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Understanding the Bethe Approximation: When and How can it go Wrong?
https://mlg.eng.cam.ac.uk/adrian/pabc.pdf19 Jun 2024: 100. Maximum coupling strength y. BethelocalBethecycleBethemargTRWlocalTRWcycleTRWmarg. log partition error. 2 8 16 24 320. ... 2 8 16 24 320. 0.1. 0.2. 0.3. 0.4. Maximum coupling strength y. -
Blind Justice: Fairness with Encrypted Sensitive Attributes
https://mlg.eng.cam.ac.uk/adrian/poster_ICML18_BlindJustice.pdf19 Jun 2024: Bank. Datasets and Feasibility. Adult Bank COMPAS German SQFn training examples 214 215 212 29 216d features 51 62 7 24 23p sensitive attributes 1 1 7 1 1certification 802 ms -
Clamping Variables and Approximate Inference
https://mlg.eng.cam.ac.uk/adrian/newsclamp.pdf19 Jun 2024: 0.1. 0.2. 0.3. 0.4. max interaction strength W 24 / 19. -
Revisiting the Limits of MAP Inference by MWSS on Perfect Graphs
https://mlg.eng.cam.ac.uk/adrian/slides-revisit.pdf19 Jun 2024: Revisiting the Limits of MAP Inference by MWSSon Perfect Graphs. Adrian WellerUniversity of Cambridge. CP 2015Cork, Ireland. Slides and full paper athttp://mlg.eng.cam.ac.uk/adrian/. 1 / 21. Motivation: undirected graphical models (MRFs). • -
Clamping Variables and Approximate Inference
https://mlg.eng.cam.ac.uk/adrian/slides_msr2.pdf19 Jun 2024: 24 / 21. Error in log Z vs number of clamps: complete graphs. ... 24 / 21. Time (secs) vs error in log Z for various methods. -
Directed and Undirected Graphical Models
https://mlg.eng.cam.ac.uk/adrian/2018-MLSALT4-AW1-models.pdf19 Jun 2024: 23 / 26. Directed and undirected models are different. 24 / 26. ... Directed and undirected models are different. With <3 edges,. 24 / 26. -
Junction Tree, BP and Variational Methods
https://mlg.eng.cam.ac.uk/adrian/2018-MLSALT4-AW3-approx.pdf19 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). -
The Case for Process Fairness in Learning:Feature Selection for ...
https://mlg.eng.cam.ac.uk/adrian/grgic.pdf19 Jun 2024: Age 46% 56% 32%Gender 24% 50% 22%Race 24% 35% 15%. Table 3: Comparing judgment of fairness of each feature, of 54 male users, when they have differentknowledge about the ... Age 42% 63% 33%Gender 24% 54% 23%Race 18% 43% 18%. Table 5: Comparing judgment -
Bounding the Integrality Distance ofLP Relaxations for Structured…
https://mlg.eng.cam.ac.uk/adrian/OPT2016_paper_3.pdf19 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. -
Structured Prediction Models for Chord Transcription of Music Audio…
https://mlg.eng.cam.ac.uk/adrian/icmla09adrian.pdf19 Jun 2024: To its right, 0-1 adds the features of the previous frame, so in this model each frame is represented by 24 dimensions. -
ML-IRL: Machine Learning in Real Life Workshop at ICLR ...
https://mlg.eng.cam.ac.uk/adrian/ML_IRL_2020-CLUE.pdf19 Jun 2024: 6). LSATAl. (7). COMPASEp. (6). COMPASAl. (5) Total (24). Prolific Unc. -
Transparency: Motivations and Challenges? Adrian…
https://mlg.eng.cam.ac.uk/adrian/transparency.pdf19 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. -
Understanding the Bethe Approximation: When and How can it ...
https://mlg.eng.cam.ac.uk/adrian/abc.pdf19 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. -
Now You See Me (CME): Concept-based Model Extraction
https://mlg.eng.cam.ac.uk/adrian/AIMLAI20-CME.pdf19 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 -
Ode to an ODE Krzysztof Choromanski ∗Robotics at Google ...
https://mlg.eng.cam.ac.uk/adrian/NeurIPS20-ODEtoODE.pdf19 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. -
Evaluating and Aggregating Feature-based Model Explanations
https://mlg.eng.cam.ac.uk/adrian/IJCAI20_EvaluatingAndAggregating.pdf19 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. -
An Introduction to LP Relaxations for MAP Inference
https://mlg.eng.cam.ac.uk/adrian/2018-MLSALT4-AW2-LP.pdf19 Jun 2024: x3. x4. x5. x6. s. balanced almost balanced(attractive up to flipping) 24 / 41. -
Gauged Mini-Bucket Elimination for Approximate Inference Sungsoo Ahn…
https://mlg.eng.cam.ac.uk/adrian/Gauge_for_Holder_Inference.pdf19 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. -
Human Perceptions of Fairness in Algorithmic Decision Making: A Case…
https://mlg.eng.cam.ac.uk/adrian/WWW18-HumanPerceptions.pdf19 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. -
Blind Justice: Fairness with Encrypted Sensitive Attributes
https://mlg.eng.cam.ac.uk/adrian/ICML18-BlindJustice.pdf19 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.
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