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Found 17 papers, 3 papers with code
Efficiency in Collective Decision-Making via Quadratic Transfers
Consider the following collective choice problem: a group of budget constrained agents must choose one of several alternatives.
Generalization in the Face of Adaptivity: A Bayesian Perspective
Repeated use of a data sample via adaptively chosen queries can rapidly lead to overfitting, wherein the empirical evaluation of queries on the sample significantly deviates from their mean with respect to the underlying data distribution.
Gaming Helps! Learning from Strategic Interactions in Natural Dynamics
We consider an online regression setting in which individuals adapt to the regression model: arriving individuals are aware of the current model, and invest strategically in modifying their own features so as to improve the predicted score that the current model assigns to them.
Learn to Expect the Unexpected: Probably Approximately Correct Domain Generalization
Domain generalization is the problem of machine learning when the training data and the test data come from different data domains.
Privately Learning Thresholds: Closing the Exponential Gap
This problem has received much attention recently; unlike the non-private case, where the sample complexity is independent of the domain size and just depends on the desired accuracy and confidence, for private learning the sample complexity must depend on the domain size $X$ (even for approximate differential privacy).
A New Analysis of Differential Privacy's Generalization Guarantees
This second claim follows from a thought experiment in which we imagine that the dataset is resampled from the posterior distribution after the mechanism has committed to its answers.
A necessary and sufficient stability notion for adaptive generalization
We introduce a new notion of the stability of computations, which holds under post-processing and adaptive composition.
Learning to Prune: Speeding up Repeated Computations
We present an algorithm that learns to maximally prune the search space on repeated computations, thereby reducing runtime while provably outputting the correct solution each period with high probability.
Equal Opportunity in Online Classification with Partial Feedback
We study an online classification problem with partial feedback in which individuals arrive one at a time from a fixed but unknown distribution, and must be classified as positive or negative.
Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM
Traditional approaches to differential privacy assume a fixed privacy requirement ε for a computation, and attempt to maximize the accuracy of the computation subject to the privacy constraint.
Penalizing Unfairness in Binary Classification
We present a new approach for mitigating unfairness in learned classifiers.
Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM
Traditional approaches to differential privacy assume a fixed privacy requirement $\epsilon$ for a computation, and attempt to maximize the accuracy of the computation subject to the privacy constraint.
Adaptive Learning with Robust Generalization Guarantees
We also show that perfect generalization is a strictly stronger guarantee than differential privacy, but that, nevertheless, many learning tasks can be carried out subject to the guarantees of perfect generalization.
Truthful Linear Regression
We consider the problem of fitting a linear model to data held by individuals who are concerned about their privacy.
A Simple and Practical Algorithm for Differentially Private Data Release
We present a new algorithm for differentially private data release, based on a simple combination of the Exponential Mechanism with the Multiplicative Weights update rule.
Differentially Private Combinatorial Optimization
Is it even possible to design good algorithms for this problem that preserve the privacy of the clients?
Data Structures and Algorithms Cryptography and Security Computer Science and Game Theory
