Fetching the paper…
Reading the bibliography…
Importance-weighted risk minimization is a key ingredient in many machine learning algorithms for causal inference, domain adaptation, class imbalance, and off-policy reinforcement learning.
A generalization of sampling without replacement from a finite universe
Horvitz, D. G. and Thompson, D. J · 1952
Earlier work this paper cites.
Methods of reducing sample size in monte carlo computations
Kahn, H. and Marshall, A. W · 1953
Earlier work this paper cites.
Eligibility traces for off-policy policy evaluation
Precup, D · 2000
Earlier work this paper cites.
Improving predictive inference under covariate shift by weighting the log-likelihood function
Shimodaira, H · 2000
Earlier work this paper cites.
Automatically constructing a corpus of sentential paraphrases
Dolan, B. and Brockett, C · 2005
Earlier work this paper cites.
Curriculum learning
Bengio, Y., Louradour, J., Collobert, R., and Weston, J · 2009
Earlier work this paper cites.
Importance weighted active learning
Beygelzimer, A., Dasgupta, S., and Langford, J · 2009
Earlier work this paper cites.
Covariate shift by kernel mean matching
Gretton, A., Smola, A. J., Huang, J., Schmittfull, M., Borgwardt, K. M., and Schölkopf, B · 2009
Earlier work this paper cites.
Probabilistic graphical models: principles and techniques
Koller, D., Friedman, N., and Bach, F · 2009
Earlier work this paper cites.
Active learning literature survey
Settles, B · 2010
Earlier work this paper cites.
Weighted importance sampling for off-policy learning with linear function approximation
Mahmood, A. R., van Hasselt, H. P., and Sutton, R. S · 2014
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
Cited alongside, same era.
Importance weighted autoencoders
Burda, Y., Grosse, R., and Salakhutdinov, R · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
Cited alongside, same era.
Self-paced curriculum learning
Jiang, L., Meng, D., Zhao, Q., Shan, S., and Hauptmann, A · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
Cited alongside, same era.
Counterfactual risk minimization: Learning from logged bandit feedback
Swaminathan, A. and Joachims, T · 2015
Cited alongside, same era.
Estimating individual treatment effect: generalization bounds and algorithms
Shalit, U., Johansson, F. D., and Sontag, D · 2017
Later among the works it cites.
The implicit bias of gradient descent on separable data
Soudry, D., Hoffer, E., and Srebro, N · 2017
Later among the works it cites.
Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O · 2017
Later among the works it cites.
BERT: pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M., Lee, K., and Toutanova, K · 2018
Closest in time.
Implicit bias of gradient descent on linear convolutional networks
Gunasekar, S., Lee, J., Soudry, D., and Srebro, N · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Tsc-dl: Unsupervised trajectory segmentation of multi-modal surgical demonstrations with deep learning
Murali, A., Garg, A., Krishnan, S., Pokorny, F. T., Abbeel, P., Darrell, T., and Goldberg, K · 2016
Cited alongside, same era.
Simulation and the Monte Carlo method
Rubinstein, R. Y. and Kroese, D. P · 2016
Cited alongside, same era.
Prioritized experience replay
Schaul, T., Quan, J., Antonoglou, I., and Silver, D · 2016
Cited alongside, same era.
Teacher-student curriculum learning
Matiisen, T., Oliver, A., Cohen, T., and Schulman, J · 2017
Cited alongside, same era.
Deep learning with logged bandit feedback
Joachims, T., Swaminathan, A., and Rijke, M. d · 2018
Closest in time.
Learning from noisy singly-labeled data
Khetan, A., Lipton, Z. C., and Anandkumar, A · 2018
Closest in time.
Detecting and correcting for label shift with black box predictors
Lipton, Z. C., Wang, Y.-X., and Smola, A · 2018
Closest in time.
Pytorch pretrained bert
Wolf, T. and Sanh, V · 2018
Closest in time.
Regularized learning for domain adaptation under label shifts
Azizzadenesheli, K., Liu, A., Yang, F., and Anandkumar, A · 2019
Closest in time.
Addressing sample inefficiency and reward bias in inverse reinforcement learning
Kostrikov, I., Agrawal, K. K., Levine, S., and Tompson, J · 2019
Closest in time.