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- 4F13: Machine Learning
https://mlg.eng.cam.ac.uk/teaching/4f13/1011/lect07.pdf19 Nov 2023: But we can not usually simulate Hamiltonian dynamics exactly. Ghahramani & Rasmussen (CUED) Lecture 7 and 8: Markov Chain Monte Carlo 24 / 28. -
- Machine Learning 4F13, Michaelmas 2015
https://mlg.eng.cam.ac.uk/teaching/4f13/1516/lect0304.pdf19 Nov 2023: 1 0 1 22. 1. 0. 1. 2M=0. 1 0 1 24. ... 1. 2M=0. 1 0 1 24. 2. 0. 2. 4. M=1. -
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. -
- Machine Learning 4F13, Spring 2014
https://mlg.eng.cam.ac.uk/teaching/4f13/1314/lect0304.pdf19 Nov 2023: 1. 0. 1. 2M=0. 1 0 1 24. 2. 0. 2. ... 1. 2M=0. 1 0 1 24. 2. 0. 2. 4. M=1. -
- Machine Learning 4F13, Spring 2015
https://mlg.eng.cam.ac.uk/teaching/4f13/1415/lect0304.pdf19 Nov 2023: 1. 0. 1. 2M=0. 1 0 1 24. 2. 0. 2. ... 1. 2M=0. 1 0 1 24. 2. 0. 2. 4. M=1. -
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. -
https://mlg.eng.cam.ac.uk/blog/feed.xml
https://mlg.eng.cam.ac.uk/blog/feed.xml12 Apr 2024: Jekyll 2024-04-12T16:32:5900:00 https://mlg.eng.cam.ac.uk/blog/feed.xml MLG Blog Blog of the Machine Learning Group at the University of Cambridge An introduction to Flow Matching 2024-01-20T00:00:0000:00 2024-01-20T00:00:0000:00 -
One-network Adversarial Fairness
https://mlg.eng.cam.ac.uk/adrian/AAAI2019_OneNetworkAdversarialFairness.pdf19 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. -
C:/Users/Adrian/Documents/GitHub/betheClean/docs/nb-UAI.dvi
https://mlg.eng.cam.ac.uk/adrian/nb-UAI.pdf19 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 -
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.pdf19 Jun 2024: mostimportant feature) in 2D reduced input space (scikit-learn [24]’s PCA imple-mentation). ... 4765–4774, (2017). [24] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. -
The Geometry of Random Features Krzysztof Choromanski∗1 Mark…
https://mlg.eng.cam.ac.uk/adrian/geometry.pdf19 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 -
Leader Stochastic Gradient Descent for DistributedTraining of Deep…
https://mlg.eng.cam.ac.uk/adrian/NeurIPS2019_LSGD_preprint.pdf19 Jun 2024: Landscape symmetries are commonin a plethora of non-convex problems [18, 19, 20, 21, 22], including deep learning [23, 24, 25, 26]. ... Understanding symmetries in deep networks. CoRR,abs/1511.01029, 2015. [24] A. Choromanska, M. -
Structured Evolution with Compact Architectures for Scalable Policy…
https://mlg.eng.cam.ac.uk/adrian/structured_icml_full.pdf19 Jun 2024: Structured Evolution with Compact Architecturesfor Scalable Policy Optimization. Krzysztof Choromanski 1 Mark Rowland 2 Vikas Sindhwani 1 Richard E. Turner 2 Adrian Weller 2 3. AbstractWe present a new method of blackbox optimiza-tion via gradient -
Tightness of LP Relaxations for Almost Balanced Models Adrian ...
https://mlg.eng.cam.ac.uk/adrian/tricam.pdf19 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 -
Clamping Variables and Approximate Inference Adrian WellerColumbia…
https://mlg.eng.cam.ac.uk/adrian/NeurIPS14-clamp.pdf19 Jun 2024: Journal of Automated Reasoning, 24(1-2):225–275, 2000. N. Ruozzi. The Bethe partition function of log-supermodular graphical models. -
From Parity to Preference-based Notionsof Fairness in Classification…
https://mlg.eng.cam.ac.uk/adrian/NeurIPS17-from-parity-to-preference.pdf19 Jun 2024: decisionoutcomes. A number of learning mechanisms have been proposed to achieve parity in treatment [24],. ... Angwin. https://github.com/propublica/compas-analysis, 2016. [24] B. T. Luong, S. Ruggieri, and F. -
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring…
https://mlg.eng.cam.ac.uk/adrian/KDD2018_inequality_indices.pdf19 Jun 2024: 2.3 Axioms for Measuring InequalityBorrowing insights from the rich body of work on the axiomaticcharacterization of inequality indices in economics and social sci-ence [3, 10, 19, 24, 25, 28, -
Gaussian Process
https://mlg.eng.cam.ac.uk/teaching/4f13/1718/gaussian%20process.pdf19 Nov 2023: 64. 20. 24. 6. 6. 4. 2. 0. 2. 4. 6. -
Uprooting and Rerooting Higher-Order GraphicalModels Mark…
https://mlg.eng.cam.ac.uk/adrian/uprooting-higher-order.pdf19 Jun 2024: 4], which relates to generalized belief propagation,24) and MAP inference (using loopy belief propagation, LBP [9]). ... InArtificial Intelligence and Statistics (AISTATS), 2016. [24] J. Yedidia, W. Freeman, and Y. -
Unifying Orthogonal Monte Carlo Methods
https://mlg.eng.cam.ac.uk/adrian/ICML2019-unified.pdf19 Jun 2024: E[x̃λỹλx̃λvỹλv x̃. 2λỹ. 2λv. ] (24)We next show the following. Lemma A.7.
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