Fetching the paper…
Reading the bibliography…
We give novel algorithms for multi-task and lifelong linear bandits with shared representation.
Tight regret bounds for infinite-armed linear contextual bandits
Yingkai Li, Yining Wang, and Yuan Zhou · 1905
Earlier work this paper cites.
Probability inequalities for sums of bounded random variables
Wassily Hoeffding · 1963
Earlier work this paper cites.
Multitask learning
Rich Caruana · 1997
Earlier work this paper cites.
A model of inductive bias learning
Jonathan Baxter · 2000
Earlier work this paper cites.
Using confidence bounds for exploitation-exploration trade-offs
Peter Auer · 2002
Earlier work this paper cites.
Provable meta-learning of linear representations
Nilesh Tripuraneni, Chi Jin, and Michael I Jordan · 2002
Earlier work this paper cites.
Exploiting task relatedness for multiple task learning
Shai Ben-David and Reba Schuller · 2003
Earlier work this paper cites.
A framework for learning predictive structures from multiple tasks and unlabeled data
Rie Kubota Ando and Tong Zhang · 2005
Earlier work this paper cites.
Bounds for linear multi-task learning
Andreas Maurer · 2006
Earlier work this paper cites.
On the theory of transfer learning: The importance of task diversity
Nilesh Tripuraneni, Michael I Jordan, and Chi Jin · 2006
Earlier work this paper cites.
Stochastic linear optimization under bandit feedback
Varsha Dani, Thomas P. Hayes, and Sham M. Kakade · 2008
Earlier work this paper cites.
Transfer learning for reinforcement learning domains: A survey
Matthew E Taylor and Peter Stone · 2009
Earlier work this paper cites.
Linear algorithms for online multitask classification
Giovanni Cavallanti, Nicolo Cesa-Bianchi, and Claudio Gentile · 2010
Earlier work this paper cites.
Linearly parameterized bandits
Paat Rusmevichientong and John N Tsitsiklis · 2010
Earlier work this paper cites.
Improved algorithms for linear stochastic bandits
Yasin Abbasi-Yadkori, Dávid Pál, and Csaba Szepesvári · 2011
Earlier work this paper cites.
Contextual bandits with linear payoff functions
Wei Chu, Lihong Li, Lev Reyzin, and Robert Schapire · 2011
Earlier work this paper cites.
Transfer from multiple mdps
Alessandro Lazaric and Marcello Restelli · 2011
Earlier work this paper cites.
Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
Earlier work this paper cites.
Joint collaborative representation with multitask learning for hyperspectral image classification
Jiayi Li, Hongyan Zhang, Liangpei Zhang, Xin Huang, and Lefei Zhang · 2014
Earlier work this paper cites.
Actor-mimic: Deep multitask and transfer reinforcement learning
Emilio Parisotto, Jimmy Lei Ba, and Ruslan Salakhutdinov · 2015
Earlier work this paper cites.
Massively multitask networks for drug discovery
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, and Vijay Pande · 2015
Cited alongside, same era.
Andrei A Rusu, Sergio Gomez Colmenarejo, Caglar Gulcehre, Guillaume Desjardins, James Kirkpatrick, Razvan Pascanu, Volodymyr Mnih, Koray Kavukcuoglu, and Raia Hadsell · 2015
Cited alongside, same era.
An introduction to matrix concentration inequalities
Joel A Tropp et al · 2015
Cited alongside, same era.
Impact of representation learning in linear bandits
Jiaqi Yang, Wei Hu, Jason D Lee, and Simon S Du · 2015
Cited alongside, same era.
Regret bounds for lifelong learning
Pierre Alquier, The Tien Mai, and Massimiliano Pontil · 2016
Online meta-learning
Chelsea Finn, Aravind Rajeswaran, Sham Kakade, and Sergey Levine · 2019
Later among the works it cites.
Multi-task deep reinforcement learning with popart
Matteo Hessel, Hubert Soyer, Lasse Espeholt, Wojciech Czarnecki, Simon Schmitt, and Hado van Hasselt · 2019
Later among the works it cites.
