
Importance sampling approach to chanceconstrained DC optimal power flow
Despite significant economic and ecological effects, a higher level of r...
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Learning over noPreferred and Preferred Sequence of items for Robust Recommendation
In this paper, we propose a theoretically founded sequential strategy fo...
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Tractable Minorfree Generalization of Planar Zerofield Ising Models
We present a new family of zerofield Ising models over N binary variabl...
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A New Family of Tractable Ising Models
We present a new family of zerofield Ising models over N binary variabl...
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Sequential Learning over Implicit Feedback for Robust LargeScale Recommender Systems
In this paper, we propose a robust sequential learning strategy for trai...
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Learning a Generator Model from Terminal Bus Data
In this work we investigate approaches to reconstruct generator models f...
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Inference and Sampling of K_33free Ising Models
We call an Ising model tractable when it is possible to compute its part...
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Gauges, Loops, and Polynomials for Partition Functions of Graphical Models
We suggest a new methodology for analysis and approximate computations o...
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Belief Propagation MinSum Algorithm for Generalized MinCost Network Flow
Belief Propagation algorithms are instruments used broadly to solve grap...
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Importance sampling the union of rare events with an application to power systems analysis
This paper presents a method for estimating the probability μ of a union...
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Representation Learning and Pairwise Ranking for Implicit Feedback in Recommendation Systems
In this paper, we propose a novel ranking framework for collaborative fi...
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Aggressive Sampling for Multiclass to Binary Reduction with Applications to Text Classification
We address the problem of multiclass classification in the case where t...
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Rademacher Complexity Bounds for a Penalized Multiclass SemiSupervised Algorithm
We propose Rademacher complexity bounds for multiclass classifiers train...
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Yury Maximov
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