Provably Robust Learning Based Approach

Media Summary: In this video, we demonstrate the six trajectories used to test our proposed safe Neural-ESO: A Dual-Pathway Architecture for Provably Robust Learning-Based Control (RAL 2026) Interest in derivative-free optimization (DFO) and "evolutionary strategies" (ES) has recently surged in the Reinforcement

Provably Robust Learning Based Approach - Detailed Analysis & Overview

In this video, we demonstrate the six trajectories used to test our proposed safe Neural-ESO: A Dual-Pathway Architecture for Provably Robust Learning-Based Control (RAL 2026) Interest in derivative-free optimization (DFO) and "evolutionary strategies" (ES) has recently surged in the Reinforcement ICLR 2020 Towards Trustworthy ML Workshop Talk. Accepted to IEEE International Conference on Robotics and Automation (ICRA) arXiv: Zico Kolter (Carnegie Mellon University) Frontiers of Deep

Stefanie Jegelka, Professor at MIT, presents recent work on Chong You Research Scientist Google NYC Abstract: Recently, over-parameterized models (e.g., deep neural networks) with ... Shie Mannor (Technion) Deep Reinforcement Natasha Jaques tackles a critical problem: 1 billion weekly LLM users face zero guarantees against harmful outputs like ...

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Provably Robust Learning-Based Approach for High-Accuracy Tracking Control of Lagrangian Systems

Neural-ESO: A Dual-Pathway Architecture for Provably Robust Learning-Based Control (RAL 2026)

Provably Robust Blackbox Optimization for Reinforcement Learning

Beyond "provable" robustness: new directions in adversarial robustness

Safe, Robust, and Generalizable Machine Learning for Power Systems

PRIMER: Perception-Aware Robust Learning-based Multiagent Trajectory Planner

Provably Safe Learning-Based Robot Control (Alec Farid, PhD Defense)

Provable Robustness Beyond Bound Propagation

Robust Learning via Robust Optimization - Stefanie Jegelka

Robust Learning by Double Over-Parameterization

USENIX Security '25 - CAMP in the Odyssey: Provably Robust Reinforcement Learning with Certified...

Deep Robust Reinforcement Learning and Regularization

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Provably Robust Learning-Based Approach for High-Accuracy Tracking Control of Lagrangian Systems

Provably Robust Learning-Based Approach for High-Accuracy Tracking Control of Lagrangian Systems

In this video, we demonstrate the six trajectories used to test our proposed safe

Neural-ESO: A Dual-Pathway Architecture for Provably Robust Learning-Based Control (RAL 2026)

Neural-ESO: A Dual-Pathway Architecture for Provably Robust Learning-Based Control (RAL 2026)

Neural-ESO: A Dual-Pathway Architecture for Provably Robust Learning-Based Control (RAL 2026)

Provably Robust Blackbox Optimization for Reinforcement Learning

Provably Robust Blackbox Optimization for Reinforcement Learning

Interest in derivative-free optimization (DFO) and "evolutionary strategies" (ES) has recently surged in the Reinforcement

Beyond "provable" robustness: new directions in adversarial robustness

Beyond "provable" robustness: new directions in adversarial robustness

ICLR 2020 Towards Trustworthy ML Workshop Talk.

Safe, Robust, and Generalizable Machine Learning for Power Systems

Safe, Robust, and Generalizable Machine Learning for Power Systems

Priya Donti, MIT https://priyadonti.com/ Talk Details: ...

PRIMER: Perception-Aware Robust Learning-based Multiagent Trajectory Planner

PRIMER: Perception-Aware Robust Learning-based Multiagent Trajectory Planner

Accepted to IEEE International Conference on Robotics and Automation (ICRA) arXiv: https://arxiv.org/abs/2406.10060 ...

Provably Safe Learning-Based Robot Control (Alec Farid, PhD Defense)

Provably Safe Learning-Based Robot Control (Alec Farid, PhD Defense)

Alec Farid PhD Defense: Jan 25, 2023

Provable Robustness Beyond Bound Propagation

Provable Robustness Beyond Bound Propagation

Zico Kolter (Carnegie Mellon University) https://simons.berkeley.edu/talks/tbd-52 Frontiers of Deep

Robust Learning via Robust Optimization - Stefanie Jegelka

Robust Learning via Robust Optimization - Stefanie Jegelka

Stefanie Jegelka, Professor at MIT, presents recent work on

Robust Learning by Double Over-Parameterization

Robust Learning by Double Over-Parameterization

Chong You Research Scientist Google NYC Abstract: Recently, over-parameterized models (e.g., deep neural networks) with ...

USENIX Security '25 - CAMP in the Odyssey: Provably Robust Reinforcement Learning with Certified...

USENIX Security '25 - CAMP in the Odyssey: Provably Robust Reinforcement Learning with Certified...

CAMP in the Odyssey:

Deep Robust Reinforcement Learning and Regularization

Deep Robust Reinforcement Learning and Regularization

Shie Mannor (Technion) https://simons.berkeley.edu/talks/tbd-226 Deep Reinforcement

Natasha Jaques - Multi-agent RL for Provably Robust LLM Safety [Alignment Workshop]

Natasha Jaques - Multi-agent RL for Provably Robust LLM Safety [Alignment Workshop]

Natasha Jaques tackles a critical problem: 1 billion weekly LLM users face zero guarantees against harmful outputs like ...