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Mikael Henaff
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2020 – today
- 2024
- [c16]Arjun Majumdar, Anurag Ajay, Xiaohan Zhang, Pranav Putta, Sriram Yenamandra, Mikael Henaff, Sneha Silwal, Paul McVay, Oleksandr Maksymets, Sergio Arnaud, Karmesh Yadav, Qiyang Li, Ben Newman, Mohit Sharma, Vincent-Pierre Berges, Shiqi Zhang, Pulkit Agrawal, Yonatan Bisk, Dhruv Batra, Mrinal Kalakrishnan, Franziska Meier, Chris Paxton, Alexander Sax, Aravind Rajeswaran:
OpenEQA: Embodied Question Answering in the Era of Foundation Models. CVPR 2024: 16488-16498 - [c15]Martin Klissarov, Pierluca D'Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, Mikael Henaff:
Motif: Intrinsic Motivation from Artificial Intelligence Feedback. ICLR 2024 - [c14]Sharath Chandra Raparthy, Eric Hambro, Robert Kirk, Mikael Henaff, Roberta Raileanu:
Generalization to New Sequential Decision Making Tasks with In-Context Learning. ICML 2024 - [i16]Qinqing Zheng, Mikael Henaff, Amy Zhang, Aditya Grover, Brandon Amos:
Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedback. CoRR abs/2410.23022 (2024) - 2023
- [c13]Mikael Henaff, Minqi Jiang, Roberta Raileanu:
A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs. ICML 2023: 12972-12999 - [c12]Qinqing Zheng, Mikael Henaff, Brandon Amos, Aditya Grover:
Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories. ICML 2023: 42339-42362 - [i15]Mikael Henaff, Minqi Jiang, Roberta Raileanu:
A Study of Global and Episodic Bonuses for Exploration in Contextual MDPs. CoRR abs/2306.03236 (2023) - [i14]Martin Klissarov, Pierluca D'Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, Mikael Henaff:
Motif: Intrinsic Motivation from Artificial Intelligence Feedback. CoRR abs/2310.00166 (2023) - [i13]Sharath Chandra Raparthy, Eric Hambro, Robert Kirk, Mikael Henaff, Roberta Raileanu:
Generalization to New Sequential Decision Making Tasks with In-Context Learning. CoRR abs/2312.03801 (2023) - 2022
- [c11]Mikael Henaff, Roberta Raileanu, Minqi Jiang, Tim Rocktäschel:
Exploration via Elliptical Episodic Bonuses. NeurIPS 2022 - [i12]Mikael Henaff, Roberta Raileanu, Minqi Jiang, Tim Rocktäschel:
Exploration via Elliptical Episodic Bonuses. CoRR abs/2210.05805 (2022) - [i11]Qinqing Zheng, Mikael Henaff, Brandon Amos, Aditya Grover:
Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories. CoRR abs/2210.06518 (2022) - 2020
- [c10]Kianté Brantley, Wen Sun, Mikael Henaff:
Disagreement-Regularized Imitation Learning. ICLR 2020 - [c9]Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford:
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning. ICML 2020: 6961-6971 - [c8]Alekh Agarwal, Mikael Henaff, Sham M. Kakade, Wen Sun:
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning. NeurIPS 2020 - [i10]Alekh Agarwal, Mikael Henaff, Sham M. Kakade, Wen Sun:
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning. CoRR abs/2007.08459 (2020)
2010 – 2019
- 2019
- [c7]Mikael Henaff, Alfredo Canziani, Yann LeCun:
Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic. ICLR (Poster) 2019 - [c6]Mikael Henaff:
Explicit Explore-Exploit Algorithms in Continuous State Spaces. NeurIPS 2019: 9372-9382 - [i9]Mikael Henaff, Alfredo Canziani, Yann LeCun:
Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic. CoRR abs/1901.02705 (2019) - [i8]Mikael Henaff:
Explicit Explore-Exploit Algorithms in Continuous State Spaces. CoRR abs/1911.00617 (2019) - [i7]Dipendra Misra, Mikael Henaff, Akshay Krishnamurthy, John Langford:
Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning. CoRR abs/1911.05815 (2019) - 2018
- [b1]Mikael Henaff:
Deep Networks for Forward Prediction and Planning. New York University, USA, 2018 - 2017
- [c5]Mikael Henaff, Jason Weston, Arthur Szlam, Antoine Bordes, Yann LeCun:
Tracking the World State with Recurrent Entity Networks. ICLR (Poster) 2017 - [i6]Mikael Henaff, William F. Whitney, Yann LeCun:
Model-Based Planning in Discrete Action Spaces. CoRR abs/1705.07177 (2017) - [i5]Mikael Henaff, Junbo Jake Zhao, Yann LeCun:
Prediction Under Uncertainty with Error-Encoding Networks. CoRR abs/1711.04994 (2017) - 2016
- [c4]Mikael Henaff, Arthur Szlam, Yann LeCun:
Recurrent Orthogonal Networks and Long-Memory Tasks. ICML 2016: 2034-2042 - [i4]Mikael Henaff, Arthur Szlam, Yann LeCun:
Orthogonal RNNs and Long-Memory Tasks. CoRR abs/1602.06662 (2016) - [i3]Mikael Henaff, Jason Weston, Arthur Szlam, Antoine Bordes, Yann LeCun:
Tracking the World State with Recurrent Entity Networks. CoRR abs/1612.03969 (2016) - 2015
- [j1]Alexander R. Statnikov, Sisi Ma, Mikael Henaff, Nikita I. Lytkin, Efstratios Efstathiadis, Eric R. Peskin, Constantin F. Aliferis:
Ultra-scalable and efficient methods for hybrid observational and experimental local causal pathway discovery. J. Mach. Learn. Res. 16: 3219-3267 (2015) - [c3]Anna Choromanska, Mikael Henaff, Michaël Mathieu, Gérard Ben Arous, Yann LeCun:
The Loss Surfaces of Multilayer Networks. AISTATS 2015 - [i2]Mikael Henaff, Joan Bruna, Yann LeCun:
Deep Convolutional Networks on Graph-Structured Data. CoRR abs/1506.05163 (2015) - 2014
- [c2]Michaël Mathieu, Mikael Henaff, Yann LeCun:
Fast Training of Convolutional Networks through FFTs. ICLR (Poster) 2014 - [i1]Anna Choromanska, Mikael Henaff, Michaël Mathieu, Gérard Ben Arous, Yann LeCun:
The Loss Surface of Multilayer Networks. CoRR abs/1412.0233 (2014) - 2011
- [c1]Mikael Henaff, Kevin Jarrett, Koray Kavukcuoglu, Yann LeCun:
Unsupervised Learning of Sparse Features for Scalable Audio Classification. ISMIR 2011: 681-686
Coauthor Index
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