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41 - 50 of 51 search results for TALK:PC53 20 |u:mlg.eng.cam.ac.uk where 0 match all words and 51 match some words.

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  2. The Geometry of Random Features Krzysztof Choromanski∗1 Mark…

    https://mlg.eng.cam.ac.uk/adrian/geometry.pdf
    19 Jun 2024: The Geometry of Random Features. Krzysztof Choromanski1 Mark Rowland2 Tamas Sarlos1 Vikas Sindhwani1 Richard E. Turner2 Adrian Weller231Google Brain, NY 2University of Cambridge, UK 3The Alan Turing Institute, UK. Abstract. We present an in-depth

  3. Blind Justice: Fairness with Encrypted Sensitive Attributes

    https://mlg.eng.cam.ac.uk/adrian/ICML18-BlindJustice.pdf
    19 Jun 2024: In STOC, pp. 218–229. ACM,1987. Graham, C. NHS cyber attack: Everything you need toknow about ’biggest ransomware’ offensive in history.Telegraph, May 20, 2017.

  4. 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: SHAP [23], 5. LIME [26], and 6. Guided-backpropagation [32]. Our approach introduces an explanation loss term during training.This is similar to [20], who propose a loss function which enforcesan ... compas age 4.01.69 4.341.82 -2.230.66 -3.20.91

  5. Conditions Beyond Treewidth for Tightness of Higher-order LP…

    https://mlg.eng.cam.ac.uk/adrian/conditions.pdf
    19 Jun 2024: 20). If V01 = V10 = , we are done as v? =

  6. Unifying Orthogonal Monte Carlo Methods

    https://mlg.eng.cam.ac.uk/adrian/ICML2019-unified.pdf
    19 Jun 2024: Then we have:. E[(F̃i,LDix̃)λ(F̃. i,LDiỹ)λ(F̃i,LDix̃)µv(F̃. i,LDiỹ)µv. ]= (20). 1. 4E [x̃λỹλx̃µvỹµv x̃λỹλx̃µveiỹµvei x̃λeiỹλeix̃µvỹµv x̃λeiỹλeix̃µveiỹµvei].

  7. The Unreasonable Effectiveness of StructuredRandom Orthogonal…

    https://mlg.eng.cam.ac.uk/adrian/NeurIPS17-unreasonable-effectiveness.pdf
    19 Jun 2024: 20). Let us now compute E[XiXj]. Writing Z1 = ri, Z2 = rj, we begin with some geometric observa-tions:. • ... π/20. dφf(φ) cos2(φ). π0. dϕ1 sinn3(ϕ1). π0. dϕ2 sinn4(ϕ2). 13. (sin2(φ) cos2(ϕ1) sin.

  8. Orthogonal Estimation of Wasserstein Distances Mark Rowland∗1 Jiri…

    https://mlg.eng.cam.ac.uk/adrian/AISTATS19-slicedwasserstein.pdf
    19 Jun 2024: 18). = minσSM. 1. M. Mm=1. ‖xm yσ(m)‖p2 (19). = Wpp(η,µ) , (20).

  9. Geometrically Coupled Monte Carlo Sampling Mark Rowland∗University of …

    https://mlg.eng.cam.ac.uk/adrian/NeurIPS18-gcmc.pdf
    19 Jun 2024: 0.00 0.05 0.10 0.15 0.20 0.25Discrepancy. 0.0. 2.5. 5.0. 7.5. 10.0.

  10. Tightness of LP Relaxations for Almost Balanced Models Adrian ...

    https://mlg.eng.cam.ac.uk/adrian/tricam.pdf
    19 Jun 2024: Tightness of LP Relaxations for Almost Balanced Models. Adrian Weller Mark Rowland David SontagUniversity of Cambridge University of Cambridge New York University. Abstract. Linear programming (LP) relaxations are widelyused to attempt to identify a

  11. Bethe and Related Pairwise Entropy Approximations Adrian…

    https://mlg.eng.cam.ac.uk/adrian/Weller_UAI15_BetheAndRelated.pdf
    19 Jun 2024: Figure 3: Average over 20 runs of singleton marginal vs. uniformsymmetric edge weight W for: exact inference, Bethe approxima-tion, Bethemarginal polytope, and TRW (all ρij = 2/3).

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