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Found 8 papers, 0 papers with code
Ranking & Reweighting Improves Group Distributional Robustness
Recent work has shown that standard training via empirical risk minimization (ERM) can produce models that achieve high accuracy on average but low accuracy on underrepresented groups due to the prevalence of spurious features.
Modeling Dynamic User Interests: A Neural Matrix Factorization Approach
In recent years, there has been significant interest in understanding users' online content consumption patterns.
Targeting for long-term outcomes
We apply our approach in two large-scale proactive churn management experiments at The Boston Globe by targeting optimal discounts to its digital subscribers with the aim of maximizing long-term revenue.
New Subsampling Algorithms for Fast Least Squares Regression
We address the problem of fast estimation of ordinary least squares (OLS) from large amounts of data ($n \gg p$).
Faster Ridge Regression via the Subsampled Randomized Hadamard Transform
We propose a fast algorithm for ridge regression when the number of features is much larger than the number of observations ($p \gg n$).
Multi-View Learning of Word Embeddings via CCA
Recently, there has been substantial interest in using large amounts of unlabeled data to learn word representations which can then be used as features in supervised classifiers for NLP tasks.
