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Papers & CODE

Zero-Shot / One-Shot / Few-Shot / Low-Shot Learning

  • Siamese Neural Networks for One-shot Image Recognition, (2015), Gregory Koch, Richard Zemel, Ruslan Salakhutdinov. [pdf] [code]

  • Prototypical Networks for Few-shot Learning, (2017), Jake Snell, Kevin Swersky, Richard S. Zemel. [pdf] [code]

  • Gaussian Prototypical Networks for Few-Shot Learning on Omniglot (2017), Stanislav Fort. [pdf] [code]

  • Matching Networks for One Shot Learning, (2017), Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, Daan Wierstra. [pdf] [code]

  • Learning to Compare: Relation Network for Few-Shot Learning, (2017), Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H.S. Torr, Timothy M. Hospedales. [pdf] [code]

  • One-shot Learning with Memory-Augmented Neural Networks, (2016), Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap. [pdf] [code]

  • Optimization as a Model for Few-Shot Learning, (2016), Sachin Ravi and Hugo Larochelle. [pdf] [code]

  • An embarrassingly simple approach to zero-shot learning, (2015), B Romera-Paredes, Philip H. S. Torr. [pdf] [code]

  • Low-shot Learning by Shrinking and Hallucinating Features, (2017), Bharath Hariharan, Ross Girshick. [pdf] [code]

  • Low-shot learning with large-scale diffusion, (2018), Matthijs Douze, Arthur Szlam, Bharath Hariharan, Hervé Jégou. [pdf] [code]

  • Low-Shot Learning with Imprinted Weights, (2018), Hang Qi, Matthew Brown, David G. Lowe. [pdf] [code]

  • One-Shot Video Object Segmentation, (2017), S. Caelles and K.K. Maninis and J. Pont-Tuset and L. Leal-Taixe' and D. Cremers and L. Van Gool. [pdf] [code]

  • One-Shot Learning for Semantic Segmentation, (2017), Amirreza Shaban, Shray Bansal, Zhen Liu, Irfan Essa, Byron Boots. [pdf] [code]

  • Few-Shot Segmentation Propagation with Guided Networks, (2018), Kate Rakelly, Evan Shelhamer, Trevor Darrell, Alexei A. Efros, Sergey Levine. [pdf] [code]

  • Few-Shot Semantic Segmentation with Prototype Learning, (2018), Nanqing Dong and Eric P. Xing. [pdf]

  • Dynamic Few-Shot Visual Learning without Forgetting, (2018), Spyros Gidaris, Nikos Komodakis. [pdf] [code]

  • Feature Generating Networks for Zero-Shot Learning, (2017), Yongqin Xian, Tobias Lorenz, Bernt Schiele, Zeynep Akata. [pdf]

  • Meta-Learning Deep Visual Words for Fast Video Object Segmentation, (2019), Harkirat Singh Behl, Mohammad Najafi, Anurag Arnab, Philip H.S. Torr. [pdf]

Model Agnostic Meta Learning

  • Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, (2017), Chelsea Finn, Pieter Abbeel, Sergey Levine. [pdf] [code]

  • Adversarial Meta-Learning, (2018), Chengxiang Yin, Jian Tang, Zhiyuan Xu, Yanzhi Wang. [pdf] [code]

  • On First-Order Meta-Learning Algorithms, (2018), Alex Nichol, Joshua Achiam, John Schulman. [pdf] [code]

  • Meta-SGD: Learning to Learn Quickly for Few-Shot Learning, (2017), Zhenguo Li, Fengwei Zhou, Fei Chen, Hang Li. [pdf] [code]

  • Gradient Agreement as an Optimization Objective for Meta-Learning, (2018), Amir Erfan Eshratifar, David Eigen, Massoud Pedram. [pdf] [code]

  • Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace, (2018), Yoonho Lee, Seungjin Choi. [pdf] [code]

  • A Simple Neural Attentive Meta-Learner, (2018), Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, Pieter Abbeel. [pdf] [code]

  • Personalizing Dialogue Agents via Meta-Learning, (2019), Zhaojiang Lin, Andrea Madotto, Chien-Sheng Wu, Pascale Fung. [pdf] [code]

  • How to train your MAML, (2019), Antreas Antoniou, Harrison Edwards, Amos Storkey. [pdf] [code]

  • Learning to learn by gradient descent by gradient descent, (206), Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W. Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, Nando de Freitas. [pdf] [code]

  • Unsupervised Learning via Meta-Learning, (2019), Kyle Hsu, Sergey Levine, Chelsea Finn. [pdf] [code]

  • Few-Shot Image Recognition by Predicting Parameters from Activations, (2018), Siyuan Qiao, Chenxi Liu, Wei Shen, Alan Yuille. [pdf] [code]

  • One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning, (2018), Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Pieter Abbeel, Sergey Levine, [pdf] [code]

  • MetaGAN: An Adversarial Approach to Few-Shot Learning, (2018), ZHANG, Ruixiang and Che, Tong and Ghahramani, Zoubin and Bengio, Yoshua and Song, Yangqiu. [pdf]

  • Fast Parameter Adaptation for Few-shot Image Captioning and Visual Question Answering,(2018), Xuanyi Dong, Linchao Zhu, De Zhang, Yi Yang, Fei Wu. [pdf]

  • CAML: Fast Context Adaptation via Meta-Learning, (2019), Luisa M Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann, Shimon Whiteson. [pdf]

  • Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems, (2019), Fei Mi, Minlie Huang, Jiyong Zhang, Boi Faltings. [pdf]

  • MIND: Model Independent Neural Decoder, (2019), Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan. [pdf]

  • Toward Multimodal Model-Agnostic Meta-Learning, (2018), Risto Vuorio, Shao-Hua Sun, Hexiang Hu, Joseph J. Lim. [pdf]

  • Alpha MAML: Adaptive Model-Agnostic Meta-Learning, (2019), Harkirat Singh Behl, Atılım Güneş Baydin, Philip H. S. Torr. [pdf]

  • Online Meta-Learning, (2019), Chelsea Finn, Aravind Rajeswaran, Sham Kakade, Sergey Levine. [pdf]

Meta Reinforcement Learning

  • Generalizing Skills with Semi-Supervised Reinforcement Learning, (2017), Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, Sergey Levine. [pdf] [code]

  • Guided Meta-Policy Search, (2019), Russell Mendonca, Abhishek Gupta, Rosen Kralev, Pieter Abbeel, Sergey Levine, Chelsea Finn. [pdf] [code]

  • End-to-End Robotic Reinforcement Learning without Reward Engineering, (2019), Avi Singh, Larry Yang, Kristian Hartikainen, Chelsea Finn, Sergey Levine. [pdf] [code]

  • Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables, (2019), Kate Rakelly, Aurick Zhou, Deirdre Quillen, Chelsea Finn, Sergey Levine. [pdf] [code]

  • Meta-Gradient Reinforcement Learning, (2018), Zhongwen Xu, Hado van Hasselt,David Silver. [pdf]

  • Task-Agnostic Dynamics Priors for Deep Reinforcement Learning, (2019), Yilun Du, Karthik Narasimhan. [pdf]

  • Meta Reinforcement Learning with Task Embedding and Shared Policy,(2019), Lin Lan, Zhenguo Li, Xiaohong Guan, Pinghui Wang. [pdf]

