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  2. Background material crib-sheet Iain Murray , October 2003 Here ...

    https://mlg.eng.cam.ac.uk/teaching/4f13/cribsheet.pdf
    19 Nov 2023: P (A = a|B = b) is the probability A = a occurs given the knowledge B = b. ... Note. a P (A = a, B = b|H) =. P (B = b|H) gives the normalising constant of proportionality.
  3. Gaussian Process

    https://mlg.eng.cam.ac.uk/teaching/4f13/2122/gaussian%20process.pdf
    19 Nov 2023: b. ],[A B. B> C. ])= p(x|y) = N(a BC1(y b), ABC1B>),. ... For Gaussians:. p(fn, f<n) = N([ a. b. ],[A B. B> C. ])=.
  4. Gaussian Process

    https://mlg.eng.cam.ac.uk/teaching/4f13/1819/gaussian%20process.pdf
    19 Nov 2023: b. ],[A B. B> C. ])= p(x|y) = N(a BC1(y b), ABC1B>),. ... For Gaussians:. p(fn, f<n) = N([ a. b. ],[A B. B> C. ])=.
  5. 4F13 Machine Learning: Coursework #3: Latent Dirichlet Allocation…

    https://mlg.eng.cam.ac.uk/teaching/4f13/1314/cw/coursework3.pdf
    19 Nov 2023: How manydocuments, how many words and how many unique words are there in A, in B and in the union of Aand B? ... What is theper-word perplexity over all documents in B? f) 10% : What would the perplexity be for a uniform multinomial?
  6. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/0809/lect04.pdf
    19 Nov 2023: E(a) (b). Two types of nodes:• The circles in a factor graph. ... Z =aA. bB. cC. dD. eE. g1(A = a, C = c)g2(B = b, C = c, D = d)g3(C = c, D = d, E = e).
  7. - Machine Learning 4F13, Michaelmas 2015

    https://mlg.eng.cam.ac.uk/teaching/4f13/1516/lect0102.pdf
    19 Nov 2023: N(x|a, A) N(P> x|b, B) = zc N(x|c, C). • is proportional to a Gaussian density function with covariance and mean. ... 1(b P> a). )Ghahramani Lecture 1 and 2: Probabilistic Regression 38 / 38.
  8. Adversarial Graph Embeddings for Fair Influence Maximization over…

    https://mlg.eng.cam.ac.uk/adrian/IJCAI20_AdversarialGraphEmbeddings.pdf
    19 Jun 2024: distributions of nodes from A and B in the embedding space(to have |UA||A|. ... Group AGroup BGroup AGroup B. (b) The fractions of influencednodes in the two groups.
  9. Gaussian Process

    https://mlg.eng.cam.ac.uk/teaching/4f13/1718/gaussian%20process.pdf
    19 Nov 2023: b. ],[A B. B> C. ])= p(x|y) = N(a BC1(y b), ABC1B>),. ... For Gaussians:. p(fn, f<n) = N([ a. b. ],[A B. B> C. ])=.
  10. 4F13 Machine Learning: Coursework #3: Latent Dirichlet Allocation…

    https://mlg.eng.cam.ac.uk/teaching/4f13/1213/cw/coursework3.pdf
    19 Nov 2023: How manydocuments, how many words and how many unique words are there in A, in B and in the union of Aand B? ... What is theper-word perplexity over all documents in B? f) 10% : What would the perplexity be for a uniform multinomial?
  11. Blind Justice: Fairness with Encrypted Sensitive Attributes

    https://mlg.eng.cam.ac.uk/adrian/ICML18-BlindJustice.pdf
    19 Jun 2024: Wethen multiply the sum by b/n > 2m. As long as b, b/n(and thus also n/b) can be represented with sufficient preci-sion, which is the case in ... Details about parameters and thealgorithm can be found in Section B in the appendix.
  12. 19 Jun 2024: Partition thevariable indices into two subsets, A [n] = {1,. ,n}and B = [n] A. ... Proof B. We provide an alternative derivation which essen-tially incorporates the flipping into the proof.
  13. Bethe and Related Pairwise Entropy Approximations Adrian…

