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4F13 Machine Learning: Coursework #4: Reinforcement Learning Zoubin…
https://mlg.eng.cam.ac.uk/teaching/4f13/0910/cw/coursework4.pdf19 Nov 2023: Test yourvalueIteration algorithm. -
PROPAGATION OF UNCERTAINTY IN BAYESIAN KERNEL MODELS— APPLICATION TO…
https://mlg.eng.cam.ac.uk/pub/pdf/QuiGirLarRas03.pdf13 Feb 2023: Thiscorresponds to using the model in recall/test phase under uncer-tain input. ... 3. PREDICTION WITH UNCERTAIN INPUT. Assume that the test inputx can not be observed directly and theuncertainty is modeled asx p(x) = N (u, S), with meanuand covariance -
Variable noise and dimensionality reduction forsparse Gaussian…
https://mlg.eng.cam.ac.uk/zoubin/papers/snelson_uai.pdf27 Jan 2023: We have triedto implement both versions efficiently. Validation Time /s. Method NLPD MSE Train Test. ... To test this weused PCA to reduce the dimension to 5, before usingthe SPGP. -
4F13 Machine Learning: Coursework #4: Reinforcement Learning Zoubin…
https://mlg.eng.cam.ac.uk/teaching/4f13/1011/cw/coursework4.pdf19 Nov 2023: Test yourvalueIteration algorithm. -
You Shouldn’t Trust Me: Learning Models WhichConceal Unfairness From…
https://mlg.eng.cam.ac.uk/adrian/ECAI20-You_Shouldn%E2%80%99t_Trust_Me.pdf19 Jun 2024: Each histogramrepresents the ranking across the test set assigned by the designated feature importance method. ... These results suggest that ourattack is successful in generalising across unseen test points. -
Gaussian Process Change Point Models
https://mlg.eng.cam.ac.uk/pub/pdf/SaaTurRas10.pdf13 Feb 2023: Weevaluated the models’ ability to predict next day snow-fall using 35 years of test data. ... Method Negative Log Likelihood p-value MSE p-valueNile Data (200 Training Points, 462 Test Points). -
Adaptive Sequential Bayesian Change Point Detection Ryan…
https://mlg.eng.cam.ac.uk/pub/pdf/TurSaaRas09.pdf13 Feb 2023: We also include the 95% error bars on the NLL and the p-value that the joint model/learned hypers hasa higher NLL using a one sided t-test. ... Industry: We test on the last 8455 points of the portfolio data, 3 July 1975–31 December 2008. -
G:\bioinformatics\Bioinfo-26(7)issue\btq053.dvi
https://mlg.eng.cam.ac.uk/pub/pdf/LipGhaBor10.pdf13 Feb 2023: A two-sample test tries to decide whether twosamples, in our case x and DC , have been generated by the samedistribution or not. ... edge,and might artificially boost prediction accuracy if they appear in both trainingand test set. -
Relational Learning with Gaussian Processes Wei ChuCCLS Columbia…
https://mlg.eng.cam.ac.uk/pub/pdf/ChuSinGhaetal07.pdf13 Feb 2023: in the input space and provides proba-bilistic induction over unseen test points. ... K̃(zm, zt)]T. One can computethe Bernoulli distribution over the test labelyt by. -
4F13 Machine Learning: Coursework #1: Gaussian Processes Carl Edward…
https://mlg.eng.cam.ac.uk/teaching/4f13/1314/cw/coursework1.pdf19 Nov 2023: Show and comment on the fit and the hypers, and the predictions forthe test data. -
https://mlg.eng.cam.ac.uk/zoubin/misc/karna.txt
https://mlg.eng.cam.ac.uk/zoubin/misc/karna.txt27 Jan 2023: This could take time and test our patience. Q. Once the enemy is defined, is violence the proper response? ... It may require education. It will require time and may test our patience. -
Relational Learning with Gaussian Processes Wei ChuCCLS Columbia…
https://mlg.eng.cam.ac.uk/zoubin/papers/relationalgp.pdf27 Jan 2023: in the input space and provides proba-bilistic induction over unseen test points. ... K̃(zm, zt)]T. One can computethe Bernoulli distribution over the test labelyt by. -
Gaussian Process Model Based Predictive Control
https://mlg.eng.cam.ac.uk/pub/pdf/KocMurRasGir04.pdf13 Feb 2023: 4. Fitting of theresponse for validation signal:• average absolute test error. AE = 0.1276 (14). • ... average squared test error. SE = 0.0373 (15). 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100002. -
544 The Block Diagonal Infinite Hidden Markov Model Thomas ...
https://mlg.eng.cam.ac.uk/pub/pdf/SteGhaGoretal09.pdf13 Feb 2023: Each dataset had 2000 stepsof training data and 2000 steps of test data. ... these were not significantly different (two sam-ple t-test, p = 0.3). -
Determinantal Clustering Process - A Nonparametric BayesianApproach…
https://mlg.eng.cam.ac.uk/pub/pdf/ShaGha13a.pdf13 Feb 2023: Next anyparameters of the density model can be integrated outto produce a predictive clustering of unseen test data. ... lected wheat types) and leave 5 data points from eachwheat type as unobserved test points. -
The Infinite Hidden Markov Model Matthew J. Beal Zoubin ...
