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

    https://mlg.eng.cam.ac.uk/zoubin/course04/cribsheet.pdf
    27 Jan 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.
  6. 4F13: Machine Learning Lectures 1-2: Introduction to Machine Learning …

    https://mlg.eng.cam.ac.uk/zoubin/ml06/lect1-2.pdf
    27 Jan 2023: 1 θ = 1 and θ 0. 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.
  7. - 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!
  8. Directed and Undirected Graphical Models

    https://mlg.eng.cam.ac.uk/adrian/2018-MLSALT4-AW1-models.pdf
    16 May 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.
  9. paper.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/nips96.pdf
    27 Jan 2023: b b b. q qz z z z1 2 3 T. ... in the decision tree at the precedingmoment in time; (b) as an HMM in which the state variable at each moment intime is factorized (cf.
  10. Hidden Markov decision treesMichael I. Jordan�, Zoubin Ghahramaniy,…

    https://mlg.eng.cam.ac.uk/pub/pdf/JorGhaSau96a.pdf
    13 Feb 2023: b b b. q qz z z z1 2 3 T. ... ihoo. d. b). 1. 2. 3. Figure 4: a) Articial time series data.
  11. Junction Tree, BP and Variational Methods

    https://mlg.eng.cam.ac.uk/adrian/2018-MLSALT4-AW3-approx.pdf
    16 May 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).
  12. 4F13: Machine Learning Lectures 6-7: Graphical Models Zoubin…

    https://mlg.eng.cam.ac.uk/zoubin/ml06/lect6-7.pdf
    27 Jan 2023: Z =XaA. XbB. XcC. XdD. XeE. g1(A = a, C = c)g2(B = b, C = c, D = d)g3(C = c, D = d, E = e). ... Undirected Graphical Models. A. C. B. D. E. P (A, B, C, D, E) =1Z.
  13. nlds-final.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/GhaRow98a.pdf
    13 Feb 2023: h>1 h>. 2 : : : h>. I A> B> b>]. >. ... In This Volume.MIT Press, 1999. [2] A.P. Dempster, N.M. Laird, and D.B.
  14. nlds-ftp.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/nlds-ftp.pdf
    27 Jan 2023: A> B> b>]> [1(x) 2(x) : : : I(x) x u 1] :Then, the objective can be writtenmin;Q 8<:Xj (z )>Q1(z )j J ln jQj9=; : (8). ... In This Volume.MIT Press, 1999.[2] A.P. Dempster, N.M. Laird, and D.B.
  15. main.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/Sch09b.pdf
    13 Feb 2023: 13);and third,b is generated from a constrained Gaussian analogous to Eq. ... b) Themixture data consists of 4000 imagesof two mixed digits (20 examples shown).
  16. 13 Feb 2023: In detail, ifp(x) = N (b, B) and the Gaussian kernels on the data points areN (ai = x(i), A = diag(w21,. ,
  17. zgl.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/zgl.pdf
    27 Jan 2023: T _ b: _ b T. T _ b. T _ b is the entropy of the field at the individual unlabeled datapoint b. ... The gradient is computed as [ W! VK X j. T _ b_ b: _ b [ W (12)where the values. _
  18. 13 Feb 2023: a) (b) (c)-2 µ 2-2 µ 2-2 µ 2. 0. v. ... a) Independent Normal and inverseGamma, N (µ|µµ, vµ)IG(v|a, b). b) Normal-inverse-Gamma,N (µ|µµ, vµv)IG(v|a, b).
  19. Adversarial Graph Embeddings for Fair Influence Maximization over…

    https://mlg.eng.cam.ac.uk/adrian/IJCAI20_AdversarialGraphEmbeddings.pdf
    16 May 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.
  20. Bayesian Monte Carlo Carl Edward RasmussenandZoubin GhahramaniGatsby…

    https://mlg.eng.cam.ac.uk/zoubin/papers/RasGha03.pdf
    27 Jan 2023: In detail, ifp(x) = N (b, B) and the Gaussian kernels on the data points areN (ai = x(i), A = diag(w21,. ,
  21. Prediction at an Uncertain Input for GaussianProcesses and Relevance…

    https://mlg.eng.cam.ac.uk/pub/pdf/QuiGirRas03.pdf
    13 Feb 2023: C = (Λ1 S1)1. cj = C(Λ1xj S. 1u) (32). 1N(a, A)N(b, B) N(c, C) with C = (A1 B1)1, c = C(A1a B1b) and normalizing constantzc = ... 2π)D/2|C|1/2|A|1/2|B|1/2 exp.
  22. Scalable Gaussian Process Structured Prediction for Grid Factor Graph …

    https://mlg.eng.cam.ac.uk/pub/pdf/BraQuaNowGha14.pdf
    13 Feb 2023: b b b. b b b. b b. b ba1. a2. ... bb. b. b. ai. Figure 1. Left: Grid factor graph with a pairwise 4-connected fac-tor.
  23. Learning with Multiple Labels

