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11 - 20 of 52 search results for katalk:PC53 24 / |u:mlg.eng.cam.ac.uk where 0 match all words and 52 match some words.

  1. Results that match 1 of 2 words

  2. June

    https://mlg.eng.cam.ac.uk/carl/climate/1tonCO2.pdf
    25 Jun 2024: June 24, 2024 1 / 1.

  3. Blind Justice: Fairness with Encrypted Sensitive Attributes

    https://mlg.eng.cam.ac.uk/adrian/poster_ICML18_BlindJustice.pdf
    19 Jun 2024: Bank. Datasets and Feasibility. Adult Bank COMPAS German SQFn training examples 214 215 212 29 216d features 51 62 7 24 23p sensitive attributes 1 1 7 1 1certification 802 ms

  4. Clamping Variables and Approximate Inference

    https://mlg.eng.cam.ac.uk/adrian/newsclamp.pdf
    19 Jun 2024: 0.1. 0.2. 0.3. 0.4. max interaction strength W 24 / 19.

  5. Natural-Gradient Variational Inference 2: ImageNet-scale · Cambridge…

    https://mlg.eng.cam.ac.uk/blog/2021/11/24/ngvi-bnns-part-2.html
    12 Apr 2024: Published on 24 November 2021. This is the blog of the Cambridge Machine Learning Group.

  6. Clamping Variables and Approximate Inference

    https://mlg.eng.cam.ac.uk/adrian/slides_msr2.pdf
    19 Jun 2024: 24 / 21. Error in log Z vs number of clamps: complete graphs. ... 24 / 21. Time (secs) vs error in log Z for various methods.

  7. Revisiting the Limits of MAP Inference by MWSS on Perfect Graphs

    https://mlg.eng.cam.ac.uk/adrian/slides-revisit.pdf
    19 Jun 2024: Revisiting the Limits of MAP Inference by MWSSon Perfect Graphs. Adrian WellerUniversity of Cambridge. CP 2015Cork, Ireland. Slides and full paper athttp://mlg.eng.cam.ac.uk/adrian/. 1 / 21. Motivation: undirected graphical models (MRFs). •

  8. The Case for Process Fairness in Learning:Feature Selection for ...

    https://mlg.eng.cam.ac.uk/adrian/grgic.pdf
    19 Jun 2024: Age 46% 56% 32%Gender 24% 50% 22%Race 24% 35% 15%. Table 3: Comparing judgment of fairness of each feature, of 54 male users, when they have differentknowledge about the ... Age 42% 63% 33%Gender 24% 54% 23%Race 18% 43% 18%. Table 5: Comparing judgment

  9. Blind Justice: Fairness with Encrypted Sensitive Attributes

    https://mlg.eng.cam.ac.uk/adrian/ICML18-BlindJustice.pdf
    19 Jun 2024: d features 51 62 7 24 23p sensitive attr. 1 1 7 1 1certification 802 ms 827 ms 288 ms 250 ms 765 mstraining 43 min 51 min 7 min 1 ... Bennett Capers, I. Blind justice. Yale Journal of Law &Humanities, 24:179, 2012.

  10. Human Perceptions of Fairness in Algorithmic Decision Making: A Case…

    https://mlg.eng.cam.ac.uk/adrian/WWW18-HumanPerceptions.pdf
    19 Jun 2024: 47% 68% 70%B.S. 51% 32% 30%Liberal 57% 37% 33%Conservative 17% 24% 29%Moderate 21% 33% 34%Other 5% 6% 4%. ... Substance Abuse 4.84 0.08 0.07 0.10 0.24 0.07 0.68 0.26 0.22 0.20 0.07 0.284.

  11. An Introduction to LP Relaxations for MAP Inference

    https://mlg.eng.cam.ac.uk/adrian/2018-MLSALT4-AW2-LP.pdf
    19 Jun 2024: x3. x4. x5. x6. s. balanced almost balanced(attractive up to flipping) 24 / 41.

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