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Found 12 papers, 4 papers with code
FRAPPÉ: A Group Fairness Framework for Post-Processing Everything
Despite achieving promising fairness-error trade-offs, in-processing mitigation techniques for group fairness cannot be employed in numerous practical applications with limited computation resources or no access to the training pipeline of the prediction model.
Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders
In this work, we incorporate explicit and implicit negative user feedback into the training objective of sequential recommenders in the retrieval stage using a "not-to-recommend" loss function that optimizes for the log-likelihood of not recommending items with negative feedback.
Causally motivated Shortcut Removal Using Auxiliary Labels
Shortcut learning, in which models make use of easy-to-represent but unstable associations, is a major failure mode for robust machine learning.
Measuring Recommender System Effects with Simulated Users
Using this simulation framework, we can (a) isolate the effect of the recommender system from the user preferences, and (b) examine how the system performs not just on average for an "average user" but also the extreme experiences under atypical user behavior.
Fair treatment allocations in social networks
Simulations of infectious disease spread have long been used to understand how epidemics evolve and how to effectively treat them.
Benefits of Overparameterization in Single-Layer Latent Variable Generative Models
One of the most surprising and exciting discoveries in supervising learning was the benefit of overparameterization (i. e. training a very large model) to improving the optimization landscape of a problem, with minimal effect on statistical performance (i. e. generalization).
Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models
One of the most surprising and exciting discoveries in supervised learning was the benefit of overparameterization (i. e. training a very large model) to improving the optimization landscape of a problem, with minimal effect on statistical performance (i. e. generalization).
BriarPatches: Pixel-Space Interventions for Inducing Demographic Parity
We introduce the BriarPatch, a pixel-space intervention that obscures sensitive attributes from representations encoded in pre-trained classifiers.
No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World
Further, we analyze classifiers trained on these data sets to assess the impact of these training distributions and find strong differences in the relative performance on images from different locales.
Clinical Tagging with Joint Probabilistic Models
We describe a method for parameter estimation in bipartite probabilistic graphical models for joint prediction of clinical conditions from the electronic medical record.
Anchored Discrete Factor Analysis
We present a semi-supervised learning algorithm for learning discrete factor analysis models with arbitrary structure on the latent variables.
A Practical Algorithm for Topic Modeling with Provable Guarantees
Topic models provide a useful method for dimensionality reduction and exploratory data analysis in large text corpora.
