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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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07/31/2023
Line Search for Convex Minimization
Golden-section search and bisection search are the two main principled a...
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06/09/2023
Combining a Meta-Policy and Monte-Carlo Planning for Scalable Type-Based Reasoning in Partially Observable Environments
The design of autonomous agents that can interact effectively with other...
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05/26/2023
Levin Tree Search with Context Models
Levin Tree Search (LTS) is a search algorithm that makes use of a policy...
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02/19/2023
Evaluating Representations with Readout Model Switching
Although much of the success of Deep Learning builds on learning good re...
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02/13/2023
Universal Agent Mixtures and the Geometry of Intelligence
Inspired by recent progress in multi-agent Reinforcement Learning (RL), ...
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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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02/06/2023
U-Clip: On-Average Unbiased Stochastic Gradient Clipping
U-Clip is a simple amendment to gradient clipping that can be applied to...
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12/23/2022
Generalization Bounds for Transfer Learning with Pretrained Classifiers
We study the ability of foundation models to learn representations for c...
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10/22/2022
Testing Independence of Exchangeable Random Variables
Given well-shuffled data, can we determine whether the data items are st...
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10/14/2022
Sequential Learning Of Neural Networks for Prequential MDL
Minimum Description Length (MDL) provides a framework and an objective f...
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10/05/2022
Atari-5: Distilling the Arcade Learning Environment down to Five Games
The Arcade Learning Environment (ALE) has become an essential benchmark ...
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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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07/19/2022
Formal Algorithms for Transformers
This document aims to be a self-contained, mathematically precise overvi...
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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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06/02/2022
Uniqueness and Complexity of Inverse MDP Models
What is the action sequence aa'a" that was likely responsible for reachi...
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12/30/2021
On the Role of Neural Collapse in Transfer Learning
We study the ability of foundation models to learn representations for c...
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12/29/2021
Isotuning With Applications To Scale-Free Online Learning
We extend and combine several tools of the literature to design fast, ad...
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12/26/2021
Reducing Planning Complexity of General Reinforcement Learning with Non-Markovian Abstractions
The field of General Reinforcement Learning (GRL) formulates the problem...
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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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10/06/2021
Reward-Punishment Symmetric Universal Intelligence
Can an agent's intelligence level be negative? We extend the Legg-Hutter...
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09/30/2021
Reinforcement Learning with Information-Theoretic Actuation
Reinforcement Learning formalises an embodied agent's interaction with t...
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05/13/2021
Intelligence and Unambitiousness Using Algorithmic Information Theory
Algorithmic Information Theory has inspired intractable constructions of...
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02/17/2021
Fully General Online Imitation Learning
In imitation learning, imitators and demonstrators are policies for pick...
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02/08/2021
Learning Curve Theory
Recently a number of empirical "universal" scaling law papers have been ...
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12/18/2020
Exact Reduction of Huge Action Spaces in General Reinforcement Learning
The reinforcement learning (RL) framework formalizes the notion of learn...
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11/18/2020
Counterfactual Credit Assignment in Model-Free Reinforcement Learning
Credit assignment in reinforcement learning is the problem of measuring ...
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10/23/2020
A Combinatorial Perspective on Transfer Learning
Human intelligence is characterized not only by the capacity to learn co...
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07/30/2020
On Representing (Anti)Symmetric Functions
Permutation-invariant, -equivariant, and -covariant functions and anti-s...
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06/22/2020
Logarithmic Pruning is All You Need
The Lottery Ticket Hypothesis is a conjecture that every large neural ne...
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06/15/2020
Pessimism About Unknown Unknowns Inspires Conservatism
If we could define the set of all bad outcomes, we could hard-code an ag...
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06/05/2020
Curiosity Killed the Cat and the Asymptotically Optimal Agent
Reinforcement learners are agents that learn to pick actions that lead t...
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02/21/2020
Online Learning in Contextual Bandits using Gated Linear Networks
We introduce a new and completely online contextual bandit algorithm cal...
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09/30/2019
Gated Linear Networks
This paper presents a family of backpropagation-free neural architecture...
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08/13/2019
Reward Tampering Problems and Solutions in Reinforcement Learning: A Causal Influence Diagram Perspective
Can an arbitrarily intelligent reinforcement learning agent be kept unde...
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07/11/2019
Fairness without Regret
A popular approach of achieving fairness in optimization problems is by ...
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05/29/2019
Asymptotically Unambitious Artificial General Intelligence
General intelligence, the ability to solve arbitrary solvable problems, ...
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05/28/2019
Conditions on Features for Temporal Difference-Like Methods to Converge
The convergence of many reinforcement learning (RL) algorithms with line...
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03/04/2019
Strong Asymptotic Optimality in General Environments
Reinforcement Learning agents are expected to eventually perform well. T...
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11/09/2018
Performance Guarantees for Homomorphisms Beyond Markov Decision Processes
Most real-world problems have huge state and/or action spaces. Therefore...
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05/03/2018
AGI Safety Literature Review
The development of Artificial General Intelligence (AGI) promises to be ...
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08/13/2017
A Game-Theoretic Analysis of the Off-Switch Game
The off-switch game is a game theoretic model of a highly intelligent ro...
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06/25/2017
Count-Based Exploration in Feature Space for Reinforcement Learning
We introduce a new count-based optimistic exploration algorithm for Rein...
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05/30/2017
Universal Reinforcement Learning Algorithms: Survey and Experiments
Many state-of-the-art reinforcement learning (RL) algorithms typically a...
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05/23/2017
Reinforcement Learning with a Corrupted Reward Channel
No real-world reward function is perfect. Sensory errors and software bu...
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04/12/2016
Loss Bounds and Time Complexity for Speed Priors
This paper establishes for the first time the predictive performance of ...
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02/25/2016
Thompson Sampling is Asymptotically Optimal in General Environments
We discuss a variant of Thompson sampling for nonparametric reinforcemen...
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10/19/2015
On the Computability of AIXI
How could we solve the machine learning and the artificial intelligence ...
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10/16/2015
Bad Universal Priors and Notions of Optimality
A big open question of algorithmic information theory is the choice of t...
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09/09/2015
