Progress in nanorobotics for advancing biomedicine
Nanorobotics, which has long been a fantasy in the realm of science fiction, is now a reality
due to the considerable developments in diverse fields including chemistry, materials …
due to the considerable developments in diverse fields including chemistry, materials …
Prior knowledge elicitation: The past, present, and future
Prior Knowledge Elicitation: The Past, Present, and Future Page 1 Bayesian Analysis (2024)
19, Number 4, pp. 1129–1161 Prior Knowledge Elicitation: The Past, Present, and Future ∗ …
19, Number 4, pp. 1129–1161 Prior Knowledge Elicitation: The Past, Present, and Future ∗ …
A survey of domain knowledge elicitation in applied machine learning
Eliciting knowledge from domain experts can play an important role throughout the machine
learning process, from correctly specifying the task to evaluating model results. However …
learning process, from correctly specifying the task to evaluating model results. However …
Human-in-the-loop assisted de novo molecular design
A de novo molecular design workflow can be used together with technologies such as
reinforcement learning to navigate the chemical space. A bottleneck in the workflow that …
reinforcement learning to navigate the chemical space. A bottleneck in the workflow that …
Amortized Bayesian Experimental Design for Decision-Making
Many critical decisions, such as personalized medical diagnoses and product pricing, are
made based on insights gained from designing, observing, and analyzing a series of …
made based on insights gained from designing, observing, and analyzing a series of …
AI for Science: an emerging agenda
This report documents the programme and the outcomes of Dagstuhl Seminar 22382"
Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling". Today's …
Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling". Today's …
Knowledge extraction via decentralized knowledge graph aggregation
In many industrial manufacturing processes, human operators play a central role when it
comes to parameterizing the involved machinery and dealing with errors in the process …
comes to parameterizing the involved machinery and dealing with errors in the process …
Bayesian optimization augmented with actively elicited expert knowledge
Bayesian optimization (BO) is a well-established method to optimize black-box functions
whose direct evaluations are costly. In this paper, we tackle the problem of incorporating …
whose direct evaluations are costly. In this paper, we tackle the problem of incorporating …
A decision-theoretic approach for model interpretability in Bayesian framework
A salient approach to interpretable machine learning is to restrict modeling to simple
models. In the Bayesian framework, this can be pursued by restricting the model structure …
models. In the Bayesian framework, this can be pursued by restricting the model structure …
Approximate Bayesian computation with domain expert in the loop
Approximate Bayesian computation (ABC) is a popular likelihood-free inference method for
models with intractable likelihood functions. As ABC methods usually rely on comparing …
models with intractable likelihood functions. As ABC methods usually rely on comparing …