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09/07/2023
A State Representation for Diminishing Rewards
A common setting in multitask reinforcement learning (RL) demands that a...
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06/23/2023
Prediction under Latent Subgroup Shifts with High-Dimensional Observations
We introduce a new approach to prediction in graphical models with laten...
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05/24/2023
Successor-Predecessor Intrinsic Exploration
Exploration is essential in reinforcement learning, particularly in envi...
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09/13/2022
Unsupervised representational learning with recognition-parametrised probabilistic models
We introduce a new approach to probabilistic unsupervised learning based...
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09/12/2022
Amortised Inference in Structured Generative Models with Explaining Away
A key goal of unsupervised learning is to go beyond density estimation a...
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07/17/2022
Minimum Description Length Control
We propose a novel framework for multitask reinforcement learning based ...
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12/03/2021
Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning
Artificial neural systems trained using reinforcement, supervised, and u...
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09/28/2021
A First-Occupancy Representation for Reinforcement Learning
Both animals and artificial agents benefit from state representations th...
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02/22/2020
Amortised Learning by Wake-Sleep
Models that employ latent variables to capture structure in observed dat...
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06/22/2019
A neurally plausible model learns successor representations in partially observable environments
Animals need to devise strategies to maximize returns while interacting ...
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06/01/2019
Kernel Instrumental Variable Regression
Instrumental variable regression is a strategy for learning causal relat...
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02/12/2019
Learning interpretable continuous-time models of latent stochastic dynamical systems
We develop an approach to learn an interpretable semi-parametric model o...
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07/04/2018
Empirical fixed point bifurcation analysis
In a common experimental setting, the behaviour of a noisy dynamical sys...
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05/28/2018
Flexible and accurate inference and learning for deep generative models
We introduce a new approach to learning in hierarchical latent-variable ...
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11/02/2017
A Universal Marginalizer for Amortized Inference in Generative Models
We consider the problem of inference in a causal generative model where ...
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10/24/2014
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM)
We introduce the Locally Linear Latent Variable Model (LL-LVM), a probab...
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01/23/2013
Regularization and nonlinearities for neural language models: when are they needed?
Neural language models (LMs) based on recurrent neural networks (RNN) ar...
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06/27/2012
