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Ten papers from the group to appear at ICML 2016 | Cambridge Machine…
https://mlg.eng.cam.ac.uk/news/ten-papers-from-the-group-to-appear-at-icml-2016/3 Jul 2024: Train and Test Tightness of LP Relaxations in Structured Prediction. Ofer Meshi, Mehrdad Mahdavi, Adrian Weller and David Sontag. -
TibGM: A Transferable and Information-Based Graphical Model Approach…
https://mlg.eng.cam.ac.uk/adrian/ICML2019-TibGM.pdf19 Jun 2024: Confidence intervals are shown in all the plots. Unlessnoted otherwise, each experiment was repeated 50 timesand significance has been tested via a paired t-test with sig-nificance level at 5%. ... Significance is tested usingthe same paired t-test -
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.pdf4 Jul 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 -
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 -
ML-IRL: Machine Learning in Real Life Workshop at ICLR ...
https://mlg.eng.cam.ac.uk/adrian/ML_IRL_2020-Counterfactual_Accuracy.pdf19 Jun 2024: would we have to give up so that the predictionfor the test point would change? ... 2017)), and then we constrain fora random test point to obtain counterfactual accuracy. -
Now You See Me (CME): Concept-based Model Extraction
https://mlg.eng.cam.ac.uk/adrian/AIMLAI20-CME.pdf19 Jun 2024: For every𝑓 , we evaluated its fidelity and its task performance,using a held-out sample test set. ... 96.4 0.5%on a held-out test set (averaged over 5 runs). -
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. -
What Keeps a Bayesian Awake At Night? Part 2: Night Time · Cambridge…
https://mlg.eng.cam.ac.uk/blog/2021/03/31/what-keeps-a-bayesian-awake-at-night-part-2.html12 Apr 2024: Practitioners should also test the hell out of their inference schemes to gain confidence in them. ... The acid test is whether your inference scheme works on the real world data you care about, so test cases also need to replicate aspects of this -
Evaluating and Aggregating Feature-based Model Explanations
https://mlg.eng.cam.ac.uk/adrian/IJCAI20_EvaluatingAndAggregating.pdf19 Jun 2024: For Iris [Dua and Graff, 2017], we train our modelto 96% test accuracy. ... 45). Table 2: Faithfulness µF averaged over a test set: (Zero Baseline,Training Average Baseline). -
What Keeps a Bayesian Awake At Night? Part 1: Day Time · Cambridge…
https://mlg.eng.cam.ac.uk/blog/2021/03/31/what-keeps-a-bayesian-awake-at-night-part-1.html12 Apr 2024: Examples include inferring the mass of the Higgs boson ($X$) from collider data ($D$); estimating the prevalence of Covid 19 infections ($X$) from PCR test data ($D$); or reconstructing files ($X$) ... One way to view them is as unit tests that the -
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 -
Leader Stochastic Gradient Descent for DistributedTraining of Deep…
https://mlg.eng.cam.ac.uk/adrian/NeurIPS2019_LSGD_preprint.pdf19 Jun 2024: Test error for the center variableversus wall-clock time (original plot on the left and zoomed onthe right). ... Test loss is reported in Figure 13 in the Supplement. Finally, in Figure 6 we report theempirical results for ResNet50run on ImageNet. -
Bounding the Integrality Distance ofLP Relaxations for Structured…
https://mlg.eng.cam.ac.uk/adrian/OPT2016_paper_3.pdf19 Jun 2024: Thus, the more training data we have, the better we can estimate theexpected integrality distance at test time.Remark 1. ... 9] O. Meshi, M. Mahdavi, A. Weller, and D. Sontag. Train and test tightness of LP relaxations instructured prediction. -
One-network Adversarial Fairness
https://mlg.eng.cam.ac.uk/adrian/AAAI2019_OneNetworkAdversarialFairness.pdf19 Jun 2024: To test significance, we perform a paired t-test withsignificance level at 5%. ... Totest significance, we perform a paired t-test with significance level at 5%. -
Understanding the Bethe Approximation: When and How can it ...
https://mlg.eng.cam.ac.uk/adrian/abc.pdf19 Jun 2024: Ex-periments are described in 6, where we examine test cases.Conclusions are discussed in 7. ... Given this performance, we used FW for all Bethe opti-mizations on the test cases. -
2018 Formatting Instructions for Authors Using LaTeX
https://mlg.eng.cam.ac.uk/adrian/AIES18-crowd_signals.pdf19 Jun 2024: For training our classifiers, we use 5-fold cross-validation.In each test, the original sample is partitioned into 5 sub-samples, out of which 4 are used as training data, ... The processis then repeated 5 times, with each of the 5 sub-samplesused -
Blind Justice: Fairness with Encrypted Sensitive Attributes
https://mlg.eng.cam.ac.uk/adrian/ICML18-BlindJustice.pdf19 Jun 2024: Figure 2 shows the test set accuracyover the constraint value. By design, the synthetic datasetexhibits a clear trade-off between accuracy and fairness. ... Biddle, D. Adverse impact and test validation: A practi-tioner’s guide to valid and defensible -
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. -
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
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