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Dependent Indian Buffet Processes Sinead Williamson Peter Orbanz…
https://mlg.eng.cam.ac.uk/pub/pdf/WilOrbGha10.pdf13 Feb 2023: The data was randomly split into atraining set of 130 countries, and a test set of 14 coun-tries. ... For each test country, one randomly selectedindicator was observed, and the remainder held outfor prediction. -
- IB Paper 7: Probability and Statistics
https://mlg.eng.cam.ac.uk/teaching/1BP7/1819/lect01.pdf19 Nov 2023: Why do we need this, is it useful?• Make inference about uncertain events• Form the basis of information theory• Test the strength of statistical evidence. • -
Geometrically Coupled Monte Carlo Sampling Mark Rowland∗University of …
https://mlg.eng.cam.ac.uk/adrian/NeurIPS18-gcmc.pdf19 Jun 2024: 5.2 Variance-reduced ELBO estimation for deep generative models. In this section, we test GCMC sampling strategies on a deep generative modelling application. ... 10. Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and -
Learning to Control a Low-Cost Manipulator usingData-Efficient…
https://mlg.eng.cam.ac.uk/pub/pdf/DeiRasFox11.pdf13 Feb 2023: Althoughdeposit failure feedback was not available to the learner, thedeposit success is good across 10 test trials and four differenttraining setups. ... Second, Tab. III reports the block-deposit success rates for10 test runs (and four different -
Gibbs sampling (an MCMC method) and relations to EM
https://mlg.eng.cam.ac.uk/zoubin/SALD/week7at.pdf27 Jan 2023: Uniform U(0, θ] distribution, θ > 0. We select at random m n disks, having a common θ for failure We select n of these (at random) and test them until -
Probabilistic Modelling, Machine Learning,and the Information…
https://mlg.eng.cam.ac.uk/zoubin/talks/mit12csail.pdf27 Jan 2023: Some Comparisons. Table 1: Test errors and predictive accuracy (smaller is better) for the GP classifier, the supportvector machine, the informative vector machine, and the sparse pseudo-inputGP classifier. ... Data set GPC SVM IVM SPGPC. name train:test -
Approximate inference for the loss-calibrated Bayesian
https://mlg.eng.cam.ac.uk/pub/pdf/LacHusGha11.pdf13 Feb 2023: We also assume. the transductive scenario where we are given a test setS of S points {xs}Ss=1, i.e. ... of the shift between the test and trainingdistributions (columns) and the asymmetry of loss (rows). -
LNAI 3944 - Evaluating Predictive Uncertainty Challenge
https://mlg.eng.cam.ac.uk/pub/pdf/QuiRasSinetal06.pdf13 Feb 2023: The participants could then usethem to train their algorithms before submitting the test predictions. ... The test results were made public on December 11. The website remainsopen for submission. -
Bayesian Hierarchical Clustering Katherine A. Heller…
https://mlg.eng.cam.ac.uk/zoubin/papers/bhcnew.pdf27 Jan 2023: As we will see, the main difference isthat our algorithm uses a statistical hypothesis test tochoose which clusters to merge. ... Sec-ond our algorithm is derived from Dirichlet processmixtures. Third the hypothesis test at the core ofour algorithm tests -
gppl.dvi
https://mlg.eng.cam.ac.uk/zoubin/papers/icml05chuwei-pl.pdf27 Jan 2023: 2.3. Prediction. Now let us take a test pair (r, s) on which the pref-erence relation is unknown. ... our algorithm. The test results of the two algorithmsare presented in the left graph of Figure 2.
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