David Lindner | DeepAI

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∙ 08/08/2023

RLHF-Blender: A Configurable Interactive Interface for Learning from Diverse Human Feedback

To use reinforcement learning from human feedback (RLHF) in practical ap...

0 Yannick Metz, et al. ∙

∙ 07/27/2023

Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reinforcement learning from human feedback (RLHF) is a technique for tra...

0 Stephen Casper, et al. ∙

∙ 05/25/2023

Learning Safety Constraints from Demonstrations with Unknown Rewards

We propose Convex Constraint Learning for Reinforcement Learning (CoCoRL...

0 David Lindner, et al. ∙

∙ 01/12/2023

Tracr: Compiled Transformers as a Laboratory for Interpretability

Interpretability research aims to build tools for understanding machine ...

0 David Lindner, et al. ∙

∙ 10/03/2022

Red-Teaming the Stable Diffusion Safety Filter

Stable Diffusion is a recent open-source image generation model comparab...

0 Javier Rando, et al. ∙

∙ 07/18/2022

Active Exploration for Inverse Reinforcement Learning

Inverse Reinforcement Learning (IRL) is a powerful paradigm for inferrin...

0 David Lindner, et al. ∙

∙ 06/27/2022

Humans are not Boltzmann Distributions: Challenges and Opportunities for Modelling Human Feedback and Interaction in Reinforcement Learning

Reinforcement learning (RL) commonly assumes access to well-specified re...

0 David Lindner, et al. ∙

∙ 06/10/2022

Interactively Learning Preference Constraints in Linear Bandits

We study sequential decision-making with known rewards and unknown const...

0 David Lindner, et al. ∙

∙ 01/24/2022

Scalable Safe Exploration for Global Optimization of Dynamical Systems

Learning optimal control policies directly on physical systems is challe...

0 Bhavya Sukhija, et al. ∙

∙ 06/02/2021

Addressing the Long-term Impact of ML Decisions via Policy Regret

Machine Learning (ML) increasingly informs the allocation of opportuniti...

0 David Lindner, et al. ∙

∙ 04/08/2021

Learning What To Do by Simulating the Past

Since reward functions are hard to specify, recent work has focused on l...

0 David Lindner, et al. ∙

∙ 02/24/2021

Information Directed Reward Learning for Reinforcement Learning

For many reinforcement learning (RL) applications, specifying a reward i...

0 David Lindner, et al. ∙

∙ 01/29/2021

Challenges for Using Impact Regularizers to Avoid Negative Side Effects

Designing reward functions for reinforcement learning is difficult: besi...

0 David Lindner, et al. ∙

∙ 06/30/2019

Detecting Spiky Corruption in Markov Decision Processes

Current reinforcement learning methods fail if the reward function is im...

0 Jason Mancuso, et al. ∙

∙ 03/27/2019

Sensing Social Media Signals for Cryptocurrency News

The ability to track and monitor relevant and important news in real-tim...

0 Johannes Beck, et al. ∙

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