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Found 4 papers, 1 papers with code
Tightening Discretization-based MILP Models for the Pooling Problem using Upper Bounds on Bilinear Terms
Discretization-based methods have been proposed for solving nonconvex optimization problems with bilinear terms such as the pooling problem.
On the identifiability of mixtures of ranking models
Mixtures of ranking models are standard tools for ranking problems.
PBoS: Probabilistic Bag-of-Subwords for Generalizing Word Embedding
We look into the task of \emph{generalizing} word embeddings: given a set of pre-trained word vectors over a finite vocabulary, the goal is to predict embedding vectors for out-of-vocabulary words, \emph{without} extra contextual information.
Provable Training Set Debugging for Linear Regression
We first formulate a general statistical algorithm for identifying buggy points and provide rigorous theoretical guarantees under the assumption that the data follow a linear model.
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