Function of Low-Rank Matrix
Insu Han, Haim Avron, Jinwoo Shin
Population-Based Black-Box Optimization for Biological Sequence Design
Christof Angermueller, David Belanger, Andreea Gane et al.
PoWER-BERT: Accelerating BERT Inference via Progressive Word-vector Elimination
Saurabh Goyal, Anamitra Roy Choudhury, Saurabh Raje et al.
PowerNorm: Rethinking Batch Normalization in Transformers
Sheng Shen, Zhewei Yao, Amir Gholami et al.
Predicting Choice with Set-Dependent Aggregation
Nir Rosenfeld, Kojin Oshiba, Yaron Singer
Predicting deliberative outcomes
Vikas Garg, Tommi Jaakkola
Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control
Jie Xu, Yunsheng Tian, Pingchuan Ma et al.
Predictive Coding for Locally-Linear Control
Rui Shu, Tung Nguyen, Yinlam Chow et al.
Predictive Multiplicity in Classification
Charles Marx, Flavio Calmon, Berk Ustun
Predictive Sampling with Forecasting Autoregressive Models
Auke Wiggers, Emiel Hoogeboom
Preference Modeling with Context-Dependent Salient Features
Amanda Bower, Laura Balzano
Preselection Bandits
Viktor Bengs, Eyke Hüllermeier
Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification
Hui Ye, Zhiyu Chen, Da-Han Wang et al.
Principled learning method for Wasserstein distributionally robust optimization with local perturbations
Yongchan Kwon, Wonyoung Kim, Joong-Ho Won et al.
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead
Badih Ghazi, Ravi Kumar, Pasin Manurangsi et al.
Privately detecting changes in unknown distributions
Rachel Cummings, Sara Krehbiel, Yuliia Lut et al.
Privately Learning Markov Random Fields
Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni et al.
Private Outsourced Bayesian Optimization
Dmitrii Kharkovskii, Zhongxiang Dai, Bryan Kian Hsiang Low
Private Query Release Assisted by Public Data
Raef Bassily, Albert Cheu, Shay Moran et al.
Private Reinforcement Learning with PAC and Regret Guarantees
Giuseppe Vietri, Borja Balle, Akshay Krishnamurthy et al.