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pro vyhledávání: '"Lee, Ciaran M"'
Understanding how galaxies form and evolve is at the heart of modern astronomy. With the advent of large-scale surveys and simulations, remarkable progress has been made in the last few decades. Despite this, the physical processes behind the phenome
Externí odkaz:
http://arxiv.org/abs/2412.02439
Estimating causal effects is vital for decision making. In standard causal effect estimation, treatments are usually binary- or continuous-valued. However, in many important real-world settings, treatments can be structured, high-dimensional objects,
Externí odkaz:
http://arxiv.org/abs/2411.19245
Synthetic control (SC) models are widely used to estimate causal effects in settings with observational time-series data. To identify the causal effect on a target unit, SC requires the existence of correlated units that are not impacted by the inter
Externí odkaz:
http://arxiv.org/abs/2406.11399
Quantifying cause and effect relationships is an important problem in many domains. The gold standard solution is to conduct a randomised controlled trial. However, in many situations such trials cannot be performed. In the absence of such trials, ma
Externí odkaz:
http://arxiv.org/abs/2301.07656
The ability to answer causal questions is crucial in many domains, as causal inference allows one to understand the impact of interventions. In many applications, only a single intervention is possible at a given time. However, in some important area
Externí odkaz:
http://arxiv.org/abs/2210.05446
Counterfactual inference is a powerful tool, capable of solving challenging problems in high-profile sectors. To perform counterfactual inference, one requires knowledge of the underlying causal mechanisms. However, causal mechanisms cannot be unique
Externí odkaz:
http://arxiv.org/abs/2109.01904
Autor:
Gilligan-Lee, Ciarán M.
The computational abilities of theories within the generalised probabilistic theory framework has been the subject of much recent study. Such investigations aim to gain an understanding of the possible connections between physical principles and comp
Externí odkaz:
http://arxiv.org/abs/2108.11454
Autor:
Lavin, Alexander, Gilligan-Lee, Ciarán M., Visnjic, Alessya, Ganju, Siddha, Newman, Dava, Baydin, Atılım Güneş, Ganguly, Sujoy, Lange, Danny, Sharma, Amit, Zheng, Stephan, Xing, Eric P., Gibson, Adam, Parr, James, Mattmann, Chris, Gal, Yarin
The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence can lead to technical debt, scope creep and misaligned objectives
Externí odkaz:
http://arxiv.org/abs/2101.03989
Autor:
Selby, John H., Lee, Ciarán M.
Publikováno v:
Quantum 4, 319 (2020)
Quantum coherence is one of the most important resources in quantum information. Indeed, preventing the loss of coherence is one of the most important technical challenges obstructing the development of large-scale quantum computers. Recently, there
Externí odkaz:
http://arxiv.org/abs/1911.04513
Autor:
Dhir, Anish, Lee, Ciarán M.
Causal knowledge is vital for effective reasoning in science, as causal relations, unlike correlations, allow one to reason about the outcomes of interventions. Algorithms that can discover causal relations from observational data are based on the as
Externí odkaz:
http://arxiv.org/abs/1910.11356