b'David Krueger'

research

∙ 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. ∙

research

∙ 07/27/2023

Thinker: Learning to Plan and Act

We propose the Thinker algorithm, a novel approach that enables reinforc...

0 Stephen Chung, et al. ∙

research

∙ 04/19/2023

Investigating the Nature of 3D Generalization in Deep Neural Networks

Visual object recognition systems need to generalize from a set of 2D tr...

3 Shoaib Ahmed Siddiqui, et al. ∙

research

∙ 03/16/2023

Characterizing Manipulation from AI Systems

Manipulation is a common concern in many domains, such as social media, ...

0 Micah Carroll, et al. ∙

research

∙ 03/10/2023

Unifying Grokking and Double Descent

A principled understanding of generalization in deep learning may requir...

0 Xander Davies, et al. ∙

research

∙ 02/20/2023

Harms from Increasingly Agentic Algorithmic Systems

Research in Fairness, Accountability, Transparency, and Ethics (FATE) ha...

0 Alan Chan, et al. ∙

research

∙ 02/03/2023

Blockwise Self-Supervised Learning at Scale

Current state-of-the-art deep networks are all powered by backpropagatio...

4 Shoaib Ahmed Siddiqui, et al. ∙

research

∙ 01/09/2023

On The Fragility of Learned Reward Functions

Reward functions are notoriously difficult to specify, especially for ta...

0 Lev McKinney, et al. ∙

research

∙ 11/27/2022

Domain Generalization for Robust Model-Based Offline Reinforcement Learning

Existing offline reinforcement learning (RL) algorithms typically assume...

8 Alan Clark, et al. ∙

research

∙ 11/15/2022

Mechanistic Mode Connectivity

Neural networks are known to be biased towards learning mechanisms that ...

0 Ekdeep Singh Lubana, et al. ∙

research

∙ 10/26/2022

Broken Neural Scaling Laws

We present a smoothly broken power law functional form that accurately m...

0 Ethan Caballero, et al. ∙

research

∙ 10/06/2022

Towards Out-of-Distribution Adversarial Robustness

Adversarial robustness continues to be a major challenge for deep learni...

98 Adam Ibrahim, et al. ∙

research

∙ 09/27/2022

Defining and Characterizing Reward Hacking

We provide the first formal definition of reward hacking, a phenomenon w...

0 Joar Skalse, et al. ∙

research

∙ 09/20/2022

Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics

Modern machine learning research relies on relatively few carefully cura...

17 Shoaib Ahmed Siddiqui, et al. ∙

research

∙ 12/27/2021

Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models

Learning models that generalize under different distribution shifts in m...

13 Enoch Tetteh, et al. ∙

research

∙ 12/14/2021

Filling gaps in trustworthy development of AI

The range of application of artificial intelligence (AI) is vast, as is ...

7 Shahar Avin, et al. ∙

research

∙ 11/13/2020

Active Reinforcement Learning: Observing Rewards at a Cost

Active reinforcement learning (ARL) is a variant on reinforcement learni...

0 David Krueger, et al. ∙

research

∙ 09/19/2020

Hidden Incentives for Auto-Induced Distributional Shift

Decisions made by machine learning systems have increasing influence on ...

9 David Krueger, et al. ∙

research

∙ 05/30/2020

AI Research Considerations for Human Existential Safety (ARCHES)

Framed in positive terms, this report examines how technical AI research...

0 Andrew Critch, et al. ∙

research

∙ 04/15/2020

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

With the recent wave of progress in artificial intelligence (AI) has com...

0 Miles Brundage, et al. ∙

research

∙ 03/02/2020

Out-of-Distribution Generalization via Risk Extrapolation (REx)

Generalizing outside of the training distribution is an open challenge f...

25 David Krueger, et al. ∙

research

∙ 11/19/2018

Scalable agent alignment via reward modeling: a research direction

One obstacle to applying reinforcement learning algorithms to real-world...

20 Jan Leike, et al. ∙

research

∙ 06/20/2018

Uncertainty in Multitask Transfer Learning

Using variational Bayes neural networks, we develop an algorithm capable...

0 Alexandre Lacoste, et al. ∙

research

∙ 04/03/2018

Neural Autoregressive Flows

Normalizing flows and autoregressive models have been successfully combi...

0 Chin-Wei Huang, et al. ∙

research

∙ 01/31/2018

Nested LSTMs

We propose Nested LSTMs (NLSTM), a novel RNN architecture with multiple ...

0 Joel Ruben Antony Moniz, et al. ∙

research

∙ 12/13/2017

Deep Prior

The recent literature on deep learning offers new tools to learn a rich ...

0 Alexandre Lacoste, et al. ∙

research

∙ 10/13/2017

Bayesian Hypernetworks

We propose Bayesian hypernetworks: a framework for approximate Bayesian ...

0 David Krueger, et al. ∙

research

∙ 06/16/2017

A Closer Look at Memorization in Deep Networks

We examine the role of memorization in deep learning, drawing connection...

0 Devansh Arpit, et al. ∙

research

∙ 06/03/2016

Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations

We propose zoneout, a novel method for regularizing RNNs. At each timest...

0 David Krueger, et al. ∙

research

∙ 11/26/2015

Regularizing RNNs by Stabilizing Activations

We stabilize the activations of Recurrent Neural Networks (RNNs) by pena...

0 David Krueger, et al. ∙

research

∙ 02/13/2014

Zero-bias autoencoders and the benefits of co-adapting features

Regularized training of an autoencoder typically results in hidden unit ...

0 Kishore Konda, et al. ∙

David Krueger

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