https://mlg.eng.cam.ac.uk/zoubin/papers/ihmm.pdf27 Jan 2023: We propose estimating the likelihood of a test sequence given a learned model using particlefiltering. ... 6Different particle initialisations apply if we do not assume that the test sequence immediatelyfollows the training sequence. -
The Supervised IBP: Neighbourhood PreservingInfinite Latent Feature…
https://mlg.eng.cam.ac.uk/pub/pdf/QuaShaKnoGha13.pdf13 Feb 2023: the in-ferred test latent variable z with respect to the train-ing latent variables Z. ... boldface is significant using a one-sided paired t-test with 95% confidence. -
1 Learning the Structure of Deep Sparse Graphical Models ...
https://mlg.eng.cam.ac.uk/pub/pdf/AdaWalGha10.pdf13 Feb 2023: c) (d)Figure 4: Olivetti faces a) Test images on the left, withreconstructed bottom halves on the right. ... Fig 4ashows six bottom-half test set reconstructions on theright, compared to the ground truth on the left. -
chu05a.dvi
https://mlg.eng.cam.ac.uk/pub/pdf/ChuGha05a.pdf13 Feb 2023: 0.23370.0072. Table 2: Test results of the three algorithms using a Gaussian kernel. ... Figure 4 presents the test results of the three algorithms for different numbers ofselected genes. -
Archipelago: Nonparametric Bayesian Semi-Supervised Learning Ryan…
https://mlg.eng.cam.ac.uk/pub/pdf/AdaGha09.pdf13 Feb 2023: In almost all of our tests, Archipelagohad lower test classification error than the NCNM. ... Itimproves over mixture-based Bayesian approaches toSSL while still modeling complex density functions.In empirical tests, our model compares favorably -
- 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. • -
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. -
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 -
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 -
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. -
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 -
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). -
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. -
The EM-EP Algorithm forGaussian Process Classification Hyun-Chul Kim� …
https://mlg.eng.cam.ac.uk/zoubin/papers/ecml03.pdf27 Jan 2023: Fig 2 (a) and Fig 2 (b) shows the training set and the test set. ... Each fold were subsequently used as a test set, while the other 9. -
images/test_user_webdesign.eps
https://mlg.eng.cam.ac.uk/pub/pdf/IwaShaGha13a.pdf13 Feb 2023: The vertical axis is the test likelihood, and the horizontal is the test time period. ... Thevertical axis is the test likelihood, and the horizontal is the test time period. -
Warped Gaussian Processes Edward Snelson∗ Carl Edward Rasmussen†…
https://mlg.eng.cam.ac.uk/pub/pdf/SneRasGha04.pdf13 Feb 2023: the following table which shows the range of the targets(tmin, tmax), the number of input dimensions (D), and the size of the training and test sets(Ntrain, Ntest) that we ... We show three measuresof performance over independent test sets: mean absolute -
Draft version; accepted for NIPS*03 Warped Gaussian Processes Edward…
https://mlg.eng.cam.ac.uk/zoubin/papers/gpwarp.pdf27 Jan 2023: the following table which shows the range of the targets(tmin, tmax), the number of input dimensions (D), and the size of the training and test sets(Ntrain, Ntest) that we ... We show three measuresof performance over independent test sets: mean absolute -
chu05a.dvi
https://mlg.eng.cam.ac.uk/zoubin/papers/chu05a.pdf27 Jan 2023: 0.23370.0072. Table 2: Test results of the three algorithms using a Gaussian kernel. ... Figure 4 presents the test results of the three algorithms for different numbers ofselected genes. -
Bayesian Classifier Combination Hyun-Chul Kim Zoubin GhahramaniKorea…
https://mlg.eng.cam.ac.uk/pub/pdf/KimGha12.pdf13 Feb 2023: 6500, 1000, 797 data points were selected from the orig-inal test set as a validation set for DNA data set, Satellitedata set, UCI digit data set, respectively. ... BCCresults are based on comparing the posterior mode ofti for data points in the test set -
A Probabilistic Model for Online Document Clustering with Application …
https://mlg.eng.cam.ac.uk/pub/pdf/ZhaGhaYan04a.pdf13 Feb 2023: In addition to the binary decision “novel” or “non-novel”, eachsystem is required to generated a confidence score for each test document. ... References. [1] The 2002 topic detection & tracking task definition and evaluation -
