13 research outputs found
Neural Variational Inference For Estimating Uncertainty in Knowledge Graph Embeddings
Recent advances in Neural Variational Inference allowed for a renaissance in
latent variable models in a variety of domains involving high-dimensional data.
While traditional variational methods derive an analytical approximation for
the intractable distribution over the latent variables, here we construct an
inference network conditioned on the symbolic representation of entities and
relation types in the Knowledge Graph, to provide the variational
distributions. The new framework results in a highly-scalable method. Under a
Bernoulli sampling framework, we provide an alternative justification for
commonly used techniques in large-scale stochastic variational inference, which
drastically reduce training time at a cost of an additional approximation to
the variational lower bound. We introduce two models from this highly scalable
probabilistic framework, namely the Latent Information and Latent Fact models,
for reasoning over knowledge graph-based representations. Our Latent
Information and Latent Fact models improve upon baseline performance under
certain conditions. We use the learnt embedding variance to estimate predictive
uncertainty during link prediction, and discuss the quality of these learnt
uncertainty estimates. Our source code and datasets are publicly available
online at
https://github.com/alexanderimanicowenrivers/Neural-Variational-Knowledge-Graphs.Comment: Accepted at IJCAI 19 Neural-Symbolic Learning and Reasoning Worksho
Sauté RL: Almost Surely Safe Reinforcement Learning Using State Augmentation
Satisfying safety constraints almost surely (or with probability one) can be critical for the deployment of Reinforcement Learning (RL) in real-life applications. For example, plane landing and take-off should ideally occur with probability one. We address the problem by introducing Safety Augmented (Saute) Markov Decision Processes (MDPs), where the safety constraints are eliminated by augmenting them into the state-space and reshaping the objective. We show that Saute MDP satisfies the Bellman equation and moves us closer to solving Safe RL with constraints satisfied almost surely. We argue that Saute MDP allows viewing the Safe RL problem from a different perspective enabling new features. For instance, our approach has a plug-and-play nature, i.e., any RL algorithm can be "Sauteed”. Additionally, state augmentation allows for policy generalization across safety constraints. We finally show that Saute RL algorithms can outperform their state-of-the-art counterparts when constraint satisfaction is of high importance
An Empirical Study of Assumptions in Bayesian Optimisation
Inspired by the increasing desire to efficiently tune machine learning
hyper-parameters, in this work we rigorously analyse conventional and
non-conventional assumptions inherent to Bayesian optimisation. Across an
extensive set of experiments we conclude that: 1) the majority of
hyper-parameter tuning tasks exhibit heteroscedasticity and non-stationarity,
2) multi-objective acquisition ensembles with Pareto-front solutions
significantly improve queried configurations, and 3) robust acquisition
maximisation affords empirical advantages relative to its non-robust
counterparts. We hope these findings may serve as guiding principles, both for
practitioners and for further research in the field