    https://mlg.eng.cam.ac.uk/adrian/Weller_UAI15_BetheAndRelated.pdf
    19 Jun 2024: D. Schlesinger and B. Flach. Transforming an arbitrary minsumproblem into a binary one. ... a,b B = {0,1}.Note that a third ‘dimension’ restricted to the value 1 has been added for notational convenience.
  14. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/0910/lect04.pdf
    19 Nov 2023: Z =aA. bB. cC. dD. eE. g1(A = a, C = c)g2(B = b, C = c, D = d)g3(C = c, D = d, E = e). ... A. D. C. B. E. A. D. C. B. E(a) (b) (c).
  15. - Machine Learning 4F13, Michaelmas 2015

    https://mlg.eng.cam.ac.uk/teaching/4f13/1516/lect0304.pdf
    19 Nov 2023: p(x, y) = N([ a. b. ],[A B. B> C. ])= p(x) = N(a, A),. ... b. ],[A B. B> C. ])= p(x|y) = N(a BC1(y b), ABC1B>),.
  16. Gaussian Process

    https://mlg.eng.cam.ac.uk/teaching/4f13/1617/gaussian%20process.pdf
    19 Nov 2023: b. ],[A B. B> C. ])= p(x|y) = N(a BC1(y b), ABC1B>),. ... For Gaussians:. p(fn, f<n) = N([ a. b. ],[A B. B> C. ])=.
  17. 4F13 Machine Learning: Coursework #3: Latent Dirichlet Allocation…

    https://mlg.eng.cam.ac.uk/teaching/4f13/1617/cw/coursework3.pdf
    19 Nov 2023: How manydocuments, how many words and how many unique words are there in A, in B and in the union of Aand B? ... Whatis the per-word perplexity over all documents in B? f) 10% : What would the perplexity be for a uniform multinomial?
  18. 4F13 Machine Learning: Coursework #3: Latent Dirichlet Allocation…

    https://mlg.eng.cam.ac.uk/teaching/4f13/1516/cw/coursework3.pdf
    19 Nov 2023: How manydocuments, how many words and how many unique words are there in A, in B and in the union of Aand B? ... Whatis the per-word perplexity over all documents in B? f) 10% : What would the perplexity be for a uniform multinomial?
  19. (Multivariate) Gaussian (Normal) Probability Densities

    https://mlg.eng.cam.ac.uk/teaching/4f13/2324/gaussian%20and%20matrix%20equations.pdf
    19 Nov 2023: p(x, y) = N([ a. b. ],[A B. B> C. ])= p(x) = N(a, A),. ... b. ],[A B. B> C. ])= p(x|y) = N(a BC1(y b), ABC1B>),.
  20. Methods for Inference in Graphical Models

    https://mlg.eng.cam.ac.uk/adrian/phd_FINAL.pdf
    19 Jun 2024: 142. B.2.3 Some frustrated K4 structures (treewidth 3). 145. B.3 Discussion. ... i,j) E and a,b B, which we term the edge or pairwise potentials.
  21. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/0708/lect04.pdf
    19 Nov 2023: E(a) (b). Two types of nodes:• The circles in a factor graph. ... Z =aA. bB. cC. dD. eE. g1(A = a, C = c)g2(B = b, C = c, D = d)g3(C = c, D = d, E = e).
  22. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/1011/lect04.pdf
    19 Nov 2023: Z =aA. bB. cC. dD. eE. g1(A = a, C = c)g2(B = b, C = c, D = d)g3(C = c, D = d, E = e). ... A. D. C. B. E. A. D. C. B. E(a) (b) (c).
  23. - Machine Learning 4F13, Spring 2014

    https://mlg.eng.cam.ac.uk/teaching/4f13/1314/lect0304.pdf
    19 Nov 2023: b. ],[A B. B> C. ])= p(x|y) = N(a BC1(y b), ABC1B>),. ... For Gaussians:. p(fi, f<i) = N([ a. b. ],[A B. B> C. ])=
  24. - Machine Learning 4F13, Spring 2015

    https://mlg.eng.cam.ac.uk/teaching/4f13/1415/lect0304.pdf
    19 Nov 2023: b. ],[A B. B> C. ])= p(x|y) = N(a BC1(y b), ABC1B>),. ... For Gaussians:. p(fn, f<n) = N([ a. b. ],[A B. B> C. ])=.
  25. Gaussian Process

