A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New insights into privacy guarantees for subsampled mechanisms under composition.
Unified framework for subsampling mechanisms with tighter privacy guarantees.
We present a matrix factorization algorithm that scales to input matrices that are large in both dimensions (i.e., that contains morethan 1TB of data). The algorithm streams the matrix columns while subsampling them, resulting in low complexity per iteration andreasonable memory footprint. In contrast to previous onlin…
In this paper, we study random subsampling of Gaussian process regression, one of the simplest approximation baselines, from a theoretical perspective. Although subsampling discards a large part of training data, we show provable guarantees on the accuracy of the predictive mean/variance and its generalization ability.…
New method audits DP guarantees without noise or subsampling info.
Differential privacy comes equipped with multiple analytical tools for the design of private data analyses. One important tool is the so-called "privacy amplification by subsampling" principle, which ensures that a differentially private mechanism run on a random subsample of a population provides higher privacy guaran…
Structured subsampling improves privacy in deep time series forecasting.
For optimization on large-scale data, exactly calculating its solution may be computationally difficulty because of the large size of the data. In this paper we consider subsampled optimization for fast approximating the exact solution. In this approach, one gets a surrogate dataset by sampling from the full data, and …
Enhanced privacy, utility, and efficiency through MUST subsampling.
The paper provides theoretical guarantees for optimized sampling in compressed sensing, showing error vanishes with more measurements.
Combining differential privacy and federated learning improves data security.
We propose a sampling scheme suitable for reducing a data set prior to selecting a hypothesis with minimum empirical risk. The sampling only considers a subset of the ultimate (unknown) hypothesis set, but can nonetheless guarantee that the final excess risk will compare favorably with utilizing the entire original dat…
Optimizes survey design for private mean estimation with reduced variance.
This paper studies a Nyström type subsampling approach to large kernel learning methods in the misspecified case, where the target function is not assumed to belong to the reproducing kernel Hilbert space generated by the underlying kernel. This case is less understood, in spite of its practical importance. To model su…
The paper analyzes the statistical cost of tuning kernel hyperparameters in robust regression.
SNG-DBSCAN clusters data faster with subsampled similarity queries.
New coherence parameter for GNNs with Fourier measurements improves signal recovery.
We present a matrix-factorization algorithm that scales to input matrices with both huge number of rows and columns. Learned factors may be sparse or dense and/or non-negative, which makes our algorithm suitable for dictionary learning, sparse component analysis, and non-negative matrix factorization. Our algorithm str…
New sampling scheme improves privacy in DP-SGD without sacrificing utility.
The study provides theoretical foundations for using smaller instances to predict algorithm performance on larger ones.
A new method speeds up ALS for recommender systems by subsampling key elements.
A new method selects a representative subsample for efficient kernel density estimation.
Unified theory and debiasing framework for random oblique projections in high dimensions.
In this paper we demonstrate that tempering Markov chain Monte Carlo samplers for Bayesian models by recursively subsampling observations without replacement can improve the performance of baseline samplers in terms of effective sample size per computation. We present two tempering by subsampling algorithms, subsampled…
This paper optimizes subsampling for large datasets using Poisson distribution.
Large sample size brings the computation bottleneck for modern data analysis. Subsampling is one of efