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PAC-Bayes Control: Learning Policies that Provably Generalize to Novel
  Environments
v1v2v3v4v5 (latest)

PAC-Bayes Control: Learning Policies that Provably Generalize to Novel Environments

11 June 2018
Anirudha Majumdar
M. Goldstein
Anoopkumar Sonar
ArXiv (abs)PDFHTML

Papers citing "PAC-Bayes Control: Learning Policies that Provably Generalize to Novel Environments"

13 / 13 papers shown
Title
Formal Verification and Control with Conformal Prediction
Formal Verification and Control with Conformal Prediction
Lars Lindemann
Yiqi Zhao
Xinyi Yu
George J. Pappas
Jyotirmoy Deshmukh
1.1K
36
0
31 Aug 2024
Federated reinforcement learning for robot motion planning with
  zero-shot generalization
Federated reinforcement learning for robot motion planning with zero-shot generalization
Zhenyuan Yuan
Siyuan Xu
Minghui Zhu
FedML
208
6
0
20 Mar 2024
PAC-Bayesian Soft Actor-Critic Learning
PAC-Bayesian Soft Actor-Critic LearningSymposium on Advances in Approximate Bayesian Inference (AABI), 2023
Bahareh Tasdighi
Abdullah Akgul
Manuel Haussmann
Kenny Kazimirzak Brink
M. Kandemir
312
4
0
30 Jan 2023
Learning Latent Representations to Co-Adapt to Humans
Learning Latent Representations to Co-Adapt to HumansAutonomous Robots (AR), 2022
Sagar Parekh
Dylan P. Losey
244
13
0
19 Dec 2022
PAC-Bayesian Lifelong Learning For Multi-Armed Bandits
PAC-Bayesian Lifelong Learning For Multi-Armed BanditsData mining and knowledge discovery (DMKD), 2022
H. Flynn
David Reeb
M. Kandemir
Jan Peters
158
8
0
07 Mar 2022
Learning Differentiable Safety-Critical Control using Control Barrier
  Functions for Generalization to Novel Environments
Learning Differentiable Safety-Critical Control using Control Barrier Functions for Generalization to Novel EnvironmentsEuropean Control Conference (ECC), 2022
Hengbo Ma
Bike Zhang
Masayoshi Tomizuka
Koushil Sreenath
276
25
0
04 Jan 2022
Stronger Generalization Guarantees for Robot Learning by Combining
  Generative Models and Real-World Data
Stronger Generalization Guarantees for Robot Learning by Combining Generative Models and Real-World Data
Abhinav Agarwal
Sushant Veer
Allen Z. Ren
Anirudha Majumdar
139
2
0
16 Nov 2021
Learning Provably Robust Motion Planners Using Funnel Libraries
Learning Provably Robust Motion Planners Using Funnel Libraries
Alim Gurgen
Anirudha Majumdar
Sushant Veer
OOD
109
3
0
16 Nov 2021
Task-Driven Detection of Distribution Shifts with Statistical Guarantees
  for Robot Learning
Task-Driven Detection of Distribution Shifts with Statistical Guarantees for Robot LearningIEEE Transactions on robotics (TRO), 2021
Alec Farid
Sushant Veer
Divya Pachisia
Anirudha Majumdar
OODD
167
3
0
25 Jun 2021
How Tight Can PAC-Bayes be in the Small Data Regime?
How Tight Can PAC-Bayes be in the Small Data Regime?Neural Information Processing Systems (NeurIPS), 2021
Andrew Y. K. Foong
W. Bruinsma
David R. Burt
Richard Turner
255
23
0
07 Jun 2021
Generalization Guarantees for Imitation Learning
Generalization Guarantees for Imitation Learning
Allen Z. Ren
Sushant Veer
Anirudha Majumdar
92
1
0
05 Aug 2020
Invariant Policy Optimization: Towards Stronger Generalization in
  Reinforcement Learning
Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement LearningConference on Learning for Dynamics & Control (L4DC), 2020
Anoopkumar Sonar
Vincent Pacelli
Anirudha Majumdar
318
57
0
01 Jun 2020
Probably Approximately Correct Vision-Based Planning using Motion
  Primitives
Probably Approximately Correct Vision-Based Planning using Motion PrimitivesConference on Robot Learning (CoRL), 2020
Sushant Veer
Anirudha Majumdar
3DV
145
22
0
28 Feb 2020
1