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Milan Ganai
Observed name:
Ganai, Milan
Institution:
Stanford University
Observed institutions:
University of California San Diego,
Stanford University
ORCID:
0009-0001-1825-3650
Observed ORCID:
0009-0001-1825-3650
OpenAlex ID:
A5031297903
Hamilton-Jacobi Reachability in Reinforcement Learning: A Survey
2024
·
Milan Ganai
, Sicun Gao
, et al.
·
IEEE Open Journal of Control Systems
·
20
Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates
2024
·
Mandal, Udayan
, Amir, Guy
, et al.
·
arXiv (Cornell University)
·
13
PDF
Iterative Reachability Estimation for Safe Reinforcement Learning
2023
·
Milan Ganai
, Zheng Gong
, et al.
·
neural information processing systems
·
11
PDF
VISTAQA: Benchmarking Joint Visual Question Answering and Pixel-Level Evidence
2026
·
Azadani, Mozhgan Nasr
, Wang, Yimu
, et al.
·
arXiv (Cornell University)
·
5
PDF
Safe and Reliable Training of Learning-Based Aerospace Controllers
2024
·
Udayan Mandal
, Guy Amir
, et al.
·
IEEE/AIAA Digital Avionics Systems Conference (DASC)
·
5
PDF
Learning Stabilization Control from Observations by Learning Lyapunov-like Proxy Models
2023
·
Milan Ganai
, Chiaki Hirayama
, et al.
·
Proceedings - IEEE International Conference on Robotics and Automation/Proceedings
·
5
PDF
Self-Supervised Bootstrapping of Action-Predictive Embodied Reasoning
2026
·
Milan Ganai
, Katie Luo
, et al.
·
Robotics: Science and Systems
·
2
PDF
DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?
2026
·
Dao, Jadelynn
, Ganai, Milan
, et al.
·
arXiv (Cornell University)
·
2
PDF
Target-independent XLA optimization using Reinforcement Learning
2023
·
Ganai, Milan
, Li, Haichen
, et al.
·
arXiv (Cornell University)
·
1
PDF
Reasoning as a Distribution: Generative Plan Latents for Vision-Language-Action Models
2026
·
Cercola, Matteo
, Luo, Katie Z
, et al.
·
arXiv (Cornell University)
PDF
Bootstrapping Embodied Reasoning
2026
·
Ganai, Milan
·
Zenodo (CERN European Organization for Nuclear Research)
Identifying Merged Tracks in Dense Environments with Machine Learning
2019
·
McCormack, Patrick
, Ganai, Milan
, et al.
·
arXiv (Cornell University)
PDF
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Works count:
14
Citation count:
70
H-index:
5
I10-index:
3
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