Fetching the paper…
Reading the bibliography…
Animals exhibit an innate ability to learn regularities of the world through interaction.
Design of experiments
Fisher, R. A · 1936
Earlier work this paper cites.
Fundamental concepts in the design of experiments
Hicks, C. R · 1964
Earlier work this paper cites.
Modeling by shortest data description
Rissanen, J · 1978
Earlier work this paper cites.
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Rousseeuw, P. J · 1987
Earlier work this paper cites.
One-factor-at-a-time versus designed experiments
Czitrom, V · 1999
Earlier work this paper cites.
A tutorial introduction to the minimum description length principle
Grunwald, P · 2004
Earlier work this paper cites.
A tutorial on the cross-entropy method
De Boer, P.-T., Kroese, D. P., Mannor, S., and Rubinstein, R. Y · 2005
Earlier work this paper cites.
Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts
Schmidhuber, J · 2006
Earlier work this paper cites.
Causality
Pearl, J · 2009
Earlier work this paper cites.
Causal inference using the algorithmic Markov condition
Janzing, D. and Schölkopf, B · 2010
Earlier work this paper cites.
Formal theory of creativity, fun, and intrinsic motivation (1990–2010)
Schmidhuber, J · 2010
Earlier work this paper cites.
Introduction to causal inference
Spirtes, P · 2010
Earlier work this paper cites.
Learning skills from play: artificial curiosity on a katana robot arm
Ngo, H., Luciw, M., Forster, A., and Schmidhuber, J · 2012
Earlier work this paper cites.
On causal and anticausal learning
Schölkopf, B., Janzing, D., Peters, J., Sgouritsa, E., Zhang, K., and Mooij, J. M · 2012
Cited alongside, same era.
Representation learning: A review and new perspectives
Bengio, Y., Courville, A., and Vincent, P · 2013
Cited alongside, same era.
Model predictive control
Camacho, E. F. and Alba, C. B · 2013
Cited alongside, same era.
Graphical causal models
Elwert, F · 2013
Cited alongside, same era.
Inferring latent structures via information inequalities
Chaves, R., Luft, L., Maciel, T., Gross, D., Janzing, D., and Schölkopf, B · 2014
Cited alongside, same era.
Semi-supervised learning with deep generative models
Kingma, D. P., Mohamed, S., Rezende, D. J., and Welling, M · 2014
Cited alongside, same era.
Isolating sources of disentanglement in variational autoencoders
Chen, T. Q., Li, X., Grosse, R. B., and Duvenaud, D. K · 2018
Later among the works it cites.
Stable baselines
Hill, A., Raffin, A., Ernestus, M., Gleave, A., Kanervisto, A., Traore, R., Dhariwal, P., Hesse, C., Klimov, O., Nichol, A., Plappert, M., Radford, A., Schulman, J., Sidor, S., and Wu, Y · 2018
Later among the works it cites.
Kim, H. and Mnih, A · 2018
Later among the works it cites.
Challenging common assumptions in the unsupervised learning of disentangled representations
Locatello, F., Bauer, S., Lucic, M., Rätsch, G., Gelly, S., Schölkopf, B., and Bachem, O · 2018
Later among the works it cites.
Learning independent causal mechanisms
Parascandolo, G., Kilbertus, N., Rojas-Carulla, M., and Schölkopf, B · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Schölkopf, B · 2015
Cited alongside, same era.
Hidden parameter markov decision processes: A semiparametric regression approach for discovering latent task parametrizations
Doshi-Velez, F. and Konidaris, G · 2016
Cited alongside, same era.
Algorithmic independence of initial condition and dynamical law in thermodynamics and causal inference
Janzing, D., Chaves, R., and Schölkopf, B · 2016
Cited alongside, same era.
Soft-dtw: a differentiable loss function for time-series
Cuturi, M. and Blondel, M · 2017
Cited alongside, same era.
Robust and efficient transfer learning with hidden parameter markov decision processes
Killian, T. W., Daulton, S., Konidaris, G., and Doshi-Velez, F · 2017
Cited alongside, same era.
Curiosity-driven exploration by self-supervised prediction
Pathak, D., Agrawal, P., Efros, A. A., and Darrell, T · 2017
Cited alongside, same era.
Direct policy transfer via hidden parameter markov decision processes
Yao, J., Killian, T., Konidaris, G., and Doshi-Velez, F · 2018
Later among the works it cites.
Diva: Domain invariant variational autoencoders
Ilse, M., Tomczak, J. M., Louizos, C., and Welling, M · 2019
Later among the works it cites.
Causality for machine learning
Schölkopf, B · 2019
Later among the works it cites.
Varibad: A very good method for bayes-adaptive deep rl via meta-learning
Zintgraf, L., Shiarlis, K., Igl, M., Schulze, S., Gal, Y., Hofmann, K., and Whiteson, S · 2019
Later among the works it cites.
Causalworld: A robotic manipulation benchmark for causal structure and transfer learning
Ahmed, O., Träuble, F., Goyal, A., Neitz, A., Wüthrich, M., Bengio, Y., Schölkopf, B., and Bauer, S · 2020
Closest in time.
Weakly-supervised disentanglement without compromises
Locatello, F., Poole, B., Rätsch, G., Schölkopf, B., Bachem, O., and Tschannen, M · 2020
Closest in time.
Generalized hidden parameter mdps: Transferable model-based rl in a handful of trials
Perez, C. F., Such, F. P., and Karaletsos, T · 2020
Closest in time.