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  2. Bounding the Integrality Distance ofLP Relaxations for Structured…

    https://mlg.eng.cam.ac.uk/adrian/OPT2016_paper_3.pdf
    19 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.
  3. One-network Adversarial Fairness

    https://mlg.eng.cam.ac.uk/adrian/AAAI2019_OneNetworkAdversarialFairness.pdf
    19 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%.
  4. 19 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.
  5. 2018 Formatting Instructions for Authors Using LaTeX

    https://mlg.eng.cam.ac.uk/adrian/AIES18-crowd_signals.pdf
    19 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
  6. Blind Justice: Fairness with Encrypted Sensitive Attributes

    https://mlg.eng.cam.ac.uk/adrian/ICML18-BlindJustice.pdf
    19 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
  7. 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.pdf
    19 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.
  8. Geometrically Coupled Monte Carlo Sampling Mark Rowland∗University of …

    https://mlg.eng.cam.ac.uk/adrian/NeurIPS18-gcmc.pdf
    19 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
  9. Unifying Orthogonal Monte Carlo Methods

    https://mlg.eng.cam.ac.uk/adrian/ICML2019-unified.pdf
    19 Jun 2024: 3, 4. Approximating kernel matrices: We test the relative er-ror of kernel matrix estimation for the above estimators forthe Gaussian kernel (following the setting of Choromanski& Sindhwani, 2016). ... A kernel two-sample test. J. Mach. Learn.Res.,
  10. 19 Jun 2024: Tightness of LP Relaxations for Almost Balanced Models. Adrian Weller Mark Rowland David SontagUniversity of Cambridge University of Cambridge New York University. Abstract. Linear programming (LP) relaxations are widelyused to attempt to identify a
  11. THIS VERSION FIXES A TYPO IN THE STATEMENT OF ...

    https://mlg.eng.cam.ac.uk/adrian/Weller15_revisit_fixed.pdf
    19 Jun 2024: We shall first prove sufficiency then necessity of the condi-tion. In order to test whether a derived NMRF is perfect, weuse Theorem 1, hence must check for possible odd holes

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