MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning

16 June 2019

Papers citing "MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning"

24 / 24 papers shown

Title
Verified Probabilistic Policies for Deep Reinforcement Learning E. Bacci David Parker 36 4 0 10 Jan 2022
Explainable Deep Reinforcement Learning Using Introspection in a Non-episodic Task Angel Ayala Francisco Cruz Bruno José Torres Fernandes Richard Dazeley 27 6 0 18 Aug 2021
Towards Scalable Verification of Deep Reinforcement Learning Guy Amir Michael Schapira Guy Katz OffRL 36 46 0 25 May 2021
KnowRU: Knowledge Reusing via Knowledge Distillation in Multi-agent Reinforcement Learning Zijian Gao Kele Xu Bo Ding Huaimin Wang Yiying Li Hongda Jia 55 15 0 27 Mar 2021
Explainability in Deep Reinforcement Learning Alexandre Heuillet Fabien Couthouis Natalia Díaz Rodríguez XAI 79 279 0 15 Aug 2020
Knowledge Distillation: A Survey Jianping Gou B. Yu Stephen J. Maybank Dacheng Tao VLM 42 2,907 0 09 Jun 2020
Explainable Reinforcement Learning: A Survey Erika Puiutta Eric M. S. P. Veith XAI 30 242 0 13 May 2020
An Inductive Synthesis Framework for Verifiable Reinforcement Learning He Zhu Zikang Xiong Stephen Magill Suresh Jagannathan 35 95 0 16 Jul 2019
Learning Causal State Representations of Partially Observable Environments Amy Zhang Zachary Chase Lipton Luis Villaseñor-Pineda Kamyar Azizzadenesheli Anima Anandkumar Laurent Itti Joelle Pineau Tommaso Furlanello CML 45 49 0 25 Jun 2019
Explainable Machine Learning for Scientific Insights and Discoveries R. Roscher B. Bohn Marco F. Duarte Jochen Garcke XAI 47 662 0 21 May 2019
Formal Verification of Input-Output Mappings of Tree Ensembles John Törnblom Simin Nadjm-Tehrani 31 36 0 10 May 2019
Hierarchical Routing Mixture of Experts Wenbo Zhao Yang Gao Shahan Ali Memon Bhiksha Raj Rita Singh MoE 18 4 0 18 Mar 2019
Learning Finite State Representations of Recurrent Policy Networks Anurag Koul S. Greydanus Alan Fern 21 88 0 29 Nov 2018
Local Rule-Based Explanations of Black Box Decision Systems Riccardo Guidotti A. Monreale Salvatore Ruggieri D. Pedreschi Franco Turini F. Giannotti 101 436 0 28 May 2018
Verifiable Reinforcement Learning via Policy Extraction Osbert Bastani Yewen Pu Armando Solar-Lezama OffRL 104 331 0 22 May 2018
Born Again Neural Networks Tommaso Furlanello Zachary Chase Lipton Michael Tschannen Laurent Itti Anima Anandkumar 49 1,030 0 12 May 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 69 3,922 0 06 Feb 2018
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 334 3,742 0 28 Feb 2017
The Mythos of Model Interpretability Zachary Chase Lipton FaML 78 3,672 0 10 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 338 16,765 0 16 Feb 2016
Dueling Network Architectures for Deep Reinforcement Learning Ziyun Wang Tom Schaul Matteo Hessel H. V. Hasselt Marc Lanctot Nando de Freitas OffRL 52 3,742 0 20 Nov 2015
Policy Distillation Andrei A. Rusu Sergio Gomez Colmenarejo Çağlar Gülçehre Guillaume Desjardins J. Kirkpatrick Razvan Pascanu Volodymyr Mnih Koray Kavukcuoglu R. Hadsell 39 685 0 19 Nov 2015
Distilling the Knowledge in a Neural Network Geoffrey E. Hinton Oriol Vinyals J. Dean FedML 100 19,448 0 09 Mar 2015
A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning Stéphane Ross Geoffrey J. Gordon J. Andrew Bagnell OffRL 107 3,196 0 02 Nov 2010