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Beyond Distributive Fairness in Algorithmic Decision Making: Feature…
https://mlg.eng.cam.ac.uk/adrian/AAAI18-BeyondDistributiveFairness.pdf19 Jun 2024: We provide fast submodular mechanisms to op-timize the tradeoff between procedural fairness and predic-tion accuracy. ... Rather, we sometimes see a sig-nificant change in the whole feature set used, in order to op-timize the objective. -
Understanding the Bethe Approximation: When and How can it ...
https://mlg.eng.cam.ac.uk/adrian/abc.pdf19 Jun 2024: See 5.1 in the Appendix for further analysis. 6 EXPERIMENTS. We are interested in the empirical performance of the op-timum Bethe marginals and partition function, as the re-. ... Results on the validation set are shown in Figure 4, indi-cating that FW -
C:/Users/Adrian/Documents/GitHub/betheClean/docs/nb-UAI.dvi
https://mlg.eng.cam.ac.uk/adrian/nb-UAI.pdf19 Jun 2024: accuracy) of ageneralbinarypairwise MRF. Runtime is practical forsmall real-world problems. • This now allows the accuracy of the global Bethe op-timum to be tested. • ... Importantly, we now have the opportunity to ex-amine rigorously the -
An Introduction to LP Relaxations for MAP Inference
https://mlg.eng.cam.ac.uk/adrian/2018-MLSALT4-AW2-LP.pdf19 Jun 2024: The rationalefor using an approximate solver is that especially in the beginning of the op-timization process the current relaxation is not a “tight” relaxation of the cutpolytope anyway.”. -
Orthogonal Estimation of Wasserstein Distances Mark Rowland∗1 Jiri…
https://mlg.eng.cam.ac.uk/adrian/AISTATS19-slicedwasserstein.pdf19 Jun 2024: Genevay, A., Cuturi, M., Peyré, G., and Bach, F.(2016). Stochastic Optimization for Large-scale Op-timal Transport. ... 2018). Computational Op-timal Transport. arXiv. Pitié, F., Kokaram, A. C., and Dahyot, R. -
TibGM: A Transferable and Information-Based Graphical Model Approach…
https://mlg.eng.cam.ac.uk/adrian/ICML2019-TibGM.pdf19 Jun 2024: Kappen, H. Path integrals and symmetry breaking for op-timal control theory. -
Structured Evolution with Compact Architectures for Scalable Policy…
https://mlg.eng.cam.ac.uk/adrian/structured_icml_full.pdf19 Jun 2024: Ourexperiments include a detailed comparison of variousDFO schemes on a collection of 212 benchmark op-timization problems from (Moré & Wild, 2009) and12 continuous control tasks from the OpenAI Gymbenchmark suite. • -
Discovering Interpretable Representations for Both Deep Generative…
https://mlg.eng.cam.ac.uk/adrian/ICML18-Discovering.pdf19 Jun 2024: Also, note that an interpretable model will op-timize the dependence between the latent space z and theside information s to express a simple, i.e. -
One-network Adversarial Fairness
https://mlg.eng.cam.ac.uk/adrian/AAAI2019_OneNetworkAdversarialFairness.pdf19 Jun 2024: classifier, we make onlyslight adjustments to the architecture including adding a newhidden layer, in order to enable the concurrent adversarial op-timization for fairness and accuracy. -
Bethe and Related Pairwise Entropy Approximations Adrian…
https://mlg.eng.cam.ac.uk/adrian/Weller_UAI15_BetheAndRelated.pdf19 Jun 2024: ReferencesF. Bach. Learning with submodular functions: A convex op-. timization perspective. -
Clamping Improves TRW and Mean Field Approximations Adrian Weller∗ ...
https://mlg.eng.cam.ac.uk/adrian/clamp_aistats_final.pdf19 Jun 2024: the constrained op-timization where P is the standard space over which themethod optimizes (P = M′ for MF, P = L for Bethe andTRW), P(xi) is the sub-space constrained to -
Conditions Beyond Treewidth for Tightness of Higher-order LP…
https://mlg.eng.cam.ac.uk/adrian/conditions.pdf19 Jun 2024: maxx{0,1}VE. xij =xixjijE. iV. θixi ijE. Wijxij. . (3)With the objective now linear, an LP may be formed by op-timizing over the convex hull of the
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