    https://mlg.eng.cam.ac.uk/teaching/4f13/2324/gaussian%20process.pdf
    19 Nov 2023: p(x, y) = p([ x. y. ])= N. ([ ab. ],[. A B. B> C. ]),. we get the marginal distribution of x, p(x) by. ... For Gaussians:. p(fn, f<n) = N([ a. b. ],[A B. B> C. ])=.
  26. 4F13 Machine Learning: Coursework #2: Latent Dirichlet Allocation…

    https://mlg.eng.cam.ac.uk/teaching/4f13/1112/cw/coursework2.pdf
    19 Nov 2023: How many documents, how many words and how many unique words are there inA, in B and in the union of A and B? ... What is the per-wordperplexity? What is the per-word perplexity over all documents in B?
  27. 4F13 Machine Learning: Coursework #3: Latent Dirichlet Allocation…

    https://mlg.eng.cam.ac.uk/teaching/4f13/1415/cw/coursework3.pdf
    19 Nov 2023: How manydocuments, how many words and how many unique words are there in A, in B and in the union of Aand B? ... What is theper-word perplexity over all documents in B? f) 10% : What would the perplexity be for a uniform multinomial?
  28. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/1213/lect0304.pdf
    19 Nov 2023: p(x, y) = N([ a. b. ],[A B. B> C. ])= p(x|y) = N(aBC1(yb), ABC1B>),. ... b. ],[A B. B> C. ])= p(x|y) = N(aBC1(yb), ABC1B>). Do try this at home!
  29. 3F3: Signal and Pattern Processing Lecture 1: Introduction to ...

    https://mlg.eng.cam.ac.uk/teaching/3f3/1011/lect1.pdf
    19 Nov 2023: Some distributions (cont). Uniform (x [a,b]):p(x|a,b) =. {1ba if a x b0 otherwise. ... Gamma (x 0):p(x|a,b) = b. a. Γ(a)xa1 exp{bx}. Beta (x [0, 1]):p(x|α,β) = Γ(α β).
  30. - 4F13: Machine Learning

    https://mlg.eng.cam.ac.uk/teaching/4f13/1112/lect0304.pdf
    19 Nov 2023: p(x, y) = N([ a. b. ],[A B. B> C. ])= p(x|y) = N(aBC1(yb), ABC1B>),. ... b. ],[A B. B> C. ])= p(x|y) = N(aBC1(yb), ABC1B>). Do try this at home!
  31. Directed and Undirected Graphical Models

    https://mlg.eng.cam.ac.uk/adrian/2018-MLSALT4-AW1-models.pdf
    19 Jun 2024: Z. (b). X. Y. Z. 12 / 26. D-separation (“directed separation”) in Bayesian networks. ... over A,B,C :. p(D) =a,b,c. p(A = a,B = b,C = c,D). 28 / 26.
  32. Junction Tree, BP and Variational Methods

    https://mlg.eng.cam.ac.uk/adrian/2018-MLSALT4-AW3-approx.pdf
    19 Jun 2024: over A,B,C :. p(D) =. a,b,c. p(A = a,B = b,C = c,D). 8 / 32. ... c. b. a. p(D | c)p(c | b)p(b | a)p(a).
  33. 19 Jun 2024: ergy. ES. B. (a) W = 1. 0 0.5 10.8. 0.6. ... B. (b) W = 1.38. 0 0.5 10.4. 0.3. 0.2. 0.1.
  34. A Unified Approach to Quantifying Algorithmic Unfairness: Measuring…

    https://mlg.eng.cam.ac.uk/adrian/KDD2018_inequality_indices.pdf
    19 Jun 2024: More precisely,let b′ = ⟨b, ,b⟩ Rnk0 be a k-replication of b. ... That is, forany b R0, I(b,b, ,b) = 0. In addition to the above four principles satisfied by many in-equality indices, we also focus on the following property whichis
  35. Unifying Orthogonal Monte Carlo Methods

    https://mlg.eng.cam.ac.uk/adrian/ICML2019-unified.pdf
    19 Jun 2024: Let B be a set satisfying diam(B) B for someuniversal constant B that does not depend on d (B mightbe for instance a unit sphere). ... Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B.,and Smola, A.
  36. Conditions Beyond Treewidth for Tightness of Higher-order LP…

    https://mlg.eng.cam.ac.uk/adrian/conditions.pdf
    19 Jun 2024: Let P ={x Rm|Ax b} be a polytope for some A =[a1,. ... ak]. > Rkm, b Rk (for some k N). Thenfor v Ext(P), we have.

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