Automated and autonomous experiments in electron and scanning probe microscopy
Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable
part of physics research, with domain applications ranging from theory and materials …
part of physics research, with domain applications ranging from theory and materials …
Causal machine learning: A survey and open problems
Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …
that formalize the data-generation process as a structural causal model (SCM). This …
Multi-Objective Hyperparameter Optimization--An Overview
Hyperparameter optimization constitutes a large part of typical modern machine learning
workflows. This arises from the fact that machine learning methods and corresponding …
workflows. This arises from the fact that machine learning methods and corresponding …
Simulation intelligence: Towards a new generation of scientific methods
The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …
computing, where a motif is an algorithmic method that captures a pattern of computation …
Machine learning for high-throughput experimental exploration of metal halide perovskites
Metal halide perovskites (MHPs) have catapulted to the forefront of energy research due to
the unique combination of high device performance, low materials cost, and facile solution …
the unique combination of high device performance, low materials cost, and facile solution …
Causal deep learning
Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
Approximate allocation matching for structural causal bandits with unobserved confounders
Structural causal bandit provides a framework for online decision-making problems when
causal information is available. It models the stochastic environment with a structural causal …
causal information is available. It models the stochastic environment with a structural causal …
Causal reasoning: Charting a revolutionary course for next-generation ai-native wireless networks
Despite the basic premise that next-generation wireless networks (eg, 6G) will be artificial
intelligence (AI)-native, to date, most existing efforts remain either qualitative or incremental …
intelligence (AI)-native, to date, most existing efforts remain either qualitative or incremental …
Model-based causal Bayesian optimization
How should we intervene on an unknown structural equation model to maximize a
downstream variable of interest? This setting, also known as causal Bayesian optimization …
downstream variable of interest? This setting, also known as causal Bayesian optimization …
Active learning for optimal intervention design in causal models
Sequential experimental design to discover interventions that achieve a desired outcome is
a key problem in various domains including science, engineering and public policy. When …
a key problem in various domains including science, engineering and public policy. When …