LNAI 8189 - Variational Hidden Conditional Random Fields with Coupled …
https://mlg.eng.cam.ac.uk/pub/pdf/BouZafMor13a.pdf13 Feb 2023: In each case, we evaluated their performance on a test set consist-ing of sequences from 3 debates. ... Since we have introduced parameters θ it is sensible to test our methodologyfor signs of overfitting. -
Learning to Parse Images
https://mlg.eng.cam.ac.uk/pub/pdf/HinGhaTeh99a.pdf13 Feb 2023: Then the learning. 2,3. 2,4. 2,5. 3,4. 3,5. 4,5. Figure 1: Sample images from the test set. ... tested on the same test set. -
Nonparametric Transforms of Graph Kernelsfor Semi-Supervised Learning …
https://mlg.eng.cam.ac.uk/zoubin/papers/ZhuKanGhaLaf04.pdf27 Jan 2023: Morespecifically, we restrict ourselves to thetransductive setting where the unlabeled data alsoserve as the test data. ... All classes must be present in the labeled set. The rest is used asunlabeled (test) set in that trial. -
Gaussian Processes in Reinforcement Learning Carl Edward Rasmussen…
https://mlg.eng.cam.ac.uk/pub/pdf/RasKus04.pdf13 Feb 2023: The predictive distribution for a novel test input. is Gaussian: ) 2! ... on random examples, the relations can already be approximated to within root meansquared errors (estimated on -& test samples and considering the mean of the predicteddistribution) -
The Infinite Hidden Markov Model Matthew J. Beal Zoubin ...
https://mlg.eng.cam.ac.uk/pub/pdf/BeaGhaRas02.pdf13 Feb 2023: We propose estimating the likelihood of a test sequence given a learned model using particlefiltering. ... 6Different particle initialisations apply if we do not assume that the test sequence immediatelyfollows the training sequence. -
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. -
Seven new papers from the group to appear at NIPS 2015 in Montreal |…
https://mlg.eng.cam.ac.uk/news/seven-new-papers-from-the-group-to-appear-at-nips-2015-in-montreal/3 Jul 2024: The list of papers are:. Statistical Model Criticism using Kernel Two Sample Tests. -
Bayesian Deep Learning via Subnetwork Inference · Cambridge MLG Blog
https://mlg.eng.cam.ac.uk/blog/2021/07/21/subnetwork-inference.html12 Apr 2024: Figure 9: Results on the rotated MNIST benchmark, showing the mean $pm$ std of the test error (top) and log-likelihood (bottom) across three different seeds. ... methods. Figure 10: Results on the corrupted CIFAR-10 benchmark, showing the mean $pm$ std -
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 -
Understanding the Bethe Approximation: When and How can it go Wrong?
https://mlg.eng.cam.ac.uk/adrian/pabc.pdf19 Jun 2024: Wolfe used for all runs, aftervalidating against smaller test set usingdual decomposition with guaranteed-approx mesh method (Weller andJebara, 2014). -
Natural-Gradient Variational Inference 2: ImageNet-scale · Cambridge…
https://mlg.eng.cam.ac.uk/blog/2021/11/24/ngvi-bnns-part-2.html12 Apr 2024: Reducing the prior precision $delta$ results in higher validation accuracy, but also a larger train-test gap, corresponding to more overfitting. ... Continual Learning: I personally think continual learning is a very good way to test approximate Bayesian -
The Geometry of Random Features Krzysztof Choromanski∗1 Mark…
https://mlg.eng.cam.ac.uk/adrian/geometry.pdf19 Jun 2024: pre-dictive distribution obtained by an exactly-trained GP, and(ii) predictive RMSE on test sets. ... Figure 8: Approximate GP regression results on Bostondataset. Reported numbers are average test RMSE, alongwith bootstrap estimates of standard error -
4F13 Machine Learning: Coursework #3: Latent Dirichlet Allocation…
https://mlg.eng.cam.ac.uk/teaching/4f13/1819/cw/coursework3.pdf19 Nov 2023: c) For the Bayesian model, what is the log probability for the test document with ID 2001? ... Explainwhether, when computing the log probability of a test document, you would use the multinomial withor without the “combinatorial factor”. -
Modelling data
https://mlg.eng.cam.ac.uk/teaching/4f13/1819/modelling%20data.pdf19 Nov 2023: generalize from observations in the training set to new test cases(interpolation and extrapolation). • ... make predictions on test cases• interpret the trained model, what insights is the model providing?• evaluate the accuracy of model. •
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