Bilinear bandits with low-rank structure
Kwang-Sung Jun, Rebecca Willett, Stephen Wright, and Robert Nowak · 2019
Later among the works it cites.
Adaptive gradient-based meta-learning methods
Mikhail Khodak, Maria-Florina Balcan, and Ameet Talwalkar · 2019
Later among the works it cites.
Stochastic linear bandits with hidden low rank structure
Sahin Lale, Kamyar Azizzadenesheli, Anima Anandkumar, and Babak Hassibi · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
A theoretical framework for deep transfer learning
Tomer Galanti, Lior Wolf, and Tamir Hazan · 2016
Cited alongside, same era.
Decoding multitask dqn in the world of minecraft
Lydia T Liu, Urun Dogan, and Katja Hofmann · 2016
Cited alongside, same era.
The benefit of multitask representation learning
Andreas Maurer, Massimiliano Pontil, and Bernardino Romera-Paredes · 2016
Cited alongside, same era.
Multi-task learning for contextual bandits
Aniket Anand Deshmukh, Urun Dogan, and Clay Scott · 2017
Cited alongside, same era.
Darla: Improving zero-shot transfer in reinforcement learning
Irina Higgins, Arka Pal, Andrei Rusu, Loic Matthey, Christopher Burgess, Alexander Pritzel, Matthew Botvinick, Charles Blundell, and Alexander Lerchner · 2017
Cited alongside, same era.
Risk bounds for transferring representations with and without fine-tuning
Daniel McNamara and Maria-Florina Balcan · 2017
Cited alongside, same era.
Distral: Robust multitask reinforcement learning
Yee Teh, Victor Bapst, Wojciech M Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, and Razvan Pascanu · 2017
Cited alongside, same era.
Kwonjoon Lee, Subhransu Maji, Avinash Ravichandran, and Stefano Soatto · 2019
Later among the works it cites.
Towards understanding the importance of shortcut connections in residual networks
Tianyi Liu, Minshuo Chen, Mo Zhou, Simon S Du, Enlu Zhou, and Tuo Zhao · 2019
Later among the works it cites.
Provable representation learning for imitation learning via bi-level optimization
Sanjeev Arora, Simon S Du, Sham Kakade, Yuping Luo, and Nikunj Saunshi · 2020
Later among the works it cites.
Sharing knowledge in multi-task deep reinforcement learning
Carlo D’Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, and Jan Peters · 2020
Later among the works it cites.
Few-shot learning via learning the representation, provably
Simon S Du, Wei Hu, Sham M Kakade, Jason D Lee, and Qi Lei · 2020
Later among the works it cites.
Low-rank generalized linear bandit problems
Yangyi Lu, Amirhossein Meisami, and Ambuj Tewari · 2020
Later among the works it cites.
Provable lifelong learning of representations
Xinyuan Cao, Weiyang Liu, and Santosh S Vempala · 2021
Later among the works it cites.
Near-optimal representation learning for linear bandits and linear rl
Jiachen Hu, Xiaoyu Chen, Chi Jin, Lihong Li, and Liwei Wang · 2021
Later among the works it cites.
Optimal gradient-based algorithms for non-concave bandit optimization
Baihe Huang, Kaixuan Huang, Sham M Kakade, Jason D Lee, Qi Lei, Runzhe Wang, and Jiaqi Yang · 2021
Later among the works it cites.
Tor Lattimore and Botao Hao · 2021
Later among the works it cites.
On the power of multitask representation learning in linear mdp
Rui Lu, Gao Huang, and Simon S Du · 2021
Later among the works it cites.
Joint learning of linear time-invariant dynamical systems
Aditya Modi, Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, and George Michailidis · 2021
Later among the works it cites.
Sample efficient linear meta-learning by alternating minimization
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli, and Sewoong Oh · 2021
Later among the works it cites.
Non-stationary representation learning in sequential linear bandits
Yuzhen Qin, Tommaso Menara, Samet Oymak, ShiNung Ching, and Fabio Pasqualetti · 2022
Closest in time.