
Gated Linear Networks
This paper presents a family of backpropagationfree neural architecture...
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Strong Asymptotic Optimality in General Environments
Reinforcement Learning agents are expected to eventually perform well. T...
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Fairness without Regret
A popular approach of achieving fairness in optimization problems is by ...
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Asymptotically Unambitious Artificial General Intelligence
General intelligence, the ability to solve arbitrary solvable problems, ...
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Conditions on Features for Temporal DifferenceLike Methods to Converge
The convergence of many reinforcement learning (RL) algorithms with line...
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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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Performance Guarantees for Homomorphisms Beyond Markov Decision Processes
Most realworld problems have huge state and/or action spaces. Therefore...
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Online Learning in Contextual Bandits using Gated Linear Networks
We introduce a new and completely online contextual bandit algorithm cal...
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Metric State Space Reinforcement Learning for a VisionCapable Mobile Robot
We address the problem of autonomously learning controllers for visionc...
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Gradientbased Reinforcement Planning in PolicySearch Methods
We introduce a learning method called "gradientbased reinforcement plan...
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CountBased Exploration in Feature Space for Reinforcement Learning
We introduce a new countbased optimistic exploration algorithm for Rein...
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Universal Reinforcement Learning Algorithms: Survey and Experiments
Many stateoftheart reinforcement learning (RL) algorithms typically a...
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Reinforcement Learning with a Corrupted Reward Channel
No realworld reward function is perfect. Sensory errors and software bu...
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Fitness Uniform Optimization
In evolutionary algorithms, the fitness of a population increases with t...
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Fitness Uniform Deletion: A Simple Way to Preserve Diversity
A commonly experienced problem with population based optimisation method...
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MarketBased Reinforcement Learning in Partially Observable Worlds
Unlike traditional reinforcement learning (RL), marketbased RL is in pr...
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On the Computability of AIXI
How could we solve the machine learning and the artificial intelligence ...
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Bad Universal Priors and Notions of Optimality
A big open question of algorithmic information theory is the choice of t...
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A Topological Approach to Metaheuristics: Analytical Results on the BFS vs. DFS Algorithm Selection Problem
Search is a central problem in artificial intelligence, and BFS and DFS ...
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Compress and Control
This paper describes a new informationtheoretic policy evaluation techn...
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Loss Bounds and Time Complexity for Speed Priors
This paper establishes for the first time the predictive performance of ...
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Robust Feature Selection by Mutual Information Distributions
Mutual information is widely used in artificial intelligence, in a descr...
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Extreme State Aggregation Beyond MDPs
We consider a Reinforcement Learning setup where an agent interacts with...
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Thompson Sampling is Asymptotically Optimal in General Environments
We discuss a variant of Thompson sampling for nonparametric reinforcemen...
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Optimistic Agents are Asymptotically Optimal
We use optimism to introduce generic asymptotically optimal reinforcemen...
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Probabilities on Sentences in an Expressive Logic
Automated reasoning about uncertain knowledge has many applications. One...
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Can Intelligence Explode?
The technological singularity refers to a hypothetical scenario in which...
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One Decade of Universal Artificial Intelligence
The first decade of this century has seen the nascency of the first math...
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Principles of Solomonoff Induction and AIXI
We identify principles characterizing Solomonoff Induction by demands on...
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Feature Reinforcement Learning In Practice
Following a recent surge in using historybased methods for resolving pe...
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Asymptotically Optimal Agents
Artificial general intelligence aims to create agents capable of learnin...
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Time Consistent Discounting
A possibly immortal agent tries to maximise its summed discounted reward...
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Algorithmic Randomness as Foundation of Inductive Reasoning and Artificial Intelligence
This article is a brief personal account of the past, present, and futur...
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Concentration and Confidence for Discrete Bayesian Sequence Predictors
Bayesian sequence prediction is a simple technique for predicting future...
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A Monte Carlo AIXI Approximation
This paper introduces a principled approach for the design of a scalable...
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Open Problems in Universal Induction & Intelligence
Specialized intelligent systems can be found everywhere: finger print, h...
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Feature Reinforcement Learning: Part I: Unstructured MDPs
Generalpurpose, intelligent, learning agents cycle through sequences of...
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SelfOptimizing and ParetoOptimal Policies in General Environments based on BayesMixtures
The problem of making sequential decisions in unknown probabilistic envi...
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A Theory of Universal Artificial Intelligence based on Algorithmic Complexity
Decision theory formally solves the problem of rational agents in uncert...
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Model Selection by Loss Rank for Classification and Unsupervised Learning
Hutter (2007) recently introduced the loss rank principle (LoRP) as a ge...
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New Error Bounds for Solomonoff Prediction
Solomonoff sequence prediction is a scheme to predict digits of binary s...
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Discrete MDL Predicts in Total Variation
The Minimum Description Length (MDL) principle selects the model that ha...
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A Novel IlluminationInvariant Loss for Monocular 3D Pose Estimation
The problem of identifying the 3D pose of a known object from a given 2D...
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Predictive Hypothesis Identification
While statistics focusses on hypothesis testing and on estimating (prope...
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On Universal Prediction and Bayesian Confirmation
The Bayesian framework is a wellstudied and successful framework for in...
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The Loss Rank Principle for Model Selection
We introduce a new principle for model selection in regression and class...
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3D Model Assisted Image Segmentation
The problem of segmenting a given image into coherent regions is importa...
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Featureless 2D3D Pose Estimation by Minimising an IlluminationInvariant Loss
The problem of identifying the 3D pose of a known object from a given 2D...
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Matching 2D Ellipses to 3D Circles with Application to Vehicle Pose Estimation
Finding the threedimensional representation of all or a part of a scene...
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A GameTheoretic Analysis of the OffSwitch Game
The offswitch game is a game theoretic model of a highly intelligent ro...
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