  • NoRML: No-Reward Meta Learning, (2019), Yuxiang Yang, Ken Caluwaerts, Atil Iscen, Jie Tan, Chelsea Finn. [pdf]

  • Actor-Critic Algorithms for Constrained Multi-agent Reinforcement Learning, (2019), Raghuram Bharadwaj Diddigi, Sai Koti Reddy Danda, Prabuchandran K. J., Shalabh Bhatnagar. [pdf]

  • Adaptive Guidance and Integrated Navigation with Reinforcement Meta-Learning, (2019), Brian Gaudet, Richard Linares, Roberto Furfaro. [pdf]

  • Watch, Try, Learn: Meta-Learning from Demonstrations and Reward, (2019), Allan Zhou, Eric Jang, Daniel Kappler, Alex Herzog, Mohi Khansari, Paul Wohlhart, Yunfei Bai, Mrinal Kalakrishnan, Sergey Levine, Chelsea Finn. [pdf]

  • Options as responses: Grounding behavioural hierarchies in multi-agent RL, (2019), Alexander Sasha Vezhnevets, Yuhuai Wu, Remi Leblond, Joel Z. Leibo. [pdf]

  • Learning latent state representation for speeding up exploration, (2019), Giulia Vezzani, Abhishek Gupta, Lorenzo Natale, Pieter Abbeel. [pdf]

  • Beyond Exponentially Discounted Sum: Automatic Learning of Return Function, (2019), Yufei Wang, Qiwei Ye, Tie-Yan Liu. [pdf]

  • Learning Efficient and Effective Exploration Policies with Counterfactual Meta Policy, (2019), Ruihan Yang, Qiwei Ye, Tie-Yan Liu. [pdf]

  • Dealing with Non-Stationarity in Multi-Agent Deep Reinforcement Learning, (2019), Georgios Papoudakis, Filippos Christianos, Arrasy Rahman, Stefano V. Albrecht. [pdf]

  • Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning, (2019), Yufei Wang, Ziju Shen, Zichao Long, Bin Dong. [[pdf]]

Other & Uncategorized

Grounded Language Learning Fast and Slow
by Hill, Felix and Tieleman, Olivier and von Glehn, Tamara and Wong, Nathaniel and Merzic, Hamza and Clark, Stephen
http://arxiv.org/abs/2009.01719

Sparse Meta Networks for Sequential Adaptation and its Application to Adaptive Language Modelling
by Munkhdalai, Tsendsuren
http://arxiv.org/abs/2009.01803

Learning with Differentiable Perturbed Optimizers
by Berthet, Quentin and Blondel, Mathieu and Teboul, Olivier and Cuturi, Marco and Vert, Jean-Philippe and Bach, Francis
http://arxiv.org/abs/2002.08676

What is being transferred in transfer learning?
by Neyshabur, Behnam and Sedghi, Hanie and Zhang, Chiyuan
http://arxiv.org/abs/2008.11687

On modulating the gradient for meta-learning
by Simon, Christian and Koniusz, Piotr and Nock, Richard and Harandi, Mehrtash
https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530545.pdf

Meta-Learning with Shared Amortized Variational Inference
by Iakovleva, Ekaterina and Verbeek, Jakob and Alahari, Karteek
http://arxiv.org/abs/2008.12037

learn2learn: A Library for Meta-Learning Research
by Arnold, Sébastien M R and Mahajan, Praateek and Datta, Debajyoti and Bunner, Ian and Zarkias, Konstantinos Saitas
http://arxiv.org/abs/2008.12284

A Universal Representation Transformer Layer for Few-Shot Image Classification
by Liu, Lu and Hamilton, William and Long, Guodong and Jiang, Jing and Larochelle, Hugo
http://arxiv.org/abs/2006.11702

Safe Model-Based Meta-Reinforcement Learning: A Sequential Exploration-Exploitation Framework
by Lew, Thomas and Sharma, Apoorva and Harrison, James and Pavone, Marco
http://arxiv.org/abs/2008.11700

Learning to Learn in a Semi-Supervised Fashion
by Chen, Yun-Chun and Chou, Chao-Te and Wang, Yu-Chiang Frank
http://arxiv.org/abs/2008.11203

The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning
by Denevi, Giulia and Pontil, Massimiliano and Ciliberto, Carlo
http://arxiv.org/abs/2008.10857

Adaptive Multi-level Hyper-gradient Descent
by Jie, Renlong and Gao, Junbin and Vasnev, Andrey and Tran, Minh-Ngoc
http://arxiv.org/abs/2008.07277

Few-Shot Image Classification via Contrastive Self-Supervised Learning
by Li, Jianyi and Liu, Guizhong
http://arxiv.org/abs/2008.09942

Does MAML really want feature reuse only?
by Oh, Jaehoon and Yoo, Hyungjun and Kim, Changhwan and Yun, Se-Young
http://arxiv.org/abs/2008.08882

Meta Learning MPC using Finite-Dimensional Gaussian Process Approximations
by Arcari, Elena and Carron, Andrea and Zeilinger, Melanie N
http://arxiv.org/abs/2008.05984

Offline Meta-Reinforcement Learning with Advantage Weighting
by Mitchell, Eric and Rafailov, Rafael and Peng, Xue Bin and Levine, Sergey and Finn, Chelsea
http://arxiv.org/abs/2008.06043

Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning
by Liu, Evan Zheran and Raghunathan, Aditi and Liang, Percy and Finn, Chelsea
http://arxiv.org/abs/2008.02790

Offline Meta Reinforcement Learning
by Dorfman, Ron and Tamar, Aviv
http://arxiv.org/abs/2008.02598

Few-Shot Learning via Learning the Representation, Provably
by Du, Simon S and Hu, Wei and Kakade, Sham M and Lee, Jason D and Lei, Qi
http://arxiv.org/abs/2002.09434

Multi-Task Reinforcement Learning as a Hidden-Parameter Block MDP
by Zhang, Amy and Sodhani, Shagun and Khetarpal, Khimya and Pineau, Joelle
http://arxiv.org/abs/2007.07206

CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs
by Chitnis, Rohan and Silver, Tom and Kim, Beomjoon and Kaelbling, Leslie Pack and Lozano-Perez, Tomas
http://arxiv.org/abs/2007.13202

Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
by Caron, Mathilde and Misra, Ishan and Mairal, Julien and Goyal, Priya and Bojanowski, Piotr and Joulin, Armand
http://arxiv.org/abs/2006.09882

MiCo: Mixup Co-Training for Semi-Supervised Domain Adaptation
by Yang, Luyu and Wang, Yan and Gao, Mingfei and Shrivastava, Abhinav and Weinberger, Kilian Q and Chao, Wei-Lun and Lim, Ser-Nam
http://arxiv.org/abs/2007.12684

Adaptive Task Sampling for Meta-Learning
by Liu, Chenghao and Wang, Zhihao and Sahoo, Doyen and Fang, Yuan and Zhang, Kun and Hoi, Steven C H
http://arxiv.org/abs/2007.08735

