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Fairness through awareness
TLDR
A framework for fair classification comprising a (hypothetical) task-specific metric for determining the degree to which individuals are similar with respect to the classification task at hand and an algorithm for maximizing utility subject to the fairness constraint, that similar individuals are treated similarly is presented.
Learning Fair Representations
We propose a learning algorithm for fair classification that achieves both group fairness (the proportion of members in a protected group receiving positive classification is identical to the
Differential privacy under continual observation
TLDR
This work identifies the problem of maintaining a counter in a privacy preserving manner and shows its wide applicability to many different problems.
Learning Adversarially Fair and Transferable Representations
TLDR
This paper presents the first in-depth experimental demonstration of fair transfer learning and demonstrates empirically that the authors' learned representations admit fair predictions on new tasks while maintaining utility, an essential goal of fair representation learning.
Preserving Statistical Validity in Adaptive Data Analysis
TLDR
It is shown that, surprisingly, there is a way to estimate an exponential in n number of expectations accurately even if the functions are chosen adaptively, and this gives an exponential improvement over standard empirical estimators that are limited to a linear number of estimates.
Combining Component Caching and Clause Learning for Effective Model Counting
TLDR
A model-counting program that combines component caching with clause learning, one of the most important ideas used in modern SAT solvers, and provides significant evidence that it can outperform existing algorithms for #SAT by orders of magnitude.
Generalization in Adaptive Data Analysis and Holdout Reuse
TLDR
A simple and practical method for reusing a holdout set to validate the accuracy of hypotheses produced by a learning algorithm operating on a training set and it is shown that a simple approach based on description length can also be used to give guarantees of statistical validity in adaptive settings.
The reusable holdout: Preserving validity in adaptive data analysis
TLDR
A new approach for addressing the challenges of adaptivity based on insights from privacy-preserving data analysis is demonstrated, and how to safely reuse a holdout data set many times to validate the results of adaptively chosen analyses is shown.
Pan-Private Streaming Algorithms
TLDR
A study of pan-private algorithms, where each datum may be discarded immediately after processing, where these algorithms retain their privacy properties even if their internal state becomes visible to an adversary.
Solving #SAT and Bayesian Inference with Backtracking Search
TLDR
It is shown that standard backtracking search when augmented with a simple memoization scheme (caching) can solve any sum-of-products problem with time complexity that is at least as good any other state- of-the-art exact algorithm, and that it can also achieve the best known time-space tradeoff.
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