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Found 7 papers, 0 papers with code
Machine Learning's Dropout Training is Distributionally Robust Optimal
This paper shows that dropout training in Generalized Linear Models is the minimax solution of a two-player, zero-sum game where an adversarial nature corrupts a statistician's covariates using a multiplicative nonparametric errors-in-variables model.
A Distributionally Robust Boosting Algorithm
Distributionally Robust Optimization (DRO) has been shown to provide a flexible framework for decision making under uncertainty and statistical estimation.
Bidirectional Attentional Encoder-Decoder Model and Bidirectional Beam Search for Abstractive Summarization
The model has a bidirectional encoder-decoder architecture; in which the encoder and the decoder are bidirectional LSTMs.
Latent Semantic Analysis Approach for Document Summarization Based on Word Embeddings
The new schemes combine the strength of the traditional weighting schemes and word embedding.
Data-driven Optimal Cost Selection for Distributionally Robust Optimization
Recently, (Blanchet, Kang, and Murhy 2016, and Blanchet, and Kang 2017) showed that several machine learning algorithms, such as square-root Lasso, Support Vector Machines, and regularized logistic regression, among many others, can be represented exactly as distributionally robust optimization (DRO) problems.
Doubly Robust Data-Driven Distributionally Robust Optimization
Data-driven Distributionally Robust Optimization (DD-DRO) via optimal transport has been shown to encompass a wide range of popular machine learning algorithms.
Semi-supervised Learning based on Distributionally Robust Optimization
We propose a novel method for semi-supervised learning (SSL) based on data-driven distributionally robust optimization (DRO) using optimal transport metrics.