Discovering Reinforcement Learning Algorithms
by Oh, Junhyuk and Hessel, Matteo and Czarnecki, Wojciech M and Xu, Zhongwen and van Hasselt, Hado and Singh, Satinder and Silver, David
http://arxiv.org/abs/2007.08794

On the Outsized Importance of Learning Rates in Local Update Methods
by Charles, Zachary and Kone{\v c}n{\'y}, Jakub
http://arxiv.org/abs/2007.00878

Global Convergence and Induced Kernels of Gradient-Based Meta-Learning with Neural Nets
by Wang, Haoxiang and Sun, Ruoyu and Li, Bo
http://arxiv.org/abs/2006.14606

On the Iteration Complexity of Hypergradient Computation
by Grazzi, Riccardo and Franceschi, Luca and Pontil, Massimiliano and Salzo, Saverio
http://arxiv.org/abs/2006.16218

On the Outsized Importance of Learning Rates in Local Update Methods
by Charles, Zachary and Kone{\v c}n{\'y}, Jakub
http://arxiv.org/abs/2007.00878

Meta-SAC: Auto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient
by Wang, Yufei and Ni, Tianwei
http://arxiv.org/abs/2007.01932

Meta Learning in the Continuous Time Limit
by Xu, Ruitu and Chen, Lin and Karbasi, Amin
http://arxiv.org/abs/2006.10921

Expert Training: Task Hardness Aware Meta-Learning for Few-Shot Classification
by Zhou, Yucan and Wang, Yu and Cai, Jianfei and Zhou, Yu and Hu, Qinghua and Wang, Weiping
http://arxiv.org/abs/2007.06240

MTL2L: A Context Aware Neural Optimiser
by Kuo, Nicholas I-Hsien and Harandi, Mehrtash and Fourrier, Nicolas and Walder, Christian and Ferraro, Gabriela and Suominen, Hanna
http://arxiv.org/abs/2007.09343

Navigating the Trade-Off between Multi-Task Learning and Learning to Multitask in Deep Neural Networks
by Ravi, Sachin and Musslick, Sebastian and Hamin, Maia and Willke, Theodore L and Cohen, Jonathan D
http://arxiv.org/abs/2007.10527

Balanced Meta-Softmax for Long-Tailed Visual Recognition
by Ren, Jiawei and Yu, Cunjun and Sheng, Shunan and Ma, Xiao and Zhao, Haiyu and Yi, Shuai and Li, Hongsheng
http://arxiv.org/abs/2007.10740

CrossTransformers: spatially-aware few-shot transfer
by Doersch, Carl and Gupta, Ankush and Zisserman, Andrew
http://arxiv.org/abs/2007.11498

Meta-Learning a Dynamical Language Model
by Wolf, Thomas and Chaumond, Julien and Delangue, Clement
http://arxiv.org/abs/1803.10631

Meta-Learning Requires Meta-Augmentation
by Rajendran, Janarthanan and Irpan, Alex and Jang, Eric
http://arxiv.org/abs/2007.05549

Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift
by Zhang, Marvin and Marklund, Henrik and Gupta, Abhishek and Levine, Sergey and Finn, Chelsea
http://arxiv.org/abs/2007.02931

Meta-Learning Symmetries by Reparameterization
by Zhou, Allan and Knowles, Tom and Finn, Chelsea
http://arxiv.org/abs/2007.02933

Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift
by Zhang, Marvin and Marklund, Henrik and Gupta, Abhishek and Levine, Sergey and Finn, Chelsea
http://arxiv.org/abs/2007.02931

A Brief Look at Generalization in Visual Meta-Reinforcement Learning
by Alver, Safa and Precup, Doina
http://arxiv.org/abs/2006.07262

Learning Representations by Stochastic Meta-Gradient Descent in Neural Networks
by Veeriah, Vivek and Zhang, Shangtong and Sutton, Richard S
http://arxiv.org/abs/1612.02879

PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
by Rothfuss, Jonas and Fortuin, Vincent and Krause, Andreas
http://arxiv.org/abs/2002.05551

Meta-Meta-Classification for One-Shot Learning
by Chowdhury, Arkabandhu and Chaudhari, Dipak and Chaudhuri, Swarat and Jermaine, Chris
http://arxiv.org/abs/2004.08083

Relatedness Measures to Aid the Transfer of Building Blocks among Multiple Tasks
by Nguyen, Trung B and Browne, Will N and Zhang, Mengjie
http://arxiv.org/abs/2005.03947

Information-Theoretic Generalization Bounds for Meta-Learning and Applications
by Jose, Sharu Theresa and Simeone, Osvaldo
http://arxiv.org/abs/2005.04372

On Learning Intrinsic Rewards for Policy Gradient Methods
by Zheng, Zeyu and Oh, Junhyuk and Singh, Satinder
http://arxiv.org/abs/1804.06459

A Sample Complexity Separation between Non-Convex and Convex Meta-Learning
by Saunshi, Nikunj and Zhang, Yi and Khodak, Mikhail and Arora, Sanjeev
http://arxiv.org/abs/2002.11172

Bayesian Online Meta-Learning with Laplace Approximation
by Yap, Pau Ching and Ritter, Hippolyt and Barber, David
http://arxiv.org/abs/2005.00146

Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks
by Schoettler, Gerrit and Nair, Ashvin and Ojea, Juan Aparicio and Levine, Sergey and Solowjow, Eugen
http://arxiv.org/abs/2004.14404

Continual Deep Learning by Functional Regularisation of Memorable Past
by Pan, Pingbo and Swaroop, Siddharth and Immer, Alexander and Eschenhagen, Runa and Turner, Richard E and Khan, Mohammad Emtiyaz
http://arxiv.org/abs/2004.14070

Jelly Bean World: A Testbed for Never-Ending Learning
by Platanios, Emmanouil Antonios and Saparov, Abulhair and Mitchell, Tom
https://openreview.net/pdf?id=Byx_YAVYPH

Encouraging behavioral diversity in evolutionary robotics: an empirical study
by Mouret, J-B and Doncieux, S
http://dx.doi.org/10.1162/EVCO_a_00048

Defining Benchmarks for Continual Few-Shot Learning
by Antoniou, Antreas and Patacchiola, Massimiliano and Ochal, Mateusz and Storkey, Amos
http://arxiv.org/abs/2004.11967

Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning
by Sharma, Archit and Ahn, Michael and Levine, Sergey and Kumar, Vikash and Hausman, Karol and Gu, Shixiang
http://arxiv.org/abs/2004.12974

Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
by Hu, Shell Xu and Moreno, Pablo G and Xiao, Yang and Shen, Xi and Obozinski, Guillaume and Lawrence, Neil D and Damianou, Andreas
http://arxiv.org/abs/2004.12696

Evolving Inborn Knowledge For Fast Adaptation in Dynamic POMDP Problems
by Ben-Iwhiwhu, Eseoghene and Ladosz, Pawel and Dick, Jeffery and Chen, Wen-Hua and Pilly, Praveen and Soltoggio, Andrea
http://arxiv.org/abs/2004.12846

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
by Yu, Tianhe and Quillen, Deirdre and He, Zhanpeng and Julian, Ryan and Hausman, Karol and Finn, Chelsea and Levine, Sergey
http://arxiv.org/abs/1910.10897

