Fetching the paper…
Reading the bibliography…
With the rise of stochastic generative models in robot policy learning, end-to-end visuomotor policies are increasingly successful at solving complex tasks by learning from human demonstrations.
C. J. Clopper and E. S. Pearson, “ The Use of Confidence or Fiducial Limits Illustrated in the Case of the Binomial ,” Biometrika , vol. 26, no. 4, pp. 404–413, 1934
1934
Earlier work this paper cites.
Z. Birnbaum and F. H. Tingey, “ One-Sided Confidence Contours for Probability Distribution Functions ,” The Annals of Mathematical Statistics , pp. 592–596, 1951
1951
Earlier work this paper cites.
A. Dvoretzky, J. Kiefer, and J. Wolfowitz, “ Asymptotic Minimax Character of the Sample Distribution Function and of the Classical Multinomial Estimator ,” The Annals of Mathematical Statistics , pp. 642–669, 1956
1956
Earlier work this paper cites.
J. K. Ghosh, “ On the Relation Among Shortest Confidence Intervals of Different Types ,” Calcutta Statistical Association Bulletin , vol. 10, no. 4, pp. 147–152, 1961
1961
Earlier work this paper cites.
J. W. Pratt, “ Length of Confidence Intervals ,” Journal of the American Statistical Association , vol. 56, no. 295, pp. 549–567, 1961
1961
Earlier work this paper cites.
P. Massart, “ The Tight Constant in the Dvoretzky-Kiefer-Wolfowitz Inequality ,” The annals of Probability , pp. 1269–1283, 1990
1990
Earlier work this paper cites.
M. D. d. Edwardes, “ THE EVALUATION OF CONFIDENCE SETS WITH APPLICATION TO BINOMIAL INTERVALS ,” Statistica Sinica , pp. 393–409, 1998
1998
Earlier work this paper cites.
H. Papadopoulos, V. Vovk, and A. Gammerman, “ Regression Conformal Prediction with Nearest Neighbours ,” Journal of Artificial Intelligence Research , vol. 40, pp. 815–840, 2011
2011
Earlier work this paper cites.
S. Levine, C. Finn, T. Darrell, and P. Abbeel, “ End-to-End Training of Deep Visuomotor Policies ,” The Journal of Machine Learning Research , vol. 17, no. 1, pp. 1334–1373, 2016
2016
Earlier work this paper cites.
S. Greenland, S. J. Senn, K. J. Rothman, J. B. Carlin, C. Poole, S. N. Goodman, and D. G. Altman, “ Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations ,” European journal of epidemiology , vol. 31, no. 4, pp. 337–350, 2016
2016
Earlier work this paper cites.
G. Katz, C. Barrett, D. L. Dill, K. Julian, and M. J. Kochenderfer, “ Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks ,” in Computer Aided Verification: 29th International Conference, CAV 2017, Heidelberg, Germany, July 24-28, 2017, Proceedings, Part I 30 . Springer, 2017, pp. 97–117
2017
Earlier work this paper cites.
S. M. Richards, F. Berkenkamp, and A. Krause, “ The Lyapunov Neural Network: Adaptive Stability Certification for Safe Learning of Dynamical Systems ,” in Conference on Robot Learning . PMLR, 2018, pp. 466–476
2018
Earlier work this paper cites.
W. Zhao, J. P. Queralta, and T. Westerlund, “ Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey ,” in 2020 IEEE symposium series on computational intelligence (SSCI) , 2020, pp. 737–744
2020
Earlier work this paper cites.
B. Matthiesen, C. Hellings, E. A. Jorswieck, and W. Utschick, “ Mixed Monotonic Programming for Fast Global Optimization ,” IEEE Transactions on Signal Processing , vol. 68, pp. 2529–2544, 2020
2020
Earlier work this paper cites.
H.-D. Tran, X. Yang, D. Manzanas Lopez, P. Musau, L. V. Nguyen, W. Xiang, S. Bak, and T. T. Johnson, “ NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physical Systems ,” in International Conference on Computer Aided Verification . Springer, 2020, pp. 3–17
2020
Earlier work this paper cites.
2020
Cited alongside, same era.
2021
Cited alongside, same era.
S. Singh, S. M. Richards, V. Sindhwani, J.-J. E. Slotine, and M. Pavone, “ Learning stabilizable nonlinear dynamics with contraction-based regularization ,” The International Journal of Robotics Research , vol. 40, no. 10-11, pp. 1123–1150, 2021
2021
Cited alongside, same era.
D. Sun, S. Jha, and C. Fan, “ Learning Certified Control Using Contraction Metric ,” in Conference on Robot Learning . PMLR, 2021, pp. 1519–1539
2021
Cited alongside, same era.
E. L. Lehmann and J. P. Romano, Testing Statistical Hypotheses . Springer, 2022, vol. 4
2022
Later among the works it cites.
S. R. Howard and A. Ramdas, “ Sequential estimation of quantiles with applications to A/B testing and best-arm identification ,” Bernoulli , vol. 28, no. 3, pp. 1704–1728, 2022
2022
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Corso, R. Moss, M. Koren, R. Lee, and M. Kochenderfer, “ A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems ,” Journal of Artificial Intelligence Research , vol. 72, pp. 377–428, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
M. P. Fay and S. A. Hunsberger, “ Practical valid inferences for the two-sample binomial problem ,” Statistics Surveys , vol. 15, no. none, pp. 72 – 110, 2021. [Online]. Available: https://doi.org/10.1214/21-SS131
2021
Cited alongside, same era.
E. Jang, A. Irpan, M. Khansari, D. Kappler, F. Ebert, C. Lynch, S. Levine, and C. Finn, “ BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning ,” in Conference on Robot Learning . PMLR, 2022, pp. 991–1002
2022
Cited alongside, same era.
P. Florence, C. Lynch, A. Zeng, O. A. Ramirez, A. Wahid, L. Downs, A. Wong, J. Lee, I. Mordatch, and J. Tompson, “ Implicit Behavioral Cloning ,” in Conference on Robot Learning . PMLR, 2022, pp. 158–168
2022
Cited alongside, same era.
2022
Cited alongside, same era.
S. M. Katz, A. L. Corso, C. A. Strong, and M. J. Kochenderfer, “ Verification of Image-Based Neural Network Controllers Using Generative Models ,” Journal of Aerospace Information Systems , vol. 19, no. 9, pp. 574–584, 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2023
Later among the works it cites.
N. Rober, S. M. Katz, C. Sidrane, E. Yel, M. Everett, M. J. Kochenderfer, and J. P. How, “ Backward Reachability Analysis of Neural Feedback Loops: Techniques for Linear and Nonlinear Systems ,” IEEE Open Journal of Control Systems , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
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
Later among the works it cites.
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
Later among the works it cites.
A. Z. Ren, A. Dixit, A. Bodrova, S. Singh, S. Tu, N. Brown, P. Xu, L. Takayama, F. Xia, J. Varley, et al. , “ Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners ,” in Conference on Robot Learning . PMLR, 2023, pp. 661–682
2023
Later among the works it cites.
B. Wu, B. D. Lee, B. Bucher, and N. Matni, “ Uncertainty Aware Deployment of Pre-trained Task Conditioned Imitation Learning Policies ,” in First Workshop on Out-of-Distribution Generalization in Robotics at CoRL 2023 , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2024
Closest in time.