Adaptive interventions for both accuracy and time in AI-assisted human decision making

In settings where users are both time-pressured and need high accuracy, such as doctors working in Emergency Rooms, we want to provide AI assistance that both increases accuracy and reduces time. However, different types of AI assistance have different benefits: some reduce time taken while increasing overreliance on AI, while others do the opposite. We therefore want to adapt what AI assistance we show depending on various properties (of the question and of the user) in order to best tradeoff our two objectives. We introduce a study where users have to prescribe medicines to aliens, and use it to explore the potential for adapting AI assistance. We find evidence that it is beneficial to adapt our AI assistance depending on the question, leading to good tradeoffs between time taken and accuracy. Future work would consider machine-learning algorithms (such as reinforcement learning) to automatically adapt quickly

Differentially private partitioned variational inference

Learning a privacy-preserving model from sensitive data which are distributed across multiple devices is an increasingly important problem. The problem is often formulated in the federated learning context, with the aim of learning a single global model while keeping the data distributed. Moreover, Bayesian learning is a popular approach for modelling, since it naturally supports reliable uncertainty estimates. However, Bayesian learning is generally intractable even with centralised non-private data and so approximation techniques such as variational inference are a necessity. Variational inference has recently been extended to the non-private federated learning setting via the partitioned variational inference algorithm. For privacy protection, the current gold standard is called differential privacy. Differential privacy guarantees privacy in a strong, mathematically clearly defined sense. In this paper, we present differentially private partitioned variational inference, the first general framework for learning a variational approximation to a Bayesian posterior distribution in the federated learning setting while minimising the number of communication rounds and providing differential privacy guarantees for data subjects. We propose three alternative implementations in the general framework, one based on perturbing local optimisation runs done by individual parties, and two based on perturbing updates to the global model (one using a version of federated averaging, the second one adding virtual parties to the protocol), and compare their properties both theoretically and empirically.Comment: Published in TMLR 04/2023: https://openreview.net/forum?id=55Bcghgic

InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis

In this paper, we present InstructABSA, Aspect Based Sentiment Analysis (ABSA) using the instruction learning paradigm for all ABSA subtasks: Aspect Term Extraction (ATE), Aspect Term Sentiment Classification (ATSC), and Joint Task modeling. Our method introduces positive, negative, and neutral examples to each training sample, and instruction tunes the model (Tk-Instruct) for each ABSA subtask, yielding significant performance improvements. Experimental results on the Sem Eval 2014, 15, and 16 datasets demonstrate that InstructABSA outperforms the previous state-of-the-art (SOTA) approaches on all three ABSA subtasks (ATE, ATSC, and Joint Task) by a significant margin, outperforming 7x larger models. In particular, InstructABSA surpasses the SOTA on the Rest14 ATE subtask by 7.31% points, Rest15 ATSC subtask by and on the Lapt14 Joint Task by 8.63% points. Our results also suggest a strong generalization ability to new domains across all three subtasksComment: 4 pages, 2 figures, 5 tables, 5 appendix page

Probabilistic Continual Learning using Neural Networks

Neural networks are being increasingly used in society due to their strong performance at a large scale. They excel when they have access to all data at once, requiring multiple passes through the data. However, standard deep-learning techniques are unable to continually adapt as the environment changes: either they forget old data or they fail to sufficiently adapt to new data. This limitation is a major barrier to applications in many real-world settings, where the environment is often changing, and also in stark contrast to humans, who continuously learn over their lifetimes. The study of learning systems in these settings is called continual learning: data examples arrive sequentially and predictions must be made online. In this thesis we present new algorithms for continual learning using neural networks. We use the probabilistic approach, which maintains a distribution over beliefs, naturally handling continual learning by recursively updating from priors to posteriors. Although previous work has been limited by approximations to this idealised scheme, we scale our probabilistic algorithms to large-data settings and show strong empirical performance. We also theoretically analyse why our algorithms perform well in continual learning. We start with a variational approximation over neural network weights in Chapter 3. Previous weight-prior algorithms converge slowly, and we speed up convergence by using natural-gradient updates, allowing us to scale to large-data settings such as ImageNet for the first time. However, we find there is still room for improving continual learning performance. We argue that ultimately we are only interested in model outputs, and this motivates us to view neural networks in function-space and regularise their outputs directly in Chapter 4. We approximate a term in the variational objective with its function-space alternative, leading to FROMP. FROMP identifies and regularises on a few memorable past examples to avoid forgetting, and performs very well on existing continual learning benchmarks. However, we find that FROMP is not exact in simple settings such as Generalised Linear Models (GLMs). We fix this in Chapter 5 with a method called Knowledge-adaptation priors (K-priors), a generalisation of FROMP and weight-priors that can be exact on GLMs. K-priors achieve quick and accurate adaptation across many adaptation tasks, including adding data (as in continual learning) but also removing data, changing the regulariser, and changing the model. We use K-priors to provide insight into why our previous methods achieve good performance, and we suggest improvements to them. Overall, in this thesis we provide a comprehensive probabilistic framework for continual learning using neural networks, and provide thorough evaluation of instances of this framework

Differentially private partitioned variational inference

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