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Found 17 papers, 1 papers with code
Mean estimation in the add-remove model of differential privacy
We propose a new algorithm and show that it is min-max optimal, achieving the best possible constant in the leading term of the mean squared error for all $\epsilon$, and that this constant is the same as the optimal algorithm under the swap model.
Subset-Based Instance Optimality in Private Estimation
We propose a new definition of instance optimality for differentially private estimation algorithms.
Combining Public and Private Data
We argue that our mechanism is preferable to techniques that preserve the privacy of individuals by subsampling data proportionally to the privacy needs of users.
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.
Maximizing Induced Cardinality Under a Determinantal Point Process
In this paper we advocate an alternative framework for applying DPPs to recommender systems.
Completing State Representations using Spectral Learning
A central problem in dynamical system modeling is state discovery—that is, finding a compact summary of the past that captures the information needed to predict the future.
Diversifying Sparsity Using Variational Determinantal Point Processes
We propose a novel diverse feature selection method based on determinantal point processes (DPPs).
Expectation-Maximization for Learning Determinantal Point Processes
However, log-likelihood is non-convex in the entries of the kernel matrix, and this learning problem is conjectured to be NP-hard.
A Repository of State of the Art and Competitive Baseline Summaries for Generic News Summarization
In the period since 2004, many novel sophisticated approaches for generic multi-document summarization have been developed.
Near-Optimal MAP Inference for Determinantal Point Processes
Determinantal point processes (DPPs) have recently been proposed as computationally efficient probabilistic models of diverse sets for a variety of applications, including document summarization, image search, and pose estimation.
Determinantal point processes for machine learning
Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory.
Structured Determinantal Point Processes
We present a novel probabilistic model for distributions over sets of structures -- for example, sets of sequences, trees, or graphs.
Adaptive Regularization of Weight Vectors
We present AROW, a new online learning algorithm that combines several properties of successful : large margin training, confidence weighting, and the capacity to handle non-separable data.
Learning Bounds for Domain Adaptation
Empirical risk minimization offers well-known learning guarantees when training and test data come from the same domain.
