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Unifying Orthogonal Monte Carlo Methods
https://mlg.eng.cam.ac.uk/adrian/ICML2019-unified.pdf16 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). ... Then there exists a constantC = C(B,) > 0 such that for every x,y BS() and dlarge enough we have:. -
Bounding the Integrality Distance ofLP Relaxations for Structured…
https://mlg.eng.cam.ac.uk/adrian/OPT2016_paper_3.pdf16 May 2024: Z(m)) Zm, according to Dm, every posterior distribution, Q, over H, satisfies. ... w‖2 B. By construction, every w′ Q has distance at most 2/(mB) from w. -
Bucket Renormalization for Approximate Inference
https://mlg.eng.cam.ac.uk/adrian/ICML18-BucketRenormalization.pdf16 May 2024: Then scope Sf for all f Bigoes through renormalization by replacing every i in thescope by i as follows:. ... Physical Review B, 97(4):045111, 2018. Hinton, Geoffrey E and Salakhutdinov, Ruslan R. -
Adversarial Graph Embeddings for Fair Influence Maximization over…
https://mlg.eng.cam.ac.uk/adrian/IJCAI20_AdversarialGraphEmbeddings.pdf16 May 2024: There are 1779 inter-connections be-tween nodes of the two communities. We assume an activa-tion probability of 0.01 for every link, which is used in theindependent cascade model ... Normal Embedding: Every node of the input networkis first described by -
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring…
https://mlg.eng.cam.ac.uk/adrian/KDD2018_inequality_indices.pdf16 May 2024: For any benefit distribution b, IGβ (b) IGG. ′β (b). If one continues refining the groups, eventually every individ-ual will be in their own group and the between-group unfairnessbecomes ... Each of the above models computes the likelihood of -
Methods for Inference in Graphical Models
https://mlg.eng.cam.ac.uk/adrian/phd_FINAL.pdf16 May 2024: as an indicator function for every possible configuration of each subset c C, resulting in d =cC. ... thesis. A binary model is one in which every variable has just two states, i.e.
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