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The Case for Process Fairness in Learning:Feature Selection for ...
https://mlg.eng.cam.ac.uk/adrian/grgic.pdf19 Jun 2024: on their effects on people belonging to certain sensitivedemographic groups (e.g., gender, race). ... Now we compare user judgments of fairness of individual features, fordifferent demographic subgroups of people. -
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: This is despite using speed-up tricks (Goodfellow, 2015). The similarities in the equations indicate that we might be able to take techniques people use to scale Adam up to large -
Human Perceptions of Fairness in Algorithmic Decision Making: A Case…
https://mlg.eng.cam.ac.uk/adrian/WWW18-HumanPerceptions.pdf19 Jun 2024: Our Contributions. We collected and analyzed fairness judgmentsfrom a survey of 576 people. ... So our framework for how people judgefeature usage fairness consists of two-parts. -
Bayesian Deep Learning via Subnetwork Inference · Cambridge MLG Blog
https://mlg.eng.cam.ac.uk/blog/2021/07/21/subnetwork-inference.html12 Apr 2024: Of course, in larger-scale settings, a full-covariance Gaussian would be intractable, so people often resort to diagonal approximations which assume full independence between the weights (Figure 7, top right). -
Transparency: Motivations and Challenges? Adrian…
https://mlg.eng.cam.ac.uk/adrian/transparency.pdf19 Jun 2024: Yet both concepts are somewhat ambiguous, and can meandifferent things to different people in different contexts. ... People might be experts or not. We list several types and goals of transparency. -
https://mlg.eng.cam.ac.uk/blog/feed.xml
https://mlg.eng.cam.ac.uk/blog/feed.xml12 Apr 2024: Jekyll 2024-04-12T16:32:5900:00 https://mlg.eng.cam.ac.uk/blog/feed.xml MLG Blog Blog of the Machine Learning Group at the University of Cambridge An introduction to Flow Matching 2024-01-20T00:00:0000:00 2024-01-20T00:00:0000:00 -
Beyond Distributive Fairness in Algorithmic Decision Making: Feature…
https://mlg.eng.cam.ac.uk/adrian/AAAI18-BeyondDistributiveFairness.pdf19 Jun 2024: However, we note that such judgments can begathered from any other group of people, ranging fromcrowd workers to domain experts. ... people) more likelyto be falsely predicted as having a higher risk of recidivismthan another group of people (e.g., white -
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: Let x2 be a sensitive fea-ture, such as age, given by the shape of the point: assume youngand mature people. ... 100% points are accurate (correctly, blue maturepeople are in the blue zone, red mature people are in the red zone). -
Adversarial Graph Embeddings for Fair Influence Maximization over…
https://mlg.eng.cam.ac.uk/adrian/IJCAI20_AdversarialGraphEmbeddings.pdf19 Jun 2024: in terms of thetotal number of people influenced, and the fraction of peopleinfluenced from both A and B communities. ... maximizing the to-tal number of influenced people, while also significantly de-creasing disparity. -
Network Ranking With Bethe Pseudomarginals Kui TangColumbia…
https://mlg.eng.cam.ac.uk/adrian/2013_NeurIPS_DiscML_Network.pdf19 Jun 2024: 1 Introduction. Many important data-sets involve networks: people belong to social networks, webpages are joined in a linkgraph, and power utilities are connected in a grid.
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