Robuta

https://www.kaggle.com/discussions/general/318032 What is PAC Learning? | Kaggle PAC (Probably Approximately Correct) learning is a framework used for mathematical analysis. A PAC Learner tries to learn a concept (approximately correct) b... what is pac learning kaggle https://jmlr.org/papers/v23/21-1189.html Fairness-Aware PAC Learning from Corrupted Data pac learning fairness aware corrupted data https://www.jmlr.org/papers/v24/21-1250.html PAC-learning for Strategic Classification pac learning strategic classification https://openreview.net/forum?id=bNkKkQM9wx Efficient Optimal PAC Learning | OpenReview Recent advances in the binary classification setting by Hanneke (2016) and Larsen (2023) have resulted in optimal PAC learners. These learners leverage,... pac learning efficient optimal openreview https://arxiv.org/abs/2002.11519v1 [2002.11519v1] Decidability of Sample Complexity of PAC Learning in finite setting Abstract page for arXiv paper 2002.11519v1: Decidability of Sample Complexity of PAC Learning in finite setting sample complexity pac learning 2002 decidability https://wildwillpower.org/ Wild Willpower PAC | Learning from the Past to Prepare for the Future from the past pac learning to prepare wild willpower https://openreview.net/forum?id=RrqvMvwpWn Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets... Computational learning theory states that many classes of boolean formulas are learnable in polynomial time. This paper addresses the understudied subject of... deep neural networks boolean function pac learning understanding learnability https://openreview.net/forum?id=KTf5SGYZQvt&referrer=%5Bthe%20profile%20of%20Andrea%20Tirinzoni%5D(%2Fprofile%3Fid%3D~Andrea_Tirinzoni2) Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs | OpenReview The first (nearly) matching instance-dependent upper and lower bounds on the sample complexity of PAC RL in deterministic episodic MDPs reinforcement learning near instance optimal pac