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Transparency: Motivations and Challenges? Adrian…
https://mlg.eng.cam.ac.uk/adrian/transparency.pdf19 Jun 2024: truth.”. Defining criteria and tests for practical faithfulness are important open pro-blems. ... 53. Prat, A.: The wrong kind of transparency. American Economic Review 95(3),862–877 (2005). -
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. -
The Geometry of Random Features Krzysztof Choromanski∗1 Mark…
https://mlg.eng.cam.ac.uk/adrian/geometry.pdf19 Jun 2024: On the left: Gaussian kernel, inthe middle: kernel defined by the PD function φ(‖z‖) =(1 ‖z‖2. ... pre-dictive distribution obtained by an exactly-trained GP, and(ii) predictive RMSE on test sets. -
The Case for Process Fairness in Learning:Feature Selection for ...
https://mlg.eng.cam.ac.uk/adrian/grgic.pdf19 Jun 2024: 3. Prior literature in social, economic, legal, and political sciences distinguishing between directdiscrimination and indirect discrimination makes similar observations as we do in this paper. ... For each of the classifiers, we also compute -
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring…
https://mlg.eng.cam.ac.uk/adrian/KDD2018_inequality_indices.pdf19 Jun 2024: In this paper, we propose to quantify unfairness using inequal-ity indices that have been extensively studied in economics andsocial welfare [3, 10, 19]. ... For all experiments, we repeatedly split the data into 70%-30%train-test sets 10 times and -
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. -
Human Perceptions of Fairness in Algorithmic Decision Making: A Case…
https://mlg.eng.cam.ac.uk/adrian/WWW18-HumanPerceptions.pdf19 Jun 2024: We draw these latentproperties from the existing literature in social-economic-political-moral sciences, philosophy, and the law, as detailed below.I. ... To evaluate the model, we randomly split thedata into 50%/50% train/test folds five times, and -
Beyond Distributive Fairness in Algorithmic Decision Making: Feature…
https://mlg.eng.cam.ac.uk/adrian/AAAI18-BeyondDistributiveFairness.pdf19 Jun 2024: 2011). For all reported results, we ran-domly split the data into 50%/50% train/test folds 5 times and re-port average statistics. ... In Univer-sity of Michigan Law & Economics Research Paper No. 16012.Ahmed, F.; Dickerson, J. -
Clamping Variables and Approximate Inference Adrian WellerColumbia…
https://mlg.eng.cam.ac.uk/adrian/NeurIPS14-clamp.pdf19 Jun 2024: 6. Test models were constructed as follows: For n variables, singleton potentials were drawn θi U[Tmax,Tmax]; edge weights were drawn Wij U[0,Wmax] for attractive models, or Wij ... In UAI, 2009. P. Milgrom. The envelope theorems. Department of Economics -
Tightness of LP Relaxations for Almost Balanced Models Adrian ...
https://mlg.eng.cam.ac.uk/adrian/tricam.pdf19 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
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