Meta reinforcement learning as task inference
by Humplik, Jan and Galashov, Alexandre and Hasenclever, Leonard and Ortega, Pedro A and Teh, Yee Whye and Heess, Nicolas
http://arxiv.org/abs/1905.06424

Meta-Gradient Reinforcement Learning
by Xu, Zhongwen and van Hasselt, Hado and Silver, David
http://arxiv.org/abs/1805.09801

Self-Paced Deep Reinforcement Learning
by Klink, Pascal and D'Eramo, Carlo and Peters, Jan and Pajarinen, Joni
http://arxiv.org/abs/2004.11812

Scheduling the Learning Rate Via Hypergradients: New Insights and a New Algorithm
by Donini, Michele and Franceschi, Luca and Majumder, Orchid and Pontil, Massimiliano and Frasconi, Paolo
https://openreview.net/pdf?id=Ske6qJSKPH

Learning Stabilizable Nonlinear Dynamics with Contraction-Based Regularization
by Singh, Sumeet and Richards, Spencer M and Sindhwani, Vikas and Slotine, Jean-Jacques E and Pavone, Marco
http://arxiv.org/abs/1907.13122

A Comprehensive Overview and Survey of Recent Advances in Meta-Learning
by Peng, Huimin
http://arxiv.org/abs/2004.11149

Learning a Formula of Interpretability to Learn Interpretable Formulas
by Virgolin, Marco and De Lorenzo, Andrea and Medvet, Eric and Randone, Francesca
http://arxiv.org/abs/2004.11170

Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads
by Belkhale, Suneel and Li, Rachel and Kahn, Gregory and McAllister, Rowan and Calandra, Roberto and Levine, Sergey
http://arxiv.org/abs/2004.11345

Frustratingly Simple Few-Shot Object Detection
by Wang, Xin and Huang, Thomas E and Darrell, Trevor and Gonzalez, Joseph E and Yu, Fisher
http://arxiv.org/abs/2003.06957

Meta Pseudo Labels
by Pham, Hieu and Xie, Qizhe and Dai, Zihang and Le, Quoc V
http://arxiv.org/abs/2003.10580

0e56da12-a2f0-4288-b745-c15deec9183a
by Unknown
http://learn2learn.net

Finding online neural update rules by learning to remember
by Gregor, Karol
http://arxiv.org/abs/2003.03124

A New Meta-Baseline for Few-Shot Learning
by Chen, Yinbo and Wang, Xiaolong and Liu, Zhuang and Xu, Huijuan and Darrell, Trevor
http://arxiv.org/abs/2003.04390

Learning to be Global Optimizer
by Zhang, Haotian and Sun, Jianyong and Xu, Zongben
http://arxiv.org/abs/2003.04521

Scalable Multi-Task Imitation Learning with Autonomous Improvement
by Singh, Avi and Jang, Eric and Irpan, Alexander and Kappler, Daniel and Dalal, Murtaza and Levine, Sergey and Khansari, Mohi and Finn, Chelsea
http://arxiv.org/abs/2003.02636

Meta-learning for mixed linear regression
by Kong, Weihao and Somani, Raghav and Song, Zhao and Kakade, Sham and Oh, Sewoong
http://arxiv.org/abs/2002.08936

Provable Meta-Learning of Linear Representations
by Tripuraneni, Nilesh and Jin, Chi and Jordan, Michael I
http://arxiv.org/abs/2002.11684

Learning to Continually Learn
by Beaulieu, Shawn and Frati, Lapo and Miconi, Thomas and Lehman, Joel and Stanley, Kenneth O and Clune, Jeff and Cheney, Nick
http://arxiv.org/abs/2002.09571

PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
by Rothfuss, Jonas and Fortuin, Vincent and Krause, Andreas
http://arxiv.org/abs/2002.05551

Incremental Learning for Metric-Based Meta-Learners
by Liu, Qing and Majumder, Orchid and Ravichandran, Avinash and Bhotika, Rahul and Soatto, Stefano
http://arxiv.org/abs/2002.04162

Hyper-Meta Reinforcement Learning with Sparse Reward
by Hua, Yun and Wang, Xiangfeng and Jin, Bo and Li, Wenhao and Yan, Junchi and He, Xiaofeng and Zha, Hongyuan
http://arxiv.org/abs/2002.04238

Meta-Learning across Meta-Tasks for Few-Shot Learning
by Fei, Nanyi and Lu, Zhiwu and Gao, Yizhao and Tian, Jia and Xiang, Tao and Wen, Ji-Rong
http://arxiv.org/abs/2002.04274

Distribution-Agnostic Model-Agnostic Meta-Learning
by Collins, Liam and Mokhtari, Aryan and Shakkottai, Sanjay
http://arxiv.org/abs/2002.04766

Provably Convergent Policy Gradient Methods for Model-Agnostic Meta-Reinforcement Learning
by Fallah, Alireza and Mokhtari, Aryan and Ozdaglar, Asuman
http://arxiv.org/abs/2002.05135

Meta-learning framework with applications to zero-shot time-series forecasting
by Oreshkin, Boris N and Carpov, Dmitri and Chapados, Nicolas and Bengio, Yoshua
http://arxiv.org/abs/2002.02887

A Loss-Function for Causal Machine-Learning
by Yang, I-Sheng
http://arxiv.org/abs/2001.00629

Self-Tuning Deep Reinforcement Learning
by Zahavy, Tom and Xu, Zhongwen and Veeriah, Vivek and Hessel, Matteo and Van Hasslet, Hado and Silver, David and Singh, Satinder
http://arxiv.org/abs/2002.12928

Learning Adaptive Loss for Robust Learning with Noisy Labels
by Shu, Jun and Zhao, Qian and Chen, Keyu and Xu, Zongben and Meng, Deyu
http://arxiv.org/abs/2002.06482

A Structured Prediction Approach for Conditional Meta-Learning
by Wang, Ruohan and Demiris, Yiannis and Ciliberto, Carlo
http://arxiv.org/abs/2002.08799

Curriculum in Gradient-Based Meta-Reinforcement Learning
by Mehta, Bhairav and Deleu, Tristan and Raparthy, Sharath Chandra and Pal, Chris J and Paull, Liam
http://arxiv.org/abs/2002.07956

Multi-Step Model-Agnostic Meta-Learning: Convergence and Improved Algorithms
by Ji, Kaiyi and Yang, Junjie and Liang, Yingbin
http://arxiv.org/abs/2002.07836

Local Nonparametric Meta-Learning
by Goo, Wonjoon and Niekum, Scott
http://arxiv.org/abs/2002.03272

Revisiting Meta-Learning as Supervised Learning
by Chao, Wei-Lun and Ye, Han-Jia and Zhan, De-Chuan and Campbell, Mark and Weinberger, Kilian Q
http://arxiv.org/abs/2002.00573

SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning
by Wang, Yan and Chao, Wei-Lun and Weinberger, Kilian Q and van der Maaten, Laurens
http://arxiv.org/abs/1911.04623

Fast and Generalized Adaptation for Few-Shot Learning
by Song, Liang and Liu, Jinlu and Qin, Yongqiang
http://arxiv.org/abs/1911.10807

Meta-Learning without Memorization
by Yin, Mingzhang and Tucker, George and Zhou, Mingyuan and Levine, Sergey and Finn, Chelsea
http://arxiv.org/abs/1912.03820

Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One
by Grathwohl, Will and Wang, Kuan-Chieh and Jacobsen, J{\"o}rn-Henrik and Duvenaud, David and Norouzi, Mohammad and Swersky, Kevin
http://arxiv.org/abs/1912.03263

MAME : Model-Agnostic Meta-Exploration
by Gurumurthy, Swaminathan and Kumar, Sumit and Sycara, Katia
http://arxiv.org/abs/1911.04024

Constructing Multiple Tasks for Augmentation: Improving Neural Image Classification With K-means Features
by Gui, Tao and Qing, Lizhi and Zhang, Qi and Ye, Jiacheng and Yan, Hang and Fei, Zichu and Huang, Xuanjing
http://arxiv.org/abs/1911.07518

Meta Adaptation using Importance Weighted Demonstrations
by Lekkala, Kiran and Abu-El-Haija, Sami and Itti, Laurent
http://arxiv.org/abs/1911.10322

VIABLE: Fast Adaptation via Backpropagating Learned Loss
by Feng, Leo and Zintgraf, Luisa and Peng, Bei and Whiteson, Shimon
http://arxiv.org/abs/1911.13159

Decoupling Adaptation from Modeling with Meta-Optimizers for Meta Learning
by Arnold, S{\'e}bastien M R and Iqbal, Shariq and Sha, Fei
http://arxiv.org/abs/1910.13603

TADAM: Task dependent adaptive metric for improved few-shot learning
by Oreshkin, Boris and Rodr{\'\i}guez L{\'o}pez, Pau and Lacoste, Alexandre
http://papers.nips.cc/paper/7352-tadam-task-dependent-adaptive-metric-for-improved-few-shot-learning.pdf

Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks
by Bansal, Trapit and Jha, Rishikesh and McCallum, Andrew
http://arxiv.org/abs/1911.03863

Optimizing Millions of Hyperparameters by Implicit Differentiation
by Lorraine, Jonathan and Vicol, Paul and Duvenaud, David
http://arxiv.org/abs/1911.02590

Meta-data: Characterization of Input Features for Meta-learning
by Castiello, Ciro and Castellano, Giovanna and Fanelli, Anna Maria
http://dx.doi.org/10.1007/11526018_45

Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems
by Mi, Fei and Huang, Minlie and Zhang, Jiyong and Faltings, Boi
http://arxiv.org/abs/1905.05644

Domain Generalization via Model-Agnostic Learning of Semantic Features
by Dou, Qi and Castro, Daniel C and Kamnitsas, Konstantinos and Glocker, Ben
http://arxiv.org/abs/1910.13580

Hierarchical Expert Networks for Meta-Learning
by Hihn, Heinke and Braun, Daniel A
http://arxiv.org/abs/1911.00348

Online Meta-Learning on Non-convex Setting
by Zhuang, Zhenxun and Wang, Yunlong and Yu, Kezi and Lu, Songtao
http://arxiv.org/abs/1910.10196

Learning-to-Learn Stochastic Gradient Descent with Biased Regularization
by Denevi, Giulia and Ciliberto, Carlo and Grazzi, Riccardo and Pontil, Massimiliano
http://arxiv.org/abs/1903.10399

Provable Guarantees for Gradient-Based Meta-Learning
by Khodak, Mikhail and Balcan, Maria-Florina and Talwalkar, Ameet
http://arxiv.org/abs/1902.10644

The TCGA Meta-Dataset Clinical Benchmark
by Samiei, Mandana and W{\"u}rfl, Tobias and Deleu, Tristan and Weiss, Martin and Dutil, Francis and Fevens, Thomas and Boucher, Genevi{`e}ve and Lemieux, Sebastien and Cohen, Joseph Paul
http://arxiv.org/abs/1910.08636

VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning
by Zintgraf, Luisa and Shiarlis, Kyriacos and Igl, Maximilian and Schulze, Sebastian and Gal, Yarin and Hofmann, Katja and Whiteson, Shimon
http://arxiv.org/abs/1910.08348

Meta-Transfer Learning through Hard Tasks
by Sun, Qianru and Liu, Yaoyao and Chen, Zhaozheng and Chua, Tat-Seng and Schiele, Bernt
http://arxiv.org/abs/1910.03648

Model-Agnostic Meta-Learning using Runge-Kutta Methods
by Im, Daniel Jiwoong and Jiang, Yibo and Verma, Nakul
http://arxiv.org/abs/1910.07368

Improving Generalization in Meta Reinforcement Learning using Learned Objectives
by Kirsch, Louis and van Steenkiste, Sjoerd and Schmidhuber, J{\"u}rgen
http://arxiv.org/abs/1910.04098

Generalized Inner Loop Meta-Learning
by Grefenstette, Edward and Amos, Brandon and Yarats, Denis and Htut, Phu Mon and Molchanov, Artem and Meier, Franziska and Kiela, Douwe and Cho, Kyunghyun and Chintala, Soumith
http://arxiv.org/abs/1910.01727

Is Fast Adaptation All You Need?
by Javed, Khurram and Yao, Hengshuai and White, Martha
http://arxiv.org/abs/1910.01705

Deep Reinforcement Learning for Single-Shot Diagnosis and Adaptation in Damaged Robots
by Verma, Shresth and Nair, Haritha S and Agarwal, Gaurav and Dhar, Joydip and Shukla, Anupam
http://arxiv.org/abs/1910.01240

ES-MAML: Simple Hessian-Free Meta Learning
by Song, Xingyou and Gao, Wenbo and Yang, Yuxiang and Choromanski, Krzysztof and Pacchiano, Aldo and Tang, Yunhao
http://arxiv.org/abs/1910.01215

Meta-Q-Learning
by Fakoor, Rasool and Chaudhari, Pratik and Soatto, Stefano and Smola, Alexander J
http://arxiv.org/abs/1910.00125

Efficient meta reinforcement learning via meta goal generation
by Fu, Haotian and Tang, Hongyao and Hao, Jianye
http://arxiv.org/abs/1909.13607

Chameleon: Learning Model Initializations Across Tasks With Different Schemas
by Brinkmeyer, Lukas and Drumond, Rafael Rego and Scholz, Randolf and Grabocka, Josif and Schmidt-Thieme, Lars
http://arxiv.org/abs/1909.13576

Learning Fast Adaptation with Meta Strategy Optimization
by Yu, Wenhao and Tan, Jie and Bai, Yunfei and Coumans, Erwin and Ha, Sehoon
http://arxiv.org/abs/1909.12995

Meta-Inverse Reinforcement Learning with Probabilistic Context Variables
by Yu, Lantao and Yu, Tianhe and Finn, Chelsea and Ermon, Stefano
http://arxiv.org/abs/1909.09314

Modular Meta-Learning with Shrinkage
by Chen, Yutian and Friesen, Abram L and Behbahani, Feryal and Budden, David and Hoffman, Matthew W and Doucet, Arnaud and de Freitas, Nando
http://arxiv.org/abs/1909.05557

Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Estimators for Reinforcement Learning
by Farquhar, Gregory and Whiteson, Shimon and Foerster, Jakob
http://arxiv.org/abs/1909.10549

Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML
by Raghu, Aniruddh and Raghu, Maithra and Bengio, Samy and Vinyals, Oriol
http://arxiv.org/abs/1909.09157

Meta-Learning
by Vanschoren, Joaquin
https://doi.org/10.1007/978-3-030-05318-5_2

Understanding Short-Horizon Bias in Stochastic Meta-Optimization
by Wu, Yuhuai and Ren, Mengye and Liao, Renjie and Grosse, Roger
http://arxiv.org/abs/1803.02021

On First-Order Meta-Learning Algorithms
by Nichol, Alex and Achiam, Joshua and Schulman, John
http://arxiv.org/abs/1803.02999

Towards Understanding Generalization in Gradient-Based Meta-Learning
by Guiroy, Simon and Verma, Vikas and Pal, Christopher
http://arxiv.org/abs/1907.07287
They empirically study the landscape of fast-adaptation in MAML. The most interesting claim is that when meta-overfitting, the loss landscape becomes flatter on test tasks.

