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Discovering Interpretable Representations for Both Deep Generative…
https://mlg.eng.cam.ac.uk/adrian/ICML18-Discovering.pdf19 Jun 2024: 1. IntroductionLearning interpretable data representations is becomingever more important as machine learning models grow insize and complexity, and as applications reach critical so-cial, economic and public health domains. -
Speaking Truth to Climate Change
https://mlg.eng.cam.ac.uk/carl/climate/truth.pdf25 Jun 2024: The effect of the alliance is to immediately apply strong economic pressure on allcontries to reduce emissions. ... Alliance dynamics. Initially, from a purely economic perspective, it’ll be advantageous for low percapita emitting countries to join -
Computational abstractions for probabilistic and differentiable…
https://mlg.eng.cam.ac.uk/capp-workshop/23 Nov 2022: Online. Bayesian inference and machine learning have found numerous use cases in applied domains and basic science, such as disease modeling, climate research, economics, or astronomy. -
Mechanisms Against Climate Change
https://mlg.eng.cam.ac.uk/carl/talks/cifar.pdf25 Jun 2024: The cooperative immediately creates strong economic pressure on all members to reduce emissions. -
Orthogonal estimation of Wasserstein distances Mark Rowland*, Jiri…
https://mlg.eng.cam.ac.uk/adrian/slicedwasserstein_poster.pdf19 Jun 2024: Naturally incorporate spatial information. • Applications from economics to machine learning. -
Transparency: Motivations and Challenges? Adrian…
https://mlg.eng.cam.ac.uk/adrian/transparency.pdf19 Jun 2024: 53. Prat, A.: The wrong kind of transparency. American Economic Review 95(3),862–877 (2005). ... In: KDD (2016). 55. Ross, S.A.: The economic theory of agency: The principal’s problem. -
ICML-Presentation
https://mlg.eng.cam.ac.uk/zoubin/talks/ICML-Presentation.pdf27 Jan 2023: Lesson: don’t make your paper popular for. -
Gibbs sampling (an MCMC method) and relations to EM
https://mlg.eng.cam.ac.uk/zoubin/SALD/week7at.pdf27 Jan 2023: The lesson to be learned from this example is this:. Before using the EM-algorithm, make sure that the log-likelihood function exists, so that the E-step is properly defined. -
Scaling the iHMM: Parallelization versus Hadoop Sébastien…
https://mlg.eng.cam.ac.uk/pub/pdf/BraVanVlaGha10.pdf13 Feb 2023: best in which situation,3) We report the lessons learned from our distributed. ... D. Qualitative comparison. This section completes the computing cost comparisonwith lessons from the software development exercise of allimplementations. -
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring…
https://mlg.eng.cam.ac.uk/adrian/KDD2018_inequality_indices.pdf19 Jun 2024: Our core idea is to use existing inequalityindices from economics to measure how unequally the outcomes ofan algorithm benefit different individuals or groups in a population.Our work offers a justified ... In this paper, we propose to quantify
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