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  2. Discovering Interpretable Representations for Both Deep Generative…

    https://mlg.eng.cam.ac.uk/adrian/ICML18-Discovering.pdf
    16 May 2024: Significant re-sults are identified using a paired t-test with p = 0.05. ... The SVHN dataset contains 73,257training digits (instances) and 26,032 test digits.
  3. Cambridge Machine Learning Group Publications

    https://mlg.eng.cam.ac.uk/pub/authors/
    13 Feb 2023: Finally, the fourth conversion yields an algorithm for synthesising program source code from input-output examples that is able to solve test problems 1-3 orders of magnitude faster than a ... Versa substitutes optimization at test time with forward
  4. From Parity to Preference-based Notionsof Fairness in Classification…

    https://mlg.eng.cam.ac.uk/adrian/NeurIPS17-from-parity-to-preference.pdf
    16 May 2024: In this paper, we draw inspiration from the fair-division and envy-freeness literature in economics and game theory and proposepreference-based notions of fairness—given the choice between various sets ... Finally,we train the five classifiers
  5. Adrian Weller

    https://mlg.eng.cam.ac.uk/adrian/
    16 May 2024: Adrian serves on the boards of several organizations. He is a member of the World Economic Forum Global Future Council on the Future of AI, and is co-director of the ... Train and test tightness of LP relaxations in structured prediction.
  6. 13 Feb 2023: In this work, we test how well-automated methods can detect conversational behaviors and replace an expert human annotator. ... Abstract: Domain generalization (DG) seeks predictors which perform well on unseen test distributions by leveraging data drawn
  7. Transparency: Motivations and Challenges? Adrian…

    https://mlg.eng.cam.ac.uk/adrian/transparency.pdf
    16 May 2024: truth.”. Defining criteria and tests for practical faithfulness are important open pro-blems. ... 53. Prat, A.: The wrong kind of transparency. American Economic Review 95(3),862–877 (2005).
  8. Machine Learning Group Publications

    https://mlg.eng.cam.ac.uk/pub/topics/
    13 Feb 2023: Publications, Machine Learning Group, Department of Engineering, Cambridge. current group:. [former members:. [by year:. [Gaussian Processes and Kernel Methods. Gaussian processes are non-parametric distributions useful for doing Bayesian inference
  9. 13 Feb 2023: may have contained data close to the test in-put, which hadn’t been designated as relevance vectors. ... For the robot arm we use dis-joint test and training sets both of 2000 cases.
  10. LNAI 7524 - Modelling Input Varying Correlations between Multiple…

    https://mlg.eng.cam.ac.uk/pub/pdf/WilGha12a.pdf
    13 Feb 2023: modeling through spatially varying coregionalization. Test 13(2), 263–312 (2004)Gouriéroux, C.: ARCH models and financial applications. ... The Re-. view of Economic Studies 61(2), 247–264 (1994)Murray, I., Adams, R.P., MacKay, D.J.: Elliptical
  11. A Unified Approach to Quantifying Algorithmic Unfairness: Measuring…

    https://mlg.eng.cam.ac.uk/adrian/KDD2018_inequality_indices.pdf
    16 May 2024: In this paper, we propose to quantify unfairness using inequal-ity indices that have been extensively studied in economics andsocial welfare [3, 10, 19]. ... For all experiments, we repeatedly split the data into 70%-30%train-test sets 10 times and

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