Fetching the paper…
Reading the bibliography…
Robust learning from noisy demonstrations is a practical but highly challenging problem in imitation learning.
Wasserstein adversarial imitation learning
Xiao, H., Herman, M., Wagner, J., Ziesche, S., Etesami, J., and Linh, T. H. (2019) · 1906
Earlier work this paper cites.
Learning from noisy examples
Angluin, D. and Laird, P. (1988) · 1988
Earlier work this paper cites.
ALVINN: an autonomous land vehicle in a neural network
Pomerleau, D. (1988) · 1988
Earlier work this paper cites.
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Puterman, M. L. (1994) · 1994
Earlier work this paper cites.
Combining labeled and unlabeled data with co-training
Blum, A. and Mitchell, T. (1998) · 1998
Earlier work this paper cites.
Is imitation learning the route to humanoid robots?
Schaal, S. (1999) · 1999
Earlier work this paper cites.
Algorithms for inverse reinforcement learning
Ng, A. Y. and Russell, S. J. (2000) · 2000
Earlier work this paper cites.
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., and Friedman, J. (2001) · 2001
Earlier work this paper cites.
Apprenticeship learning using linear programming
Syed, U., Bowling, M. H., and Schapire, R. E. (2008) · 2008
Earlier work this paper cites.
The balanced accuracy and its posterior distribution
Brodersen, K. H., Ong, C. S., Stephan, K. E., and Buhmann, J. M. (2010) · 2010
Earlier work this paper cites.
Semi-Supervised Learning
Chapelle, O., Schlkopf, B., and Zien, A. (2010) · 2010
Earlier work this paper cites.
Modeling interaction via the principle of maximum causal entropy
Ziebart, B. D., Bagnell, J. A., and Dey, A. K. (2010) · 2010
Earlier work this paper cites.
Donut as I do: Learning from failed demonstrations
Grollman, D. H. and Billard, A. (2011) · 2011
Cited alongside, same era.
On the statistical consistency of algorithms for binary classification under class imbalance
Menon, A., Narasimhan, H., Agarwal, S., and Chawla, S. (2013) · 2013
Cited alongside, same era.
Learning with noisy labels
Natarajan, N., Dhillon, I. S., Ravikumar, P. K., and Tewari, A. (2013) · 2013
Cited alongside, same era.
Semi-supervised learning with deep generative models
Kingma, D. P., Mohamed, S., Jimenez Rezende, D., and Welling, M. (2014) · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2015) · 2015
Cited alongside, same era.
Learning with symmetric label noise: The importance of being unhinged
van Rooyen, B., Menon, A., and Williamson, R. C. (2015) · 2015
Cited alongside, same era.
Pytorch implementations of reinforcement learning algorithms
Kostrikov, I. (2018) · 2018
Later among the works it cites.
ROBOTURK: A crowdsourcing platform for robotic skill learning through imitation
Mandlekar, A., Zhu, Y., Garg, A., Booher, J., Spero, M., Tung, A., Gao, J., Emmons, J., Gupta, A., Orbay, E., Savarese, S., and Fei-Fei, L. (2018) · 2018
Later among the works it cites.
Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations
Brown, D. S., Goo, W., Nagarajan, P., and Niekum, S. (2019) · 2019
Later among the works it cites.
On symmetric losses for learning from corrupted labels
Charoenphakdee, N., Lee, J., and Sugiyama, M. (2019) · 2019
Later among the works it cites.
Pybullet, a python module for physics simulation for games, robotics and machine learning
Coumans, E. and Bai, Y. (2016–2019) · 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…
Generative adversarial imitation learning
Ho, J. and Ermon, S. (2016) · 2016
Cited alongside, same era.
Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C. (2017) · 2017
Cited alongside, same era.
Infogail: Interpretable imitation learning from visual demonstrations
Li, Y., Song, J., and Ermon, S. (2017) · 2017
Cited alongside, same era.
Mastering the game of Go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., van den Driessche, G., Graepel, T., and Hassabis, D. (2017) · 2017
Cited alongside, same era.
Scalable trust-region method for deep reinforcement learning using kronecker-factored approximation
Wu, Y., Mansimov, E., Grosse, R. B., Liao, S., and Ba, J. (2017) · 2017
Cited alongside, same era.
Co-teaching: Robust training of deep neural networks with extremely noisy labels
Han, B., Yao, Q., Yu, X., Niu, G., Xu, M., Hu, W., Tsang, I. W., and Sugiyama, M. (2018) · 2018
Cited alongside, same era.
Lu, N., Niu, G., Menon, A. K., and Sugiyama, M. (2019) · 2019
Later among the works it cites.
Provably efficient imitation learning from observation alone
Sun, W., Vemula, A., Boots, B., and Bagnell, D. (2019) · 2019
Later among the works it cites.
Imitation learning from imperfect demonstration
Wu, Y., Charoenphakdee, N., Bao, H., Tangkaratt, V., and Sugiyama, M. (2019) · 2019
Later among the works it cites.
Safe imitation learning via fast bayesian reward inference from preferences
Brown, D., Coleman, R., Srinivasan, R., and Niekum, S. (2020) · 2020
Closest in time.
A divergence minimization perspective on imitation learning methods
Ghasemipour, S. K. S., Zemel, R., and Gu, S. (2020) · 2020
Closest in time.
Normalized loss functions for deep learning with noisy labels
Ma, X., Huang, H., Wang, Y., Romano, S., Erfani, S., and Bailey, J. (2020) · 2020
Closest in time.
Variational imitation learning with diverse-quality demonstrations
Tangkaratt, V., Han, B., Khan, M. E., and Sugiyama, M. (2020) · 2020
Closest in time.