Search

Search Funnelback University

Search powered by Funnelback
1 - 23 of 23 search results for People aliens |u:mlg.eng.cam.ac.uk where 0 match all words and 23 match some words.
  1. Results that match 1 of 2 words

  2. Quantifying climate change: Degree Person Days

    https://mlg.eng.cam.ac.uk/carl/climate/dpd.html
    28 Jun 2024: We are 8000 million people on earth enjoying the climate, there are 365 days in a year and CO. ... Therefore:. 0.45 ton / 7800 million ton per ppm 0.01 C / ppm 8000 million people 365 days per year 300 years = 500 degree person days.
  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. Sustainable Growth

    https://mlg.eng.cam.ac.uk/carl/words/growth.html
    28 Jun 2024: And finally, in reality we are 7 billion people, not 1 person, who expect growth. ... It could be that people who use it are just a bit sloppy, and what they really mean is 'for 10 years or so' or something like that.
  5. What should we do about Climate Change?

    https://mlg.eng.cam.ac.uk/carl/climate/
    28 Jun 2024: Most people agree that the atmosphere is a common asset, shared by us all.
  6. 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.
  7. Are Current UK Greenhouse gas Emissions Limits Fit For Purpose?

    https://mlg.eng.cam.ac.uk/carl/words/limits.html
    28 Jun 2024: 2. per year. We are around 8 billion people, so that is 5 tons of CO. ... 2. (equivalents). Since we are about 67 million people in the UK, that is 69 tons per person.
  8. 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.
  9. Addressing Climate Change

    https://mlg.eng.cam.ac.uk/carl/climate/eaccs.html
    28 Jun 2024: Most people agree that the only reasonable answer to the question "Who owns the atmosphere?" is: all of us.
  10. Who owns the atmosphere?

    https://mlg.eng.cam.ac.uk/carl/climate/eacc.html
    28 Jun 2024: atmosphere. So, location can't be applied. Most people find it difficult to rationalise any other answer than: all of us. ... This should apply to our common atmospheric resource. You cannot just take from people of other nations, or from future
  11. 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
  12. International Cooperation against Climate Change

    https://mlg.eng.cam.ac.uk/carl/climate/internationalcooperation.html
    28 Jun 2024: It is built on the ethical foundational principle that the right to the atmosphere belongs equally to all people.
  13. 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
  14. The Voluntary International Carbon Alliance

    https://mlg.eng.cam.ac.uk/carl/climate/vica.html
    28 Jun 2024: This distribution reflects the fact that all people collectively own the resource.
  15. 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.
  16. 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.
  17. 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).
  18. 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.
  19. 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.
  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. 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%
  22. 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
  23. 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
  24. 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,.

Refine your results

Search history

Recently clicked results

Recently clicked results

Your click history is empty.

Recent searches

Recent searches

Your search history is empty.