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1 - 27 of 27 search results for people alumni |u:mlg.eng.cam.ac.uk |d>01Jul2023<03Jul2024 where 0 match all words and 27 match some words.
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  2. 13 Dec 2023: Hong Ge. Principal Investigator. Senior Research Fellow. atFellow of Darwin college. Email: hg344 [at] cam [dot] ac [dot] uk. I am a Senior Research Fellow in the Department of Engineering at the University of Cambridge. I lead the Turing language
  3. 19 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.
  4. 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: 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
  5. - IB Paper 7: Probability and Statistics

    https://mlg.eng.cam.ac.uk/teaching/1BP7/1819/lect06.pdf
    19 Nov 2023: We get 10 people to blind test, each given a randomly selected drink. ... For example, thenull hypothesis could be that people can’t tell the difference betweenwhiskies. •
  6. https://mlg.eng.cam.ac.uk/blog/feed.xml

    https://mlg.eng.cam.ac.uk/blog/feed.xml
    12 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
  7. Bayesian Deep Learning via Subnetwork Inference · Cambridge MLG Blog

    https://mlg.eng.cam.ac.uk/blog/2021/07/21/subnetwork-inference.html
    12 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).
  8. - IB Paper 7: Probability and Statistics

    https://mlg.eng.cam.ac.uk/teaching/1BP7/1819/lect02.pdf
    19 Nov 2023: Example: What is the probability that two people both have their birthday inJanuary? ... First, work out the probability qn that with n people, none share birthdays.
  9. Transparency: Motivations and Challenges? Adrian…

    https://mlg.eng.cam.ac.uk/adrian/transparency.pdf
    19 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.
  10. Human Perceptions of Fairness in Algorithmic Decision Making: A Case…

    https://mlg.eng.cam.ac.uk/adrian/WWW18-HumanPerceptions.pdf
    19 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.
  11. 2018 Formatting Instructions for Authors Using LaTeX

    https://mlg.eng.cam.ac.uk/adrian/AIES18-crowd_signals.pdf
    19 Jun 2024: 2017),and might help to promote greater understanding and cohe-sion among people. ... CNN You mean, like the UNFOUNDED claims of Russiancollusion? You people are typically selective in your bias pro?https://t.co/CESkVpIZOk@nytimes His actions were
  12. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/0910/lect01.pdf
    19 Nov 2023: Here is a useful statistics / pattern recognition glossary:http://alumni.media.mit.edu/tpminka/statlearn/glossary/glossary.html. Ghahramani & Rasmussen (CUED) Lecture 1: Introduction to Machine Learning January 14th, 2010 26 /
  13. Working Draft 1 Accountability of AI Under the Law: ...

    https://mlg.eng.cam.ac.uk/adrian/SSRN-id3064761-Dec19.pdf
    19 Jun 2024: for most people.8 Moreover, decisions about how to define objective functions and what training data to use can introduce human error into AI decision making.9 Thus, there exist legitimate
  14. Methods for Inference in Graphical Models

    https://mlg.eng.cam.ac.uk/adrian/phd_FINAL.pdf
    19 Jun 2024: I’ve had the privilege to collaborate with wonderful people. Many thanks to Tony, Dan Ellis,.
  15. Beyond Distributive Fairness in Algorithmic Decision Making: Feature…

    https://mlg.eng.cam.ac.uk/adrian/AAAI18-BeyondDistributiveFairness.pdf
    19 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
  16. - IB Paper 7: Probability and Statistics

    https://mlg.eng.cam.ac.uk/teaching/1BP7/1819/lect04.pdf
    19 Nov 2023: Waiting times. The bus arrives on average every 15 minutes. Compare the average waiting timefor people arriving randomly if buses 1) arrive regularly, 2) arrive randomly.
  17. - IB Paper 7: Probability and Statistics

    https://mlg.eng.cam.ac.uk/teaching/1BP7/1819/lect03.pdf
    19 Nov 2023: The averageincome is very different from the median, since a few people have very largeincomes.
  18. Bounding the Integrality Distance ofLP Relaxations for Structured…

    https://mlg.eng.cam.ac.uk/adrian/OPT2016_paper_3.pdf
    19 Jun 2024: That is, max-margin training with approximateinference—which is something people do anyway to learn graphical models—reduces not only theprediction error, but also the inference approximation error.
  19. ML-IRL: Machine Learning in Real Life Workshop at ICLR ...

    https://mlg.eng.cam.ac.uk/adrian/ML_IRL_2020-CLUE.pdf
    19 Jun 2024: SampleSize” denotes how many people received this variant. The next two columns contain the proportionof correct answers when identifying epistemic or aleatoric uncertainty for LSAT, respectively.
  20. Blind Justice: Fairness with Encrypted Sensitive Attributes

    https://mlg.eng.cam.ac.uk/adrian/ICML18-BlindJustice.pdf
    19 Jun 2024: 2.2. Fairness Criteria. In large part, works that formalize fairness in machine learn-ing do so by balancing a certain condition between groupsof people with different sensitive attributes, z versus ... Figure 3. The fraction of people with z = 0
  21. Network Ranking With Bethe Pseudomarginals Kui TangColumbia…

    https://mlg.eng.cam.ac.uk/adrian/2013_NeurIPS_DiscML_Network.pdf
    19 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.
  22. Adversarial Graph Embeddings for Fair Influence Maximization over…

    https://mlg.eng.cam.ac.uk/adrian/IJCAI20_AdversarialGraphEmbeddings.pdf
    19 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.
  23. Exploring Properties of the Deep Image Prior Andreas…

    https://mlg.eng.cam.ac.uk/adrian/NeurIPS_2019_DIP7.pdf
    19 Jun 2024: ak960@alumni.cam.ac.uk. Tameem AdelUniversity of Cambridgetah47@cam.ac.uk. Adrian WellerUniversity of Cambridgeaw665@cam.ac.uk. Abstract. The Deep Image Prior (DIP, Ulyanov et al., 2017) is a
  24. 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
    19 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).
  25. One-network Adversarial Fairness

    https://mlg.eng.cam.ac.uk/adrian/AAAI2019_OneNetworkAdversarialFairness.pdf
    19 Jun 2024: Here, following earlierwork, unfairness means discriminating against a particulargroup of people due to sensitive group characteristics suchas gender or race (Grgic-Hlaca et al. ... For the Adult data, a random guessing classi-fier would result in 75.9%
  26. A Unified Approach to Quantifying Algorithmic Unfairness: Measuring…

    https://mlg.eng.cam.ac.uk/adrian/KDD2018_inequality_indices.pdf
    19 Jun 2024: We furtherillustrate this point with two examples: First, by this definition amodel that assigns the same outcome to everyone is considered fair,regardless of people’s merit for different outcomes ... This offersa framework to interpolate between group
  27. 3F3: Signal and Pattern Processing Lecture 1: Introduction to ...

    https://mlg.eng.cam.ac.uk/teaching/3f3/1011/lect1.pdf
    19 Nov 2023: Here is a useful statistics / pattern recognition glossary:http://alumni.media.mit.edu/tpminka/statlearn/glossary/.
  28. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/1011/lect01.pdf
    19 Nov 2023: Here is a useful statistics / pattern recognition glossary:http://alumni.media.mit.edu/tpminka/statlearn/glossary/glossary.html. Ghahramani & Rasmussen (CUED) Lecture 1: Introduction to Machine Learning 26 / 26.

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