On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms
by Fallah, Alireza and Mokhtari, Aryan and Ozdaglar, Asuman
http://arxiv.org/abs/1908.10400

Learning to Learn with Gradients
by Finn, Chelsea
http://learn2learn.net

Acetylcholine and memory
by Hasselmo, M E and Bower, J M
https://www.ncbi.nlm.nih.gov/pubmed/7688162

A THEORY OF META-LEARNING AND PRINCIPLES OF FACILITATION: AN ORGANISMIC PERSPECTIVE
by Maudsley, Donald B
https://uosc.primo.exlibrisgroup.com/discovery/fulldisplay?docid=proquest302999651&context=PC&vid=01USC_INST:01USC&lang=en&search_scope=MyInst_and_CI&adaptor=Primo%20Central&tab=Everything&mode=Basic

THE ROLE OF METALEARNING IN STUDY PROCESSES
by Biggs, J B
http://doi.wiley.com/10.1111/j.2044-8279.1985.tb02625.x

Understanding and correcting pathologies in the training of learned optimizers
by Metz, Luke and Maheswaranathan, Niru and Nixon, Jeremy and Daniel Freeman, C and Sohl-Dickstein, Jascha
http://arxiv.org/abs/1810.10180
Provides many tricks (e.g. split train batch for model \& opt, average gradient estimators) for training differentiable optimizers online. They also have a couple of interesting observations specific to recurrent optimizers.

Learned Optimizers that Scale and Generalize
by Wichrowska, Olga and Maheswaranathan, Niru and Hoffman, Matthew W and Colmenarejo, Sergio Gomez and Denil, Misha and de Freitas, Nando and Sohl-Dickstein, Jascha
http://arxiv.org/abs/1703.04813

Using learned optimizers to make models robust to input noise
by Metz, Luke and Maheswaranathan, Niru and Shlens, Jonathon and Sohl-Dickstein, Jascha and Cubuk, Ekin D
http://arxiv.org/abs/1906.03367

Learning to Optimize Neural Nets
by Li, Ke and Malik, Jitendra
http://arxiv.org/abs/1703.00441

Meta-Learning Update Rules for Unsupervised Representation Learning
by Metz, Luke and Maheswaranathan, Niru and Cheung, Brian and Sohl-Dickstein, Jascha
http://arxiv.org/abs/1804.00222

Learning to Optimize
by Li, Ke and Malik, Jitendra
http://arxiv.org/abs/1606.01885

Learning to learn by gradient descent by gradient descent
by Andrychowicz, M and Denil, M and Gomez, S
http://learn2learn.net

Online Learning Rate Adaptation with Hypergradient Descent
by Baydin, Atilim Gunes and Cornish, Robert and Rubio, David Martinez and Schmidt, Mark and Wood, Frank
http://arxiv.org/abs/1703.04782
They adapt the learning rate of SGD by differentiating the loss of the next parameters w.r.t. the learning rate. They observe that the gradient of the learning rate is simply the inner product of the last two gradients.

Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta
by Sutton, Richard S
http://dx.doi.org/
What's mostly interesting in this paper is the adaptation of delta-bar-delta to the online scenario. The idea of representing the learning rate as an exponential is nice. Also nice to see that the derivation suggests a full-matrix adaptive case.

Gain adaptation beats least squares
by Sutton, Richard S
https://pdfs.semanticscholar.org/7ec8/876f219b3b3d5c894a3f395c89c382029cc5.pdf
This paper extends IDBD as algorithms K1 and K2, but from my quick read, it isn't clear what's the motivation for those modifications. (Seems to work in a `normalized space'', {\a} la natural gradient ?)They do work better.

Local Gain Adaptation in Stochastic Gradient Descent
by Schraudolph, Nicol N
https://pdfs.semanticscholar.org/31a0/b86c3cd04e6539626f34b80db7ff79d23f40.pdf
This algorithm extends IDBD (Sutton) to the non-linear setting. Interestingly, they have a few brief discussionson the difficulties to optimize at the meta-level. (c.f. Meta-level conditioning section.) Overall, it shines light on the ground idea behind IDBD.

TIDBD: Adapting Temporal-difference Step-sizes Through Stochastic Meta-descent
by Kearney, Alex and Veeriah, Vivek and Travnik, Jaden B and Sutton, Richard S and Pilarski, Patrick M
http://arxiv.org/abs/1804.03334

Increased rates of convergence through learning rate adaptation
by Jacobs, Robert A
http://www.sciencedirect.com/science/article/pii/0893608088900032
This paper argues that we need (at least) four ingredients to improve optimization of connectionist networks: 1. each parameter has its own stepsize, 2. stepsizes vary over time, 3. if consecutive gradients of a stepsize have the same sign, the stepsize should be increased, 4. conversely, if the stepsize should be decreased if its gradients have opposite signs. It also proposes to use two improvements: 1. Momentum (i.e. Polyak's heavyball), 2. delta-bar-delta (i.e. learning the stepsize). It has an interesting comment on the difficulty of learning the stepsize, and therefore comes up with a ``hack'' that outperforms momentum.

Meta-descent for Online, Continual Prediction
by Jacobsen, Andrew and Schlegel, Matthew and Linke, Cameron and Degris, Thomas and White, Adam and White, Martha
http://arxiv.org/abs/1907.07751
The idea is to learn the learning rate so as to minimize the norm of the gradient. They argue that for the continual learning setting, this forces the algorithm to stay ``as stable as possible''. No theorems, small-scale (but interesting) experiments.

Adaptation of learning rate parameters
by Sutton, Rich
http://learn2learn.net

Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace
by Lee, Yoonho and Choi, Seungjin
http://arxiv.org/abs/1801.05558

Meta-Learning with Warped Gradient Descent
by Flennerhag, Sebastian and Rusu, Andrei A and Pascanu, Razvan and Yin, Hujun and Hadsell, Raia
http://arxiv.org/abs/1909.00025

Meta-Learning via Learned Loss
by Chebotar, Yevgen and Molchanov, Artem and Bechtle, Sarah and Righetti, Ludovic and Meier, Franziska and Sukhatme, Gaurav
http://arxiv.org/abs/1906.05374
They learn the loss as a NN, and that loss's objective is to maximize the sum of rewards. It is provided a bunch of things, including inputs, outputs, goals.

