117 citations · 278 across the 28 of their papers we have counts for
17 papers · 1 filter
Affordances Enable Partial World Modeling with LLMs
Khimya Khetarpal, Gheorghe Comanici, Jonathan Richens +5
Full models of the world require complex knowledge of immense detail. While pre-trained large models have been hypothesized to contain similar knowledge due to extensive pre-traini…
Understanding Prompt Tuning and In-Context Learning via Meta-Learning
Tim Genewein, Li Kevin Wenliang, Jordi Grau-Moya +3
Prompting is one of the main ways to adapt a pretrained model to target tasks. Besides manually constructing prompts, many prompt optimization methods have been proposed in the lit…
Super-Exponential Regret for UCT, AlphaGo and Variants
Laurent Orseau, Remi Munos
We improve the proofs of the lower bounds of Coquelin and Munos (2007) that demonstrate that UCT can have regret (with exp terms) on the -chain…
Learning Universal Predictors
Jordi Grau-Moya, Tim Genewein, Marcus Hutter +8
Meta-learning has emerged as a powerful approach to train neural networks to learn new tasks quickly from limited data. Broad exposure to different tasks leads to versatile represe…
Language Modeling Is Compression
Grégoire Delétang, Anian Ruoss, Paul-Ambroise Duquenne +9
It has long been established that predictive models can be transformed into lossless compressors and vice versa. Incidentally, in recent years, the machine learning community has f…
Levin Tree Search with Context Models
Laurent Orseau, Marcus Hutter, Levi H. S. Lelis
Levin Tree Search (LTS) is a search algorithm that makes use of a policy (a probability distribution over actions) and comes with a theoretical guarantee on the number of expansion…
Memory-Based Meta-Learning on Non-Stationary Distributions
Tim Genewein, Grégoire Delétang, Anian Ruoss +7
Memory-based meta-learning is a technique for approximating Bayes-optimal predictors. Under fairly general conditions, minimizing sequential prediction error, measured by the log l…
Isotuning With Applications To Scale-Free Online Learning
Laurent Orseau, Marcus Hutter
We extend and combine several tools of the literature to design fast, adaptive, anytime and scale-free online learning algorithms. Scale-free regret bounds must scale linearly with…
Avoiding Side Effects By Considering Future Tasks
Victoria Krakovna, Laurent Orseau, Richard Ngo +2
Designing reward functions is difficult: the designer has to specify what to do (what it means to complete the task) as well as what not to do (side effects that should be avoided…
Logarithmic Pruning is All You Need
Laurent Orseau, Marcus Hutter, Omar Rivasplata
The Lottery Ticket Hypothesis is a conjecture that every large neural network contains a subnetwork that, when trained in isolation, achieves comparable performance to the large ne…
Soft-Bayes: Prod for Mixtures of Experts with Log-Loss
Laurent Orseau, Tor Lattimore, Shane Legg
We consider prediction with expert advice under the log-loss with the goal of deriving efficient and robust algorithms. We argue that existing algorithms such as exponentiated grad…
An investigation of model-free planning
Arthur Guez, Mehdi Mirza, Karol Gregor +10
The field of reinforcement learning (RL) is facing increasingly challenging domains with combinatorial complexity. For an RL agent to address these challenges, it is essential that…
Penalizing side effects using stepwise relative reachability
Victoria Krakovna, Laurent Orseau, Ramana Kumar +2
How can we design safe reinforcement learning agents that avoid unnecessary disruptions to their environment? We show that current approaches to penalizing side effects can introdu…
Agents and Devices: A Relative Definition of Agency
Laurent Orseau, Simon McGregor McGill, Shane Legg
According to Dennett, the same system may be described using a `physical' (mechanical) explanatory stance, or using an `intentional' (belief- and goal-based) explanatory stance. Hu…
AI Safety Gridworlds
Jan Leike, Miljan Martic, Victoria Krakovna +5
We present a suite of reinforcement learning environments illustrating various safety properties of intelligent agents. These problems include safe interruptibility, avoiding side…
Thompson Sampling is Asymptotically Optimal in General Environments
Jan Leike, Tor Lattimore, Laurent Orseau +1
We discuss a variant of Thompson sampling for nonparametric reinforcement learning in a countable classes of general stochastic environments. These environments can be non-Markov,…
Online Learning of k-CNF Boolean Functions
Joel Veness, Marcus Hutter
This paper revisits the problem of learning a k-CNF Boolean function from examples in the context of online learning under the logarithmic loss. In doing so, we give a Bayesian int…