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Found 17 papers, 12 papers with code
C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory
Generative Adversarial Imitation Learning (GAIL) trains a generative policy to mimic a demonstrator.
Coalitional Bargaining via Reinforcement Learning: An Application to Collaborative Vehicle Routing
Our contribution is that our decentralised approach is both scalable and considers the self-interested nature of companies.
Fair collaborative vehicle routing: A deep multi-agent reinforcement learning approach
Our contribution is that we are the first to consider both the route allocation problem and gain sharing problem simultaneously - without access to the expensive characteristic function.
TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play
Multi-agent football poses an unsolved challenge in AI research.
Imitating Human Behaviour with Diffusion Models
This paper studies their application as observation-to-action models for imitating human behaviour in sequential environments.
DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization
We propose Diversity-Guided Policy Optimization (DGPO), an on-policy algorithm that discovers multiple strategies for solving a given task.
Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis
To offer theoretical insight into our algorithm, we show firstly that it can be interpreted as a form of expectation-maximisation, and secondly that it exhibits a desirable `self-correcting' property.
Bayesian Autoencoders: Analysing and Fixing the Bernoulli likelihood for Out-of-Distribution Detection
After an autoencoder (AE) has learnt to reconstruct one dataset, it might be expected that the likelihood on an out-of-distribution (OOD) input would be low.
Understanding Softmax Confidence and Uncertainty
It is often remarked that neural networks fail to increase their uncertainty when predicting on data far from the training distribution.
Counter-Strike Deathmatch with Large-Scale Behavioural Cloning
This paper describes an AI agent that plays the popular first-person-shooter (FPS) video game `Counter-Strike; Global Offensive' (CSGO) from pixel input.
Structured Weight Priors for Convolutional Neural Networks
This paper explores the benefits of adding structure to weight priors.
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks
Secondly, and more generally, we analyse the fixed-point dynamics of iterated kernels corresponding to a broad range of activation functions.
Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions
A simple, flexible approach to creating expressive priors in Gaussian process (GP) models makes new kernels from a combination of basic kernels, e. g. summing a periodic and linear kernel can capture seasonal variation with a long term trend.
Bayesian Neural Network Ensembles
Ensembles of neural networks (NNs) have long been used to estimate predictive uncertainty; a small number of NNs are trained from different initialisations and sometimes on differing versions of the dataset.
Uncertainty in Neural Networks: Approximately Bayesian Ensembling
Ensembling NNs provides an easily implementable, scalable method for uncertainty quantification, however, it has been criticised for not being Bayesian.
Bayesian Inference with Anchored Ensembles of Neural Networks, and Application to Exploration in Reinforcement Learning
The use of ensembles of neural networks (NNs) for the quantification of predictive uncertainty is widespread.
High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach
This paper considers the generation of prediction intervals (PIs) by neural networks for quantifying uncertainty in regression tasks.