Meta-Curvature
by Park, Eunbyung and Oliva, Junier B
http://arxiv.org/abs/1902.03356

Alpha MAML: Adaptive Model-Agnostic Meta-Learning
by Behl, Harkirat Singh and Baydin, At{\i}l{\i}m G{\"u}ne{\c s} and Torr, Philip H S
http://arxiv.org/abs/1905.07435
They combine hypergradient and MAML: adapt all learning rates at all times.

Meta-SGD: Learning to Learn Quickly for Few-Shot Learning
by Li, Zhenguo and Zhou, Fengwei and Chen, Fei and Li, Hang
http://arxiv.org/abs/1707.09835

ProMP: Proximal Meta-Policy Search
by Rothfuss, Jonas and Lee, Dennis and Clavera, Ignasi and Asfour, Tamim and Abbeel, Pieter
http://arxiv.org/abs/1810.06784

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
by Finn, Chelsea and Abbeel, Pieter and Levine, Sergey
http://learn2learn.net

Optimization as a model for few-shot learning
by Ravi, Sachin and Larochelle, Hugo
https://openreview.net/pdf?id=rJY0-Kcll

Fast Context Adaptation via Meta-Learning
by Zintgraf, Luisa M and Shiarlis, Kyriacos and Kurin, Vitaly and Hofmann, Katja and Whiteson, Shimon
http://arxiv.org/abs/1810.03642

Meta-Learning with Implicit Gradients
by Rajeswaran, Aravind and Finn, Chelsea and Kakade, Sham and Levine, Sergey
http://arxiv.org/abs/1909.04630

Natural Neural Networks
by Desjardins, Guillaume and Simonyan, Karen and Pascanu, Razvan and Kavukcuoglu, Koray
http://dl.acm.org/citation.cfm?id=2969442.2969471

A Baseline for Few-Shot Image Classification
by Dhillon, Guneet S and Chaudhari, Pratik and Ravichandran, Avinash and Soatto, Stefano
http://arxiv.org/abs/1909.02729

A CLOSER LOOK AT FEW-SHOT CLASSIFICATION
by Chen, Wei-Yu and Liu, Yen-Cheng and Kira, Zsolt
https://openreview.net/pdf?id=HkxLXnAcFQ
Suggests that meta-learning papers haven't been tested against classical baselines. When considering those baselines, they perform better than many of the recent meta-learning techniques.

Meta-learning with differentiable closed-form solvers
by Bertinetto, Luca and Henriques, Joao F and Torr, Philip and Vedaldi, Andrea
https://openreview.net/forum?id=HyxnZh0ct7

Uncertainty in Model-Agnostic Meta-Learning using Variational Inference
by Nguyen, Cuong and Do, Thanh-Toan and Carneiro, Gustavo
http://arxiv.org/abs/1907.11864

Meta-Reinforcement Learning of Structured Exploration Strategies
by Gupta, Abhishek and Mendonca, Russell and Liu, Yuxuan and Abbeel, Pieter and Levine, Sergey
http://arxiv.org/abs/1802.07245

Metalearned Neural Memory
by Munkhdalai, Tsendsuren and Sordoni, Alessandro and Wang, Tong and Trischler, Adam
http://arxiv.org/abs/1907.09720

Accelerated Stochastic Approximation
by Kesten, Harry
https://projecteuclid.org/euclid.aoms/1177706705

Meta-Learning for Black-box Optimization
by Vishnu, T V and Malhotra, Pankaj and Narwariya, Jyoti and Vig, Lovekesh and Shroff, Gautam
http://arxiv.org/abs/1907.06901
They essentially extend the recurrent meta-learning framework in a few ways: 1. Use regret instead of objective improvement as meta-learning objective. 2. Normalize the objective so as to make it play nice with LSTMs. 3. Incorporate domain-constraints, so that the LSTM always outputs feasible solutions. All are described in page 3.

Task Agnostic Continual Learning via Meta Learning
by He, Xu and Sygnowski, Jakub and Galashov, Alexandre and Rusu, Andrei A and Teh, Yee Whye and Pascanu, Razvan
http://arxiv.org/abs/1906.05201

Watch, Try, Learn: Meta-Learning from Demonstrations and Reward
by Zhou, Allan and Jang, Eric and Kappler, Daniel and Herzog, Alex and Khansari, Mohi and Wohlhart, Paul and Bai, Yunfei and Kalakrishnan, Mrinal and Levine, Sergey and Finn, Chelsea
http://arxiv.org/abs/1906.03352

Meta-Learning Representations for Continual Learning
by Javed, Khurram and White, Martha
http://arxiv.org/abs/1905.12588

TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning
by Yoon, Sung Whan and Seo, Jun and Moon, Jaekyun
http://arxiv.org/abs/1905.06549

Meta Reinforcement Learning with Task Embedding and Shared Policy
by Lan, Lin and Li, Zhenguo and Guan, Xiaohong and Wang, Pinghui
http://arxiv.org/abs/1905.06527

Hierarchically Structured Meta-learning
by Yao, Huaxiu and Wei, Ying and Huang, Junzhou and Li, Zhenhui
http://arxiv.org/abs/1905.05301

Curious Meta-Controller: Adaptive Alternation between Model-Based and Model-Free Control in Deep Reinforcement Learning
by Hafez, Muhammad Burhan and Weber, Cornelius and Kerzel, Matthias and Wermter, Stefan
http://arxiv.org/abs/1905.01718

Learning to Learn in Simulation
by Teng, Ervin and Iannucci, Bob
http://arxiv.org/abs/1902.01569

Meta-Learning with Differentiable Convex Optimization
by Lee, Kwonjoon and Maji, Subhransu and Ravichandran, Avinash and Soatto, Stefano
http://arxiv.org/abs/1904.03758

Functional Regularisation for Continual Learning
by Titsias, Michalis K and Schwarz, Jonathan and de G. Matthews, Alexander G and Pascanu, Razvan and Teh, Yee Whye
http://arxiv.org/abs/1901.11356

Learning to Forget for Meta-Learning
by Baik, Sungyong and Hong, Seokil and Lee, Kyoung Mu
http://arxiv.org/abs/1906.05895

Meta-learning of Sequential Strategies
by Ortega, Pedro A and Wang, Jane X and Rowland, Mark and Genewein, Tim and Kurth-Nelson, Zeb and Pascanu, Razvan and Heess, Nicolas and Veness, Joel and Pritzel, Alex and Sprechmann, Pablo and Jayakumar, Siddhant M and McGrath, Tom and Miller, Kevin and Azar, Mohammad and Osband, Ian and Rabinowitz, Neil and Gy{\"o}rgy, Andr{\'a}s and Chiappa, Silvia and Osindero, Simon and Teh, Yee Whye and van Hasselt, Hado and de Freitas, Nando and Botvinick, Matthew and Legg, Shane
http://arxiv.org/abs/1905.03030
This paper essentially provides a theoretical framework to ground the fact that recurrent meta-learning (RL^2, LLGD^2) performs Bayesian inference during adaptation.

