ICLR 2021 Workshop Learning to Learn Submissions

Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment
Michael Chang, Sidhant Kaushik, Thomas L. Griffiths, Sergey Levine
- 15 Mar 2021 (modified: 05 May 2023)
- Learning to Learn 2021
- Readers: Everyone
Accelerating Online Reinforcement Learning via Model-Based Meta-Learning
John D Co-Reyes, Sarah Feng, Glen Berseth, Jie Qui, Sergey Levine
- 15 Mar 2021 (modified: 05 May 2023)
- Learning to Learn 2021
- Readers: Everyone
Metalearning Using Structure-rich Pipeline Representations for Better AutoML
Anonymous
- 13 Mar 2021 (modified: 05 May 2023)
- Submitted to Learning to Learn 2021
- Readers: Everyone
Meta-Learning for Planning: Automatic Synthesis of Sample Based Planners
Lucas Paul Saldyt, Heni Amor
- 13 Mar 2021 (modified: 05 May 2023)
- Learning to Learn 2021
- Readers: Everyone
Compositionality as Learning Bias in Generative RNNs solves the Omniglot Challenge
Sarah Fabi, Sebastian Otte, Martin V. Butz
- 13 Mar 2021 (modified: 05 May 2023)
- Learning to Learn 2021
- Readers: Everyone
Learning where to learn
Dominic Zhao, Nicolas Zucchet, Joao Sacramento, Johannes Von Oswald
- 13 Mar 2021 (modified: 05 May 2023)
- Learning to Learn 2021
- Readers: Everyone
Meta Learning for Multi-agent Communication
Abhinav Gupta, Angeliki Lazaridou, Marc Lanctot
- 13 Mar 2021 (modified: 05 May 2023)
- Learning to Learn 2021
- Readers: Everyone
Trustworthy Disease Discovery: A Case Study in Alzheimer’s
Anonymous
- 13 Mar 2021 (modified: 05 May 2023)
- Submitted to Learning to Learn 2021
- Readers: Everyone
How Sensitive are Meta-Learners to Dataset Imbalance?
Mateusz Ochal, Massimiliano Patacchiola, Jose Manuel Vazquez diosdado, Amos Storkey, Sen Wang
- 13 Mar 2021 (modified: 22 Oct 2023)
- Learning to Learn 2021
- Readers: Everyone
Exploring the Similarity of Representations in Model-Agnostic Meta-Learning
Thomas Goerttler, Klaus Obermayer
- 13 Mar 2021 (modified: 05 May 2023)
- Learning to Learn 2021
- Readers: Everyone
Neurons learn slower than they think
Anonymous
- 13 Mar 2021 (modified: 05 May 2023)
- Submitted to Learning to Learn 2021
- Readers: Everyone
The Emergence of Abstract and Episodic Neurons in Episodic Meta-RL
Badr AlKhamissi, Muhammad ElNokrashy, Michael Spranger
- 13 Mar 2021 (modified: 22 Oct 2023)
- Learning to Learn 2021
- Readers: Everyone
Meta-learning using privileged information for dynamics
Ben Day, Alexander Luke Ian Norcliffe, Jacob Moss, Pietro Liò
- 13 Mar 2021 (modified: 22 Oct 2023)
- Learning to Learn 2021
- Readers: Everyone
FEW-SHOTLEARNING WITH WEAK SUPERVISION
Ali Ghadirzadeh, Petra Poklukar, Xi Chen, Huaxiu Yao, Hossein Azizpour, Mårten Björkman, Chelsea Finn, Danica Kragic
- 13 Mar 2021 (modified: 05 May 2023)
- Learning to Learn 2021
- Readers: Everyone
Offline Meta Learning of Exploration
Ron Dorfman, Idan Shenfeld, Aviv Tamar
- 13 Mar 2021 (modified: 05 May 2023)
- Learning to Learn 2021
- Readers: Everyone
Meta-learning as Learning the Meta: A Videogame-Theoretic Perspective on\\ Learning to Learn
Anonymous
- 13 Mar 2021 (modified: 05 May 2023)
- Submitted to Learning to Learn 2021
- Readers: Everyone