Fetching the paper…
Reading the bibliography…
We develop a novel approach to conformal prediction when the target task has limited data available for training.
Bayesian Learning for Neural Networks
Neal, R. M · 1996
Earlier work this paper cites.
Ridge regression confidence machine
Nouretdinov, I., Melluish, T., and Vovk, V · 2001
Earlier work this paper cites.
Predicting good probabilities with supervised learning
Niculescu-Mizil, A. and Caruana, R · 2005
Earlier work this paper cites.
Algorithmic Learning in a Random World
Vovk, V., Gammerman, A., and Shafer, G · 2005
Earlier work this paper cites.
The tradeoffs of large scale learning
Bottou, L. and Bousquet, O · 2008
Earlier work this paper cites.
Inductive conformal prediction: Theory and application to neural networks
Papadopoulos, H · 2008
Earlier work this paper cites.
A tutorial on conformal prediction
Shafer, G. and Vovk, V · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Practical variational inference for neural networks
Graves, A · 2011
Earlier work this paper cites.
Calibrating predictive model estimates to support personalized medicine
Jiang, X., Osl, M., Kim, J., and Ohno-Machado, L · 2012
Earlier work this paper cites.
Conditional validity of inductive conformal predictors
Vovk, V · 2012
Earlier work this paper cites.
Efficiency comparison of unstable transductive and inductive conformal classifiers
Linusson, H., Johansson, U., Boström, H., and Löfström, T · 2014
Earlier work this paper cites.
GloVe: Global vectors for word representation
Pennington, J., Socher, R., and Manning, C · 2014
Earlier work this paper cites.
Modifications to p-values of conformal predictors
Carlsson, L., Ahlberg, E., Boström, H., Johansson, U., and Linusson, H · 2015
Earlier work this paper cites.
Probabilistic backpropagation for scalable learning of bayesian neural networks
Hernández-Lobato, J. M. and Adams, R. P · 2015
Earlier work this paper cites.
Handling small calibration sets in mondrian inductive conformal regressors
Johansson, U., Ahlberg, E., Boström, H., Carlsson, L., Linusson, H., and Sönströd, C · 2015
Earlier work this paper cites.
Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B · 2015
Cited alongside, same era.
Concrete problems in ai safety
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., and Mané, D · 2016
Cited alongside, same era.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
Cited alongside, same era.
Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., kavukcuoglu, k., and Wierstra, D · 2016
Cited alongside, same era.
Towards a neural statistician
Edwards, H. and Storkey, A · 2017
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
Meta-learning with differentiable closed-form solvers
Bertinetto, L., Henriques, J. F., Torr, P., and Vedaldi, A · 2019
Later among the works it cites.
Distributional conformal prediction
Chernozhukov, V., Wuthrich, K., and Zhu, Y · 2019
Later among the works it cites.
Conformalized quantile regression
Romano, Y., Patterson, E., and Candes, E · 2019
Later among the works it cites.
Conformal prediction under covariate shift
Tibshirani, R. J., Foygel Barber, R., Candes, E., and Ramdas, A · 2019
Later among the works it cites.
Analyzing learned molecular representations for property prediction
Yang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., Guzman-Perez, A., Hopper, T., Kelley, B., Mathea, M., Palmer, A., Settels, V., Jaakkola, T., Jensen, K., and Barzilay, R · 2019
Later among the works it cites.
Pitfalls of in-domain uncertainty estimation and ensembling in deep learning
Ashukha, A., Lyzhov, A., Molchanov, D., and Vetrov, D · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R · 2017
Cited alongside, same era.
Deep sets
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J · 2017
Cited alongside, same era.
FewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation
Han, X., Zhu, H., Yu, P., Wang, Z., Yao, Y., Liu, Z., and Sun, M · 2018
Cited alongside, same era.
To trust or not to trust a classifier
Jiang, H., Kim, B., Guan, M., and Gupta, M · 2018
Cited alongside, same era.
Later among the works it cites.
Few-shot text classification with distributional signatures
Bao, Y., Wu, M., Chang, S., and Barzilay, R · 2020
Later among the works it cites.
Distribution free, risk controlling prediction sets
Bates, S., Angelopoulos, A. N., Lei, L., Malik, J., and Jordan, M. I · 2020
Later among the works it cites.
Knowing what you know: valid confidence sets in multiclass and multilabel prediction
Cauchois, M., Gupta, S., and Duchi, J · 2020
Later among the works it cites.
Uncertainty quantification using neural networks for molecular property prediction
Hirschfeld, L., Swanson, K., Yang, K., Barzilay, R., and Coley, C. W · 2020
Later among the works it cites.
Adaptive, distribution-free prediction intervals for deep networks
Kivaranovic, D., Johnson, K. D., and Leeb, H · 2020
Later among the works it cites.
With malice toward none: Assessing uncertainty via equalized coverage
Romano, Y., Barber, R. F., Sabatti, C., and Candès, E · 2020
Later among the works it cites.
Generalizing from a few examples: A survey on few-shot learning
Wang, Y., Yao, Q., Kwok, J. T., and Ni, L. M · 2020
Later among the works it cites.
Uncertainty sets for image classifiers using conformal prediction
Angelopoulos, A. N., Bates, S., Malik, J., and Jordan, M. I · 2021
Closest in time.
Efficient conformal prediction via cascaded inference with expanded admission
Fisch, A., Schuster, T., Jaakkola, T., and Barzilay, R · 2021
Closest in time.