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  2. Addressing Climate Change

    https://mlg.eng.cam.ac.uk/carl/climate/eaccs.html
    25 Jun 2024: Membership immediately creates economic pressure to cut emissions (for all members, not just large emitters).
  3. International Cooperation against Climate Change

    https://mlg.eng.cam.ac.uk/carl/climate/internationalcooperation.html
    25 Jun 2024: Strong individual economic pressures conflict with our common global interests. Only through global cooperation can individual and common incentives be re-aligned. ... This creates strong economic pressures to keep using fossil fuels. The real
  4. arXiv:0906.4032v1 [cs.LG] 22 Jun 2009

    https://mlg.eng.cam.ac.uk/pub/pdf/BorGha09a.pdf
    13 Feb 2023: An associated test is called a two-sample test. Such tests are encountered invarious disciplines from the life sciences to the social sciences:. • ... 3. 3 Concept of Bayesian two-sample tests. 3.1 Bayes factor as test criterion.
  5. https://mlg.eng.cam.ac.uk/zoubin/misc/karna.txt

    https://mlg.eng.cam.ac.uk/zoubin/misc/karna.txt
    27 Jan 2023: This could take time and test our patience. Q. Once the enemy is defined, is violence the proper response? ... It may require education. It will require time and may test our patience.
  6. https://mlg.eng.cam.ac.uk/index.xml

    https://mlg.eng.cam.ac.uk/index.xml
    10 Apr 2024: Cambridge Machine Learning Group https://mlg.eng.cam.ac.uk/ Cambridge Machine Learning Group Wowchemy (https://wowchemy.com) en-us Wed, 22 Mar 2023 00:00:00 0000
  7. images/test_user_webdesign.eps

    https://mlg.eng.cam.ac.uk/pub/pdf/IwaShaGha13a.pdf
    13 Feb 2023: The vertical axis is the test likelihood, and the horizontal is the test time period. ... Thevertical axis is the test likelihood, and the horizontal is the test time period.
  8. ML-IRL: Machine Learning in Real Life Workshop at ICLR ...

    https://mlg.eng.cam.ac.uk/adrian/ML_IRL_2020-Counterfactual_Accuracy.pdf
    19 Jun 2024: would we have to give up so that the predictionfor the test point would change? ... 2017)), and then we constrain fora random test point to obtain counterfactual accuracy.
  9. Evaluating and Aggregating Feature-based Model Explanations

    https://mlg.eng.cam.ac.uk/adrian/IJCAI20_EvaluatingAndAggregating.pdf
    19 Jun 2024: For Iris [Dua and Graff, 2017], we train our modelto 96% test accuracy. ... 45). Table 2: Faithfulness µF averaged over a test set: (Zero Baseline,Training Average Baseline).
  10. - 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. ... One-sided and two-sided tests. Depending on the circumstances, it may be necessary to use a two-sided test.
  11. The Geometry of Random Features Krzysztof Choromanski∗1 Mark…

    https://mlg.eng.cam.ac.uk/adrian/geometry.pdf
    19 Jun 2024: pre-dictive distribution obtained by an exactly-trained GP, and(ii) predictive RMSE on test sets. ... Figure 8: Approximate GP regression results on Bostondataset. Reported numbers are average test RMSE, alongwith bootstrap estimates of standard error

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