    https://mlg.eng.cam.ac.uk/pub/pdf/JinGha02a.pdf
    13 Feb 2023: I p(y I x"B) B i I Si I YE S,. ... prediction p(y I x;, B). We will call this model 'EMPrior Model'.
  24. 27 Jan 2023: Ü Ø Þö øùú ì ùûD. Þ. üýeþgÿþgÿeþ CÿTþlýeþgÿÿ "!#$Tý%þ&' GÿlÿTþ(ÿ)&%"vÿTþ(,.-0/ÿvÿ (132546,Tý7!#3 ( 8qþ9:8;78qþ ( =<3 <>!9eÿþ 78;@8qþ( = A%þ(B!#BTýDCZþFE:!GH7IJJ% ... ªº O - ¤ b B» z ¤ £ x y z ¤ O £ x x y
  25. 27 Jan 2023: Ü Ø Þö øùú ì ùûD. Þ. üýeþgÿþgÿeþ CÿTþlýeþgÿÿ "!#$Tý%þ&' GÿlÿTþ(ÿ)&%"vÿTþ(,.-0/ÿvÿ (132546,Tý7!#3 ( 8qþ9:8;78qþ ( =<3 <>!9eÿþ 78;@8qþ( = A%þ(B!#BTýDCZþFE:!GH7IJJ% ... ªº O - ¤ b B» z ¤ £ x y z ¤ O £ x x y
  26. 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. ])=.
  27. Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions

    https://mlg.eng.cam.ac.uk/pub/pdf/ZhuGhaLaf03a.pdf
    13 Feb 2023: T _ b: _ b T. T _ b. T _ b is the entropy of the field at the individual unlabeled datapoint b. ... The gradient is computed as [ W! VK X j. T _ b_ b: _ b [ W (12)where the values. _
  28. 16 May 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.
  29. Cambridge Machine Learning Group Publications

    https://mlg.eng.cam.ac.uk/pub/authors/
    13 Feb 2023: by year:. [Tameem Adel. George Nicholson, Marta Blangiardo, Mark Briers, Peter J Diggle, Tor Erlend Fjelde, Hong Ge, Robert J B Goudie, Radka Jersakova, Ruairidh E King, Brieuc C L Lehmann, ... J. von Kügelgen, A.-H. Karimi, U. Bhatt, I. Valera, A.
  30. Background material crib-sheet Iain Murray , October 2003 Here ...

    https://mlg.eng.cam.ac.uk/zoubin/course03/cribsheet.pdf
    27 Jan 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.
  31. 13 Feb 2023: L. Gresele, J. von Kügelgen, J. M. Kübler, E. Kirschbaum, B. ... J. von Kügelgen, A.-H. Karimi, U. Bhatt, I. Valera, A. Weller, and B.
  32. A Unified Approach to Quantifying Algorithmic Unfairness: Measuring…

    https://mlg.eng.cam.ac.uk/adrian/KDD2018_inequality_indices.pdf
    16 May 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
  33. Blind Justice: Fairness with Encrypted Sensitive Attributes

    https://mlg.eng.cam.ac.uk/adrian/ICML18-BlindJustice.pdf
    16 May 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.
  34. gf2gp.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/gf2gp.pdf
    27 Jan 2023: vNm4y=Zvx|¥zkkzpwMv¥zBkZz}BkvNtpkMp8emvwvxmzzkZB|kuB'kumkvzy=wDpw yQvzkum wDpwp k kVBNp Mv kumvzMkzp wDpw BkMpz Bv zkkB|kumBkzpmZkvxV}kv8tpZºA@.B B Pµumk B ªºmvxMwDpwzpmZkvx}BkvNtpkºµkuVkx|vxmvxkevxmM}m|vNmpwDDp ... D 0.
  35. /users/joe/src/tops/dvips

    https://mlg.eng.cam.ac.uk/pub/pdf/UedNakGha00a.pdf
    13 Feb 2023: Typically, b = 8 or b = 16 is employed. Each block is regarded as ad(= bb)-dimensional vector x. ... J. R. Statist. Soc. B, 59(4), 731–792. Sugiyama, Y., & Ariki, Y.
  36. Bayesian Knowledge Corroboration with LogicalRules and User Feedback…

    https://mlg.eng.cam.ac.uk/pub/pdf/KasVanGraHer10.pdf
    13 Feb 2023: B., Santos, L. L.,Matsumoto, S.: A First-Order Bayesian Tool for Probabilistic Ontologies. ... AAAI Press (2008). 30. Frey, B. J., Mackay, D. J. C.: A Revolution: Belief Propagation in Graphs withCycles.
  37. On the Convergence of Bound Optimization Algorithms Ruslan…

    https://mlg.eng.cam.ac.uk/pub/pdf/SalRowGha03a.pdf
    13 Feb 2023: A. A. B. B. 0 50 100 150 200 250 300. ... ihoo. d. Con. st. Logistic Regression. A. B, C. 0 5 10 15 20 250.
  38. Unifying Orthogonal Monte Carlo Methods

