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Found 21 papers, 4 papers with code
Scaling Fair Learning to Hundreds of Intersectional Groups
In real-world applications, however, there are multiple protected attributes yielding a large number of intersectional protected groups.
Cost-Sensitive Hierarchical Classification through Layer-wise Abstentions
For each layer, we also achieve higher accuracy when the overall accuracy is kept fixed across different methods.
Dynamic Social Media Monitoring for Fast-Evolving Online Discussions
However, collecting social media data using a static set of keywords fails to satisfy the growing need to monitor dynamic conversations and to study fast-changing topics.
Disentangling Observed Causal Effects from Latent Confounders using Method of Moments
Thus, we provide a principled approach to tackling the joint problem of causal discovery and latent variable inference.
Robust Fairness under Covariate Shift
We investigate fairness under covariate shift, a relaxation of the iid assumption in which the inputs or covariates change while the conditional label distribution remains the same.
Distributionally Robust Learning for Uncertainty Calibration under Domain Shift
We detect such domain shifts through the use of a binary domain classifier and integrate it with the task network and train them jointly end-to-end.
Distributionally Robust Learning for Unsupervised Domain Adaptation
This formulation motivates the use of two neural networks that are jointly trained --- a discriminative network between the source and target domains for density-ratio estimation, in addition to the standard classification network.
Active Learning under Label Shift
We address the problem of active learning under label shift: when the class proportions of source and target domains differ.
Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems
The Info-SNOC algorithm is used to compute a sub-optimal pool of safe motion plans that aid in exploration for learning unknown residual dynamics under safety constraints.
Triply Robust Off-Policy Evaluation
Ours is a general approach that can be used to augment any existing OPE method that utilizes the direct method.
Finding Social Media Trolls: Dynamic Keyword Selection Methods for Rapidly-Evolving Online Debates
Online harassment is a significant social problem.
Robust Regression for Safe Exploration in Control
To address this challenge, we present a deep robust regression model that is trained to directly predict the uncertainty bounds for safe exploration.
Modeling and Interpreting Real-world Human Risk Decision Making with Inverse Reinforcement Learning
Our results demonstrate that IRL is an effective tool to model human decision-making behavior, as well as to help interpret the human psychological process in risk decision-making.
Regularized Learning for Domain Adaptation under Label Shifts
We derive a generalization bound for the classifier on the target domain which is independent of the (ambient) data dimensions, and instead only depends on the complexity of the function class.
Consistent Robust Adversarial Prediction for General Multiclass Classification
We propose a robust adversarial prediction framework for general multiclass classification.
Learning Gibbs-regularized GANs with variational discriminator reparameterization
We propose a novel approach to regularizing generative adversarial networks (GANs) leveraging learned {\em structured Gibbs distributions}.
Kernel Robust Bias-Aware Prediction under Covariate Shift
Robust Bias-Aware (RBA) prediction provides the conditional label distribution that is robust to the worstcase logarithmic loss for the target distribution while matching feature expectation constraints from the source distribution.
Robust Covariate Shift Prediction with General Losses and Feature Views
Covariate shift relaxes the widely-employed independent and identically distributed (IID) assumption by allowing different training and testing input distributions.
Adversarial Multiclass Classification: A Risk Minimization Perspective
Recently proposed adversarial classification methods have shown promising results for cost sensitive and multivariate losses.
Etude acoustique et repr\'esentation phonologique sur /ə˞/ suffixe rhotique en mandarin (Acoustic study and phonological representation of the rhotic suffix /ə˞/ in mandarin)
Historiquement, le suffixe /ə˞/ est un suffixe diminutif correspondant au mot 儿 ({\textless}er{\textgreater} en pinyin) qui signifie {''}petitesse{''}.
Robust Classification Under Sample Selection Bias
In many important machine learning applications, the source distribution used to estimate a probabilistic classifier differs from the target distribution on which the classifier will be used to make predictions.
