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09/19/2023
Language Modeling Is Compression
It has long been established that predictive models can be transformed i...
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05/26/2023
Randomized Positional Encodings Boost Length Generalization of Transformers
Transformers have impressive generalization capabilities on tasks with a...
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02/06/2023
Memory-Based Meta-Learning on Non-Stationary Distributions
Memory-based meta-learning is a technique for approximating Bayes-optima...
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09/30/2022
Beyond Bayes-optimality: meta-learning what you know you don't know
Meta-training agents with memory has been shown to culminate in Bayes-op...
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07/05/2022
Neural Networks and the Chomsky Hierarchy
Reliable generalization lies at the heart of safe ML and AI. However, un...
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03/23/2022
Your Policy Regularizer is Secretly an Adversary
Policy regularization methods such as maximum entropy regularization are...
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11/04/2021
Model-Free Risk-Sensitive Reinforcement Learning
We extend temporal-difference (TD) learning in order to obtain risk-sens...
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10/20/2021
Shaking the foundations: delusions in sequence models for interaction and control
The recent phenomenal success of language models has reinvigorated machi...
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03/26/2021
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow
In the past decade, model-free reinforcement learning (RL) has provided ...
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03/05/2021
Causal Analysis of Agent Behavior for AI Safety
As machine learning systems become more powerful they also become increa...
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09/11/2019
Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learning
Cumulative entropy regularization introduces a regulatory signal to the ...
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07/26/2019
A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment
Empowerment is an information-theoretic method that can be used to intri...
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06/21/2019
Disentangled Skill Embeddings for Reinforcement Learning
We propose a novel framework for multi-task reinforcement learning (MTRL...
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02/09/2018
Balancing Two-Player Stochastic Games with Soft Q-Learning
Within the context of video games the notion of perfectly rational agent...
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08/06/2017
An Information-Theoretic Optimality Principle for Deep Reinforcement Learning
In this paper, we methodologically address the problem of cumulative rew...
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04/07/2016
Planning with Information-Processing Constraints and Model Uncertainty in Markov Decision Processes
Information-theoretic principles for learning and acting have been propo...
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11/05/2015
Adaptive information-theoretic bounded rational decision-making with parametric priors
Deviations from rational decision-making due to limited computational re...
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12/24/2013
Bounded Rational Decision-Making in Changing Environments
A perfectly rational decision-maker chooses the best action with the hig...
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06/09/2012