Auto-Meta: Automated Gradient Based Meta Learner Search
by Kim, Jaehong and Lee, Sangyeul and Kim, Sungwan and Cha, Moonsu and Lee, Jung Kwon and Choi, Youngduck and Choi, Yongseok and Cho, Dong-Yeon and Kim, Jiwon
http://arxiv.org/abs/1806.06927

Adaptive Gradient-Based Meta-Learning Methods
by Khodak, Mikhail and Florina-Balcan, Maria and Talwalkar, Ameet
http://arxiv.org/abs/1906.02717

Embedded Meta-Learning: Toward more flexible deep-learning models
by Lampinen, Andrew K and McClelland, James L
http://arxiv.org/abs/1905.09950

Modular meta-learning
by Alet, Ferran and Lozano-P{\'e}rez, Tom{\'a}s and Kaelbling, Leslie P
http://arxiv.org/abs/1806.10166

MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records
by Zhang, Xi Sheryl and Tang, Fengyi and Dodge, Hiroko and Zhou, Jiayu and Wang, Fei
http://arxiv.org/abs/1905.03218

Prototypical Networks for Few-shot Learning
by Snell, Jake and Swersky, Kevin and Zemel, Richard S
http://arxiv.org/abs/1703.05175

Meta-learners' learning dynamics are unlike learners'
by Rabinowitz, Neil C
http://arxiv.org/abs/1905.01320

Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity
by Miconi, Thomas and Rawal, Aditya and Clune, Jeff and Stanley, Kenneth O
https://openreview.net/forum?id=r1lrAiA5Ym

Reinforcement Learning, Fast and Slow
by Botvinick, Matthew and Ritter, Sam and Wang, Jane X and Kurth-Nelson, Zeb and Blundell, Charles and Hassabis, Demis
http://dx.doi.org/10.1016/j.tics.2019.02.006

Been There, Done That: Meta-Learning with Episodic Recall
by Ritter, Samuel and Wang, Jane X and Kurth-Nelson, Zeb and Jayakumar, Siddhant M and Blundell, Charles and Pascanu, Razvan and Botvinick, Matthew
http://arxiv.org/abs/1805.09692

Guided Meta-Policy Search
by Mendonca, Russell and Gupta, Abhishek and Kralev, Rosen and Abbeel, Pieter and Levine, Sergey and Finn, Chelsea
http://arxiv.org/abs/1904.00956

Hierarchical Meta Learning
by Zou, Yingtian and Feng, Jiashi
http://arxiv.org/abs/1904.09081

A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms
by Bengio, Yoshua and Deleu, Tristan and Rahaman, Nasim and Ke, Rosemary and Lachapelle, S{\'e}bastien and Bilaniuk, Olexa and Goyal, Anirudh and Pal, Christopher
http://arxiv.org/abs/1901.10912

Generalize Across Tasks: Efficient Algorithms for Linear Representation Learning
by Bullins, Brian and Hazan, Elad and Kalai, Adam and Livni, Roi
http://proceedings.mlr.press/v98/bullins19a.html

Incremental Learning-to-Learn with Statistical Guarantees
by Denevi, Giulia and Ciliberto, Carlo and Stamos, Dimitris and Pontil, Massimiliano
http://arxiv.org/abs/1803.08089

A Model of Inductive Bias Learning
by Baxter, J
http://arxiv.org/abs/1106.0245

Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
by Rakelly, Kate and Zhou, Aurick and Quillen, Deirdre and Finn, Chelsea and Levine, Sergey
http://arxiv.org/abs/1903.08254

Continual Learning with Tiny Episodic Memories
by Chaudhry, Arslan and Rohrbach, Marcus and Elhoseiny, Mohamed and Ajanthan, Thalaiyasingam and Dokania, Puneet K and Torr, Philip H S and Ranzato, Marc'aurelio
http://arxiv.org/abs/1902.10486

Online Meta-Learning
by Finn, Chelsea and Rajeswaran, Aravind and Kakade, Sham and Levine, Sergey
http://arxiv.org/abs/1902.08438

Modulating transfer between tasks in gradient-based meta-learning
by Grant, Erin and Jerfel, Ghassen and Heller, Katherine and Griffiths, Thomas L
https://openreview.net/pdf?id=HyxpNnRcFX

Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning
by Nagabandi, Anusha and Clavera, Ignasi and Liu, Simin and Fearing, Ronald S and Abbeel, Pieter and Levine, Sergey and Finn, Chelsea
http://arxiv.org/abs/1803.11347

Meta-Learning with Latent Embedding Optimization
by Rusu, Andrei A and Rao, Dushyant and Sygnowski, Jakub and Vinyals, Oriol and Pascanu, Razvan and Osindero, Simon and Hadsell, Raia
http://arxiv.org/abs/1807.05960

Learning to Generalize: Meta-Learning for Domain Generalization
by Li, Da and Yang, Yongxin and Song, Yi-Zhe and Hospedales, Timothy M
http://arxiv.org/abs/1710.03463

Some Considerations on Learning to Explore via Meta-Reinforcement Learning
by Stadie, Bradly C and Yang, Ge and Houthooft, Rein and Chen, Xi and Duan, Yan and Wu, Yuhuai and Abbeel, Pieter and Sutskever, Ilya
http://arxiv.org/abs/1803.01118

How to train your MAML
by Antoniou, Antreas and Edwards, Harrison and Storkey, Amos
http://arxiv.org/abs/1810.09502

Bayesian Model-Agnostic Meta-Learning
by Kim, Taesup and Yoon, Jaesik and Dia, Ousmane and Kim, Sungwoong and Bengio, Yoshua and Ahn, Sungjin
http://arxiv.org/abs/1806.03836

Probabilistic Model-Agnostic Meta-Learning
by Finn, Chelsea and Xu, Kelvin and Levine, Sergey
http://arxiv.org/abs/1806.02817

The effects of negative adaptation in Model-Agnostic Meta-Learning
by Deleu, Tristan and Bengio, Yoshua
http://arxiv.org/abs/1812.02159

Memory-based Parameter Adaptation
by Sprechmann, Pablo and Jayakumar, Siddhant M and Rae, Jack W and Pritzel, Alexander and Badia, Adri{`a} Puigdom{`e}nech and Uria, Benigno and Vinyals, Oriol and Hassabis, Demis and Pascanu, Razvan and Blundell, Charles
http://arxiv.org/abs/1802.10542

Deep Meta-Learning: Learning to Learn in the Concept Space
by Zhou, Fengwei and Wu, Bin and Li, Zhenguo
http://arxiv.org/abs/1802.03596

Deep Prior
by Lacoste, Alexandre and Boquet, Thomas and Rostamzadeh, Negar and Oreshkin, Boris and Chung, Wonchang and Krueger, David
http://arxiv.org/abs/1712.05016

Recasting Gradient-Based Meta-Learning as Hierarchical Bayes
by Grant, Erin and Finn, Chelsea and Levine, Sergey and Darrell, Trevor and Griffiths, Thomas
http://arxiv.org/abs/1801.08930

WNGrad: Learn the Learning Rate in Gradient Descent
by Wu, Xiaoxia and Ward, Rachel and Bottou, L{\'e}on
http://arxiv.org/abs/1803.02865

Learning to Learn
by Finn, Chelsea
http://bair.berkeley.edu/blog/2017/07/18/learning-to-learn/