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Found 12 papers, 1 papers with code
Learning-Augmented Private Algorithms for Multiple Quantile Release
When applying differential privacy to sensitive data, we can often improve performance using external information such as other sensitive data, public data, or human priors.
Easy Differentially Private Linear Regression
In this paper, we study an algorithm which uses the exponential mechanism to select a model with high Tukey depth from a collection of non-private regression models.
Learning with Labeling Induced Abstentions
Consider a setting where we wish to automate an expensive task with a machine learning algorithm using a limited labeling resource.
Learning with User-Level Privacy
We show that for high-dimensional mean estimation, empirical risk minimization with smooth losses, stochastic convex optimization, and learning hypothesis classes with finite metric entropy, the privacy cost decreases as $O(1/\sqrt{m})$ as users provide more samples.
Differentially Private Covariance Estimation
The covariance matrix of a dataset is a fundamental statistic that can be used for calculating optimum regression weights as well as in many other learning and data analysis settings.
Pan-Private Uniformity Testing
We show that the sample complexity of pure pan-private uniformity testing is $\Theta(k^{2/3})$.
Repeated Inverse Reinforcement Learning
We introduce a novel repeated Inverse Reinforcement Learning problem: the agent has to act on behalf of a human in a sequence of tasks and wishes to minimize the number of tasks that it surprises the human by acting suboptimally with respect to how the human would have acted.
Threshold Bandits, With and Without Censored Feedback
The learner selects one of $K$ actions (arms), this action generates a random sample from a fixed distribution, and the action then receives a unit payoff in the event that this sample exceeds the threshold value.
Towards Resolving Unidentifiability in Inverse Reinforcement Learning
We first demonstrate that if the learner can experiment with any transition dynamics on some fixed set of states and actions, then there exists an algorithm that reconstructs the agent's reward function to the fullest extent theoretically possible, and that requires only a small (logarithmic) number of experiments.
Repeated Contextual Auctions with Strategic Buyers
Motivated by real-time advertising exchanges, we analyze the problem of pricing inventory in a repeated posted-price auction.
Online Learning and Profit Maximization from Revealed Preferences
We consider the problem of learning from revealed preferences in an online setting.
Learning Prices for Repeated Auctions with Strategic Buyers
Inspired by real-time ad exchanges for online display advertising, we consider the problem of inferring a buyer's value distribution for a good when the buyer is repeatedly interacting with a seller through a posted-price mechanism.