    https://mlg.eng.cam.ac.uk/adrian/ICML2019-unified.pdf
    16 May 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.
  39. Machine Learning Group Publications

    https://mlg.eng.cam.ac.uk/pub/topics/
    13 Feb 2023: Henry B. Moss, Sebastian W. Ober, and Victor Picheny.
  40. psb04.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/DubHwaRan04a.pdf
    13 Feb 2023: A tX ¿Î;BgªQµ|XXºJ X D«OVirª"µrXÀiiD e@ ) B b_µrªiDj_«_|«OXº_µr ªeµrX ß««_0;«OjiDªµDiÀXirÑ e@!Xr 8 /ªQµ|XD3 a«_#e«O;_µ;«_jªc_ ¿ Ì ... 81È»µr|O_e«_'X;¿B«_»_B&«_B_#XD«ªr_e«_DÀXº{_µrG6ÑH1IÒOÃjrDª@;É_µ"_µ
  41. Conditions Beyond Treewidth for Tightness of Higher-order LP…

    https://mlg.eng.cam.ac.uk/adrian/conditions.pdf
    16 May 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.
  42. 16 May 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.
  43. psb04.dvi

    https://mlg.eng.cam.ac.uk/pub/pdf/DubHwaRanetal04.pdf
    13 Feb 2023: A tX ¿Î;BgªQµ|XXºJ X D«OVirª"µrXÀiiD e@ ) B b_µrªiDj_«_|«OXº_µr ªeµrX ß««_0;«OjiDªµDiÀXirÑ e@!Xr 8 /ªQµ|XD3 a«_#e«O;_µ;«_jªc_ ¿ Ì ... 81È»µr|O_e«_'X;¿B«_»_B&«_B_#XD«ªr_e«_DÀXº{_µrG6ÑH1IÒOÃjrDª@;É_µ"_µ
  44. 13 Feb 2023: ÐÑ Äê? ÐÑ 9<HQßÄÔõÇÉV ÿ ü åhýÿ ü åhý Z E6 _>@?ABXGRSBcN@Zu;,B]?AHïõê? ... q w,#]uÉC#],,}}v ,O k ,O ,O ¢ C,vÉí#XS,}}v < < S ,O qsÎSnStCÀq,} B < 1 n S B.
  45. Bethe and Related Pairwise Entropy Approximations Adrian…

    https://mlg.eng.cam.ac.uk/adrian/Weller_UAI15_BetheAndRelated.pdf
    16 May 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.
  46. Flexible Martingale Priors for Deep Hierarchies Jacob Steinhardt…

    https://mlg.eng.cam.ac.uk/pub/pdf/SteGha12.pdf
    13 Feb 2023: con-sisting of sets of the form US,a,b. We then claim thatC := {S | US,a,b B′} is the desired collection of mea-surable sets. ... Since D is Hausdorff, there exists someUS,a,b B′ such that p US,a,b and q 6 US,a,b, whichin particular implies that Pp[X
  47. chaptertr.dvi

    https://mlg.eng.cam.ac.uk/zoubin/papers/advmf.pdf
    27 Jan 2023: Q() is a produ t of Gamma densities Q(i) = G(i; a; bi)where a = a T2 , b = b 12gi, gi = PTt=1 y2ti Ui(diag() W 0)1U>i , ... J. Royal Statisti al So iety B, pages 157{224, 1988.[22 D.
  48. Practical Probabilistic Programming with Monads

    https://mlg.eng.cam.ac.uk/pub/pdf/SciGhaGor15.pdf
    13 Feb 2023: Specifically, consider the followingdesign. data PDist a whereReturn :: a -> PDist aPBind :: PDist b -> (b -> PDist a) -> PDist aPrimitive :: Sampleable d => d a -> PDist a. ... data CDist a wherePD :: PDist a -> CDist aCBind :: CDist b -> (b -> PDist a)
  49. 27 Jan 2023: 6ûý 3. 68òBð¡ñ1ðEþ)ú¡ÿ,ÆðJõú¡ïxð1òÃújúùóÐÐóÆÐðlú:jóøWÐûA 3. 6jûòBð!Æÿ,Æðô =. bdc (û/A3! -. 6 m x #"!3!$I6 "!3 O$I6 %B&%53'$ 6bdc3 (û/A3! -.
  50. LNAI 3176 - Unsupervised Learning

    https://mlg.eng.cam.ac.uk/pub/pdf/Gha03a.pdf
    13 Feb 2023: Sum-ming out C leads to P (A, B) = P (A)P (B). ... c P (C|A = a, B = a)),and continue this procedure until all variables are assigned values.
  51. 27 Jan 2023: C. B. D. E. Figure 1: Three kinds of probabilistic graphical model: undirected graphs, factor graphs and directed graphs. ... P (A, B, C, D, E) = c g1(A, C)g2(B, C, D)g3(C, D, E) (28).

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