Fetching the paper…
Reading the bibliography…
This paper focuses on the problem of detecting and reacting to changes in the distribution of a sensorimotor controller's observables.
R. Koenker and G. Bassett Jr, “Regression quantiles,” Econometrica: journal of the Econometric Society , pp. 33–50, 1978
1978
Earlier work this paper cites.
A. P. Dawid, “The well-calibrated bayesian,” Journal of the American Statistical Association , vol. 77, no. 379, pp. 605–610, 1982
1982
Earlier work this paper cites.
H. Papadopoulos, K. Proedrou, V. Vovk, and A. Gammerman, “Inductive confidence machines for regression,” in Machine Learning: European Conference on Machine Learning , 2002, pp. 345–356
2002
Earlier work this paper cites.
V. Vovk, A. Gammerman, and G. Shafer, Algorithmic Learning in a Random World . Springer, 2005
2005
Earlier work this paper cites.
J. M. Hernández-Lobato and R. Adams, “Probabilistic backpropagation for scalable learning of bayesian neural networks,” in International Conference on Machine Learning , 2015, pp. 1861–1869
2015
Earlier work this paper cites.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski et al. , “Human-level control through deep reinforcement learning,” nature , vol. 518, no. 7540, pp. 529–533, 2015
2015
Earlier work this paper cites.
Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in international conference on machine learning . PMLR, 2016, pp. 1050–1059
2016
Earlier work this paper cites.
O. Ian, “Risk versus uncertainty in deep learning: Bayes, bootstrap and the dangers of dropout,” in Advances in Neural Information Processing Systems Workshops , 2016
2016
Earlier work this paper cites.
A. Kendall and Y. Gal, “What uncertainties do we need in bayesian deep learning for computer vision?” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
B. Lakshminarayanan, A. Pritzel, and C. Blundell, “Simple and scalable predictive uncertainty estimation using deep ensembles,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
J. Lei, M. G’Sell, A. Rinaldo, R. J. Tibshirani, and L. Wasserman, “Distribution-free predictive inference for regression,” Journal of the American Statistical Association , vol. 113, no. 523, pp. 1094–1111, 2018
2018
Earlier work this paper cites.
N. Sünderhauf, O. Brock, W. Scheirer, R. Hadsell, D. Fox, J. Leitner, B. Upcroft, P. Abbeel, W. Burgard, M. Milford, and P. Corke, “The limits and potentials of deep learning for robotics,” Int. Journ. on Robotics Research , vol. 37, no. 4-5, pp. 405–420, 2018
2018
Earlier work this paper cites.
G. Kahn, A. Villaflor, B. Ding, P. Abbeel, and S. Levine, “Self-supervised deep reinforcement learning with generalized computation graphs for robot navigation,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) , 2018
2018
Earlier work this paper cites.
K. Chua, R. Calandra, R. McAllister, and S. Levine, “Deep reinforcement learning in a handful of trials using probabilistic dynamics models,” in Advances in Neural Information Processing Systems , 2018, pp. 4754–4765
2018
Earlier work this paper cites.
E. Leurent, “An environment for autonomous driving decision-making,” https://github.com/eleurent/highway-env, 2018
2018
Cited alongside, same era.
Y. Romano, E. Patterson, and E. J. Candès, “Conformalized quantile regression,” in Advances in Neural Information Processing Systems , 2019
2019
Cited alongside, same era.
E. Kaufmann, M. Gehrig, P. Foehn, R. Ranftl, A. Dosovitskiy, V. Koltun, and D. Scaramuzza, “Beauty and the beast: Optimal methods meet learning for drone racing,” in IEEE International Conference on Robotics and Automation (ICRA) , 2019
2019
Cited alongside, same era.
N. Tagasovska and D. Lopez-Paz, “Single-model uncertainties for deep learning,” in Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Cited alongside, same era.
O. Bastani, V. Gupta, C. Jung, G. Noarov, R. Ramalingam, and A. Roth, “Practical adversarial multivalid conformal prediction,” Advances in Neural Information Processing Systems , vol. 35, pp. 29 362–29 373, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
H. Yang and M. Pavone, “Object pose estimation with statistical guarantees: Conformal keypoint detection and geometric uncertainty propagation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 8947–8958
2023
Closest in time.
2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
A. Loquercio, M. Segu, and D. Scaramuzza, “A general framework for uncertainty estimation in deep learning,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 3153–3160, 2020
2020
Cited alongside, same era.
A. Fisch, T. Schuster, T. S. Jaakkola, and R. Barzilay, “Efficient conformal prediction via cascaded inference with expanded admission,” in International Conference on Learning Representations , 2021. [Online]. Available: https://openreview.net/forum?id=tnSo6VRLmT
2021
Cited alongside, same era.
A. N. Angelopoulos, S. Bates, J. Malik, and M. I. Jordan, “Uncertainty sets for image classifiers using conformal prediction,” in International Conference on Learning Representations (ICLR) , 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
I. Gibbs and E. J. Candès, “Adaptive conformal inference under distribution shift,” in Advances in Neural Information Processing Systems , 2021
2021
Cited alongside, same era.
R. Luo, S. Zhao, J. Kuck, B. Ivanovic, S. Savarese, E. Schmerling, and M. Pavone, “Sample-efficient safety assurances using conformal prediction,” in International Workshop on the Algorithmic Foundations of Robotics . Springer, 2022, pp. 149–169
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Closest in time.
2023
Closest in time.
2023
Closest in time.
A. Dixit, L. Lindemann, S. X. Wei, M. Cleaveland, G. J. Pappas, and J. W. Burdick, “Adaptive conformal prediction for motion planning among dynamic agents,” in Learning for Dynamics and Control Conference . PMLR, 2023, pp. 300–314
2023
Closest in time.
L. Lindemann, M. Cleaveland, G. Shim, and G. J. Pappas, “Safe planning in dynamic environments using conformal prediction,” IEEE Robotics and Automation Letters , 2023
2023
Closest in time.
2023
Closest in time.
J. Lekeufack, A. N. Angelopoulos, A. Bajcsy, M. I. Jordan, and J. Malik, “Conformal decision theory: Safe autonomous decisions from imperfect predictions,” Published online at https://conformal-decision.github.io/static/pdf/submission.pdf , 2023
2023
Closest in time.
A. Loquercio, A. Kumar, and J. Malik, “Learning visual locomotion with cross-modal supervision,” in IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 7295–7302
2023
Closest in time.
2023
Closest in time.