Search

Search Funnelback University

Search powered by Funnelback
21 - 40 of 52 search results for katalk:PC53 24 / |u:mlg.eng.cam.ac.uk where 0 match all words and 52 match some words.
  1. Results that match 1 of 2 words

  2. Junction Tree, BP and Variational Methods

    https://mlg.eng.cam.ac.uk/adrian/2018-MLSALT4-AW3-approx.pdf
    19 Jun 2024: 24 / 32. The log-partition function log Z. Since D(q‖p) 0, we have. ... a finite convex hull 3. M(G) = conv {φ(e), e X m} (5.24).
  3. Bounding the Integrality Distance ofLP Relaxations for Structured…

    https://mlg.eng.cam.ac.uk/adrian/OPT2016_paper_3.pdf
    19 Jun 2024: 24)Using the same approach to upper-bound Equation 22, we assume, without loss of generality, that(δ(µI ) θ) µ̃L (δ(µI ) θ. ′) ... m. (25). Using Equation 25 to upper-bound 22, and Equation 24 to upper-bound 23, we have that.
  4. 19 Jun 2024: FA8750-14-C-0005. 2 8 16 24 320. 20. 40. 60. 80. 100. ... Maximum coupling strength y. BethelocalBethecycleBethemargTRWlocalTRWcycleTRWmarg. (a) log partition error. 2 8 16 24 320.
  5. Transparency: Motivations and Challenges? Adrian…

    https://mlg.eng.cam.ac.uk/adrian/transparency.pdf
    19 Jun 2024: Notions of fairness beyond equality, and the role ofrandomness in fairness, were recently explored [24, 74]. ... Advances in experimental social psychology 24, 319–359 (1991). 26. Hammond, R., Axelrod, R.: The evolution of ethnocentrism.
  6. ML-IRL: Machine Learning in Real Life Workshop at ICLR ...

    https://mlg.eng.cam.ac.uk/adrian/ML_IRL_2020-CLUE.pdf
    19 Jun 2024: 6). LSATAl. (7). COMPASEp. (6). COMPASAl. (5) Total (24). Prolific Unc.
  7. What Keeps a Bayesian Awake At Night? Part 2: Night Time · Cambridge…

    https://mlg.eng.cam.ac.uk/blog/2021/03/31/what-keeps-a-bayesian-awake-at-night-part-2.html
    12 Apr 2024: Paris (1994, p. 24): “[W]hen an attempt is made to fill in all the details some of the attractiveness of the original is lost.”.
  8. Now You See Me (CME): Concept-based Model Extraction

    https://mlg.eng.cam.ac.uk/adrian/AIMLAI20-CME.pdf
    19 Jun 2024: Model ExtractionModel extraction techniques use rules [20, 21, 22], de-cision trees [23, 24], or other more readily explainablemodels [25] to approximate complex models, in orderto study their behaviour. ... 24] M. Sato, H. Tsukimoto, Rule extraction
  9. Ode to an ODE Krzysztof Choromanski ∗Robotics at Google ...

    https://mlg.eng.cam.ac.uk/adrian/NeurIPS20-ODEtoODE.pdf
    19 Jun 2024: MIT Press, 2016. [24] Aditya Grover, Eric Wang, Aaron Zweig, and Stefano Ermon. ... In Proceedings of the 33nd International Conference on Machine Learning,ICML 2016, New York City, NY, USA, June 19-24, 2016, pages 2034–2042, 2016.
  10. C:/Users/Adrian/Documents/GitHub/betheClean/docs/nb-UAI.dvi

    https://mlg.eng.cam.ac.uk/adrian/nb-UAI.pdf
    19 Jun 2024: Approximating the Bethe Partition Function. Adrian WellerDepartment of Computer Science. Columbia UniversityNew York NY 10027. adrian@cs.columbia.edu. Tony JebaraDepartment of Computer Science. Columbia UniversityNew York NY 10027. jebara@cs.columbia
  11. 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
  12. Blind Justice: Fairness with Encrypted Sensitive Attributes

    https://mlg.eng.cam.ac.uk/adrian/ICML18-BlindJustice.pdf
    19 Jun 2024: d features 51 62 7 24 23p sensitive attr. 1 1 7 1 1certification 802 ms 827 ms 288 ms 250 ms 765 mstraining 43 min 51 min 7 min 1 ... Bennett Capers, I. Blind justice. Yale Journal of Law &Humanities, 24:179, 2012.
  13. Human Perceptions of Fairness in Algorithmic Decision Making: A Case…

    https://mlg.eng.cam.ac.uk/adrian/WWW18-HumanPerceptions.pdf
    19 Jun 2024: 47% 68% 70%B.S. 51% 32% 30%Liberal 57% 37% 33%Conservative 17% 24% 29%Moderate 21% 33% 34%Other 5% 6% 4%. ... Substance Abuse 4.84 0.08 0.07 0.10 0.24 0.07 0.68 0.26 0.22 0.20 0.07 0.284.
  14. An Introduction to LP Relaxations for MAP Inference

    https://mlg.eng.cam.ac.uk/adrian/2018-MLSALT4-AW2-LP.pdf
    19 Jun 2024: x3. x4. x5. x6. s. balanced almost balanced(attractive up to flipping) 24 / 41.
  15. Evaluating and Aggregating Feature-based Model Explanations

    https://mlg.eng.cam.ac.uk/adrian/IJCAI20_EvaluatingAndAggregating.pdf
    19 Jun 2024: µC(f, gSHAP) 1.94 0.26 1.36 0.36 2.33 0.23µC(f, gAVA) 1.93 0.24 1.24 0.32 2.61 0.29.
  16. 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
  17. Gauged Mini-Bucket Elimination for Approximate Inference Sungsoo Ahn…

    https://mlg.eng.cam.ac.uk/adrian/Gauge_for_Holder_Inference.pdf
    19 Jun 2024: We call these methods collectively Z-invariant methods. See [23, 24, 25] for discussions ofthe differences and relations between these methods. ... 24] G. David Forney Jr and Pascal O. Vontobel. Par-tition functions of normal factor graphs.
  18. Conditions Beyond Treewidth for Tightness of Higher-order LP…

    https://mlg.eng.cam.ac.uk/adrian/conditions.pdf
    19 Jun 2024: Conditions Beyond Treewidth for Tightness of Higher-order LP Relaxations. Mark Rowland Aldo Pacchiano Adrian WellerUniversity of Cambridge UC Berkeley University of Cambridge. Abstract. Linear programming (LP) relaxations are a pop-ular method to
  19. Unifying Orthogonal Monte Carlo Methods

    https://mlg.eng.cam.ac.uk/adrian/ICML2019-unified.pdf
    19 Jun 2024: E[x̃λỹλx̃λvỹλv x̃. 2λỹ. 2λv. ] (24)We next show the following. Lemma A.7.
  20. Clamping Variables and Approximate Inference Adrian WellerColumbia…

    https://mlg.eng.cam.ac.uk/adrian/NeurIPS14-clamp.pdf
    19 Jun 2024: Journal of Automated Reasoning, 24(1-2):225–275, 2000. N. Ruozzi. The Bethe partition function of log-supermodular graphical models.
  21. One-network Adversarial Fairness

    https://mlg.eng.cam.ac.uk/adrian/AAAI2019_OneNetworkAdversarialFairness.pdf
    19 Jun 2024: log(1/δ). n(24). The term[4nI(x D0). 4nI(x D1). ]is what is estimated. ... From the latter note and (24), the two classifiers of theadversarial formulation proposed in (9) in the main docu-ment can be interpreted w.r.t.

Refine your results

Search history

Recently clicked results

Recently clicked results

Your click history is empty.

Recent searches

Recent searches

Your search history is empty.