Statistical Learning
d5c04aa72b92c53bda5b525b60958295-Supplemental-Conference.pdf
Westudy linear regression under covariate shift, where themarginal distribution over the input covariates differs in the source and the target domains, while the conditional distribution of the output given the input covariates is similar across thetwodomains.
Leveraging the Exact Likelihood of Deep Latent Variable Models
Pierre-Alexandre Mattei, Jes Frellsen
Neural Information Processing Systems http://nips.cc/
Generalization Bounds for Uniformly Stable Algorithms
Neural Information Processing Systems http://nips.cc/
Comparing Apples to Oranges: Learning Similarity Functions for Data Produced by Different Distributions
AI/ML problems, and in each such case σ is assumed to be known .
SparseHigh-DimensionalIsotonicRegression
We consider the problem of estimating an unknown coordinate-wise monotone function given noisy measurements, known as the isotonic regression problem. Often, only a small subset of the features affects the output.
d4c2f25bf0c33065b7d4fb9be2a9add1-Paper-Conference.pdf
We call this distinction thediscrimination risk. We prove that a higher discrimination risk can amplify the unfairness of a machine learning model applied totheimputed data.
Objective and efficient inference for couplings in neuronal networks
Yu Terada, Tomoyuki Obuchi, Takuya Isomura, Yoshiyuki Kabashima
Neural Information Processing Systems http://nips.cc/