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Found 16 papers, 9 papers with code
Towards Optimizing Human-Centric Objectives in AI-Assisted Decision-Making With Offline Reinforcement Learning
Across two experiments (N=316 and N=964), our results demonstrated that people interacting with policies optimized for accuracy achieve significantly better accuracy -- and even human-AI complementarity -- compared to those interacting with any other type of AI support.
Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks
Many important behavior changes are frictionful; they require individuals to expend effort over a long period with little immediate gratification.
Accuracy-Time Tradeoffs in AI-Assisted Decision Making under Time Pressure
The precise benefit can depend on both the user and task.
Modeling Mobile Health Users as Reinforcement Learning Agents
Mobile health (mHealth) technologies empower patients to adopt/maintain healthy behaviors in their daily lives, by providing interventions (e. g. push notifications) tailored to the user's needs.
Differentially private partitioned variational inference
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.
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning
Variational inference (VI) has become the method of choice for fitting many modern probabilistic models.
Collapsed Variational Bounds for Bayesian Neural Networks
Recent interest in learning large variational Bayesian Neural Networks (BNNs) has been partly hampered by poor predictive performance caused by underfitting, and their performance is known to be very sensitive to the prior over weights.
Knowledge-Adaptation Priors
Humans and animals have a natural ability to quickly adapt to their surroundings, but machine-learning models, when subjected to changes, often require a complete retraining from scratch.
Efficient Low Rank Gaussian Variational Inference for Neural Networks
Bayesian neural networks are enjoying a renaissance driven in part by recent advances in variational inference (VI).
Generalized Variational Continual Learning
One strand of research has used probabilistic regularization for continual learning, with two of the main approaches in this vein being Online Elastic Weight Consolidation (Online EWC) and Variational Continual Learning (VCL).
Combining Variational Continual Learning with FiLM Layers
The standard architecture for continual learning is a multi-headed neural network, which has shared body parameters and task-specific heads.
Continual Deep Learning by Functional Regularisation of Memorable Past
Continually learning new skills is important for intelligent systems, yet standard deep learning methods suffer from catastrophic forgetting of the past.
Differentially Private Federated Variational Inference
This setting is known as federated learning, in which privacy is a key concern.
Practical Deep Learning with Bayesian Principles
Importantly, the benefits of Bayesian principles are preserved: predictive probabilities are well-calibrated, uncertainties on out-of-distribution data are improved, and continual-learning performance is boosted.
Improving and Understanding Variational Continual Learning
In the continual learning setting, tasks are encountered sequentially.
Partitioned Variational Inference: A unified framework encompassing federated and continual learning
Second, the granularity of the updates e. g. whether the updates are local to each data point and employ message passing or global.
