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Found 20 papers, 6 papers with code
Auditing Privacy Mechanisms via Label Inference Attacks
Across a range of experimental settings, we find that differentially private schemes dominate or match the privacy-utility tradeoff of more heuristic approaches.
Measuring Re-identification Risk
In this work, we present a new theoretical framework to measure re-identification risk in such user representations.
Subset-Based Instance Optimality in Private Estimation
We propose a new definition of instance optimality for differentially private estimation algorithms.
Confidence-Ranked Reconstruction of Census Microdata from Published Statistics
Our attacks significantly outperform those that are based only on access to a public distribution or population from which the private dataset $D$ was sampled, demonstrating that they are exploiting information in the aggregate statistics $Q(D)$, and not simply the overall structure of the distribution.
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.
Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learning
We provide the first utility guarantees for differentially private top-down decision tree learning in both the single machine and distributed settings.
Algorithms and Learning for Fair Portfolio Design
We consider a variation on the classical finance problem of optimal portfolio design.
Random Smoothing Might be Unable to Certify $\ell_\infty$ Robustness for High-Dimensional Images
We show a hardness result for random smoothing to achieve certified adversarial robustness against attacks in the $\ell_p$ ball of radius $\epsilon$ when $p>2$.
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.
How much data is sufficient to learn high-performing algorithms? Generalization guarantees for data-driven algorithm design
We provide a broadly applicable theory for deriving generalization guarantees that bound the difference between the algorithm's average performance over the training set and its expected performance.
Learning piecewise Lipschitz functions in changing environments
We consider the class of piecewise Lipschitz functions, which is the most general online setting considered in the literature for the problem, and arises naturally in various combinatorial algorithm selection problems where utility functions can have sharp discontinuities.
Learning to Link
Clustering is an important part of many modern data analysis pipelines, including network analysis and data retrieval.
Semi-bandit Optimization in the Dispersed Setting
We apply our semi-bandit results to obtain the first provable guarantees for data-driven algorithm design for linkage-based clustering and we improve the best regret bounds for designing greedy knapsack algorithms.
Envy-Free Classification
In classic fair division problems such as cake cutting and rent division, envy-freeness requires that each individual (weakly) prefer his allocation to anyone else's.
Data-Driven Clustering via Parameterized Lloyd's Families
Algorithms for clustering points in metric spaces is a long-studied area of research.
Learning to Branch
Tree search algorithms recursively partition the search space to find an optimal solution.
Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization
We present general techniques for online and private optimization of the sum of dispersed piecewise Lipschitz functions.
Differentially Private Clustering in High-Dimensional Euclidean Spaces
We study the problem of clustering sensitive data while preserving the privacy of individuals represented in the dataset, which has broad applications in practical machine learning and data analysis tasks.
Data Driven Resource Allocation for Distributed Learning
In distributed machine learning, data is dispatched to multiple machines for processing.
Label Efficient Learning by Exploiting Multi-class Output Codes
We present a new perspective on the popular multi-class algorithmic techniques of one-vs-all and error correcting output codes.
