Scalable, Sample Efficient and Interpretable Bayesian Deep Learning

我的工作将集中在EPSRC的“人工智能技术”研究领域。神经网络（Neural Networks，NN）是一种机器学习模型，由于其灵活性和可扩展性，近年来越来越受欢迎。NN最常使用最大后验概率（MAP）参数估计进行训练。该框架为模型权重提供点估计，通常会导致过度拟合，预测过度自信和样本效率低下。此外，NN通常表现得像黑匣子。他们的预测是出了名的难以解释，贝叶斯方法提供了一种解决模型参数过度自信问题的原则性方法。在贝叶斯神经网络（BNN）中，权重点估计被概率分布所取代。预测是通过边际化的权重，考虑所有可能的参数值。在这些模型中，权重空间中的不确定性被转化为预测中的不确定性，为我们提供了一种建模“我们不知道的东西”的方法。不幸的是，精确的推理对于复杂的模型来说往往是棘手的，而近似的推理方法往往依赖于粗略的近似，这在不确定性估计的准确性和可扩展性之间存在权衡。我的目标是为神经网络开发一类新的灵活的近似推理方法，这些方法能够拟合复杂的数据，产生可靠的不确定性估计，并扩展到大型数据集。我想使用这些模型产生的不确定性估计来自动生成非专家可以理解的关于这些模型决策的解释。最后，我想使用这些近似推理方法来减少模型偏差，并构建样本有效的基于模型的强化学习算法。

My work will be focused around the EPSRC's "Artificial intelligence technologies" research area. An abstract can be read bellow.Neural Networks (NN) are a class of machine learning models which have recently soared in popularity due to their flexibility and scalability to large amounts of data. NNs are most commonly trained using Maximum a Posteriori (MAP) parameter estimation. This framework provides point estimates for model weights, often leading to overfitting, overconfidence in predictions and sample inefficiency. Additionally, NNs often behave like black boxes. Their predictions are notoriously difficult to interpret.Bayesian methods provide a principled way of tackling the issue of overconfidence in model parameters. In Bayesian Neural Networks (BNN), weight point estimates are substituted by probability distributions. Predictions are made by marginalising the weights, considering all possible parameter values. In these models, uncertainty in weight space is translated into uncertainty in predictions, giving us a way to model 'what we do not know.' Unfortunately, exact inference is often intractable for complex models and approximate inference methods tend to rely on crude approximations which present a trade-off between accuracy of uncertainty estimates and scalability. My goal is to develop a new class of flexible approximate inference methods for neural networks which are able to fit complex data, produce reliable uncertainty estimates, and scale to large datasets. I want to use the uncertainty estimates produced by these models to automatically generate explanations that are understandable to non-experts about these model's decisions. Finally, I want to use these approximate inference methods to reduce model-bias and build sample-efficient model-based reinforcement learning algorithms.