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  2. Adrian Weller

    https://mlg.eng.cam.ac.uk/adrian/
    16 Jul 2024: Adrian serves on the boards of several organizations. He is a member of the World Economic Forum Global Future Council on the Future of AI, and is co-director of the ... Train and Test Tightness of LP Relaxations in Structured Prediction.
  3. Exploring Properties of the Deep Image Prior Andreas…

    https://mlg.eng.cam.ac.uk/adrian/NeurIPS_2019_DIP7.pdf
    16 Jul 2024: To test the nature of these outputs we introduce a novel saliencymap approach, termed MIG-SG. ... To test this, we evaluated the sensitivity of DIP to changes innetwork architecture.
  4. Transparency: Motivations and Challenges? Adrian…

    https://mlg.eng.cam.ac.uk/adrian/transparency.pdf
    16 Jul 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).
  5. 16 Jul 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.
  6. The Geometry of Random Features Krzysztof Choromanski∗1 Mark…

    https://mlg.eng.cam.ac.uk/adrian/geometry.pdf
    16 Jul 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.
  7. 16 Jul 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
  8. A Unified Approach to Quantifying Algorithmic Unfairness: Measuring…

    https://mlg.eng.cam.ac.uk/adrian/KDD2018_inequality_indices.pdf
    16 Jul 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
  9. Human Perceptions of Fairness in Algorithmic Decision Making: A Case…

    https://mlg.eng.cam.ac.uk/adrian/WWW18-HumanPerceptions.pdf
    16 Jul 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
  10. 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
    16 Jul 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.
  11. Beyond Distributive Fairness in Algorithmic Decision Making: Feature…

    https://mlg.eng.cam.ac.uk/adrian/AAAI18-BeyondDistributiveFairness.pdf
    16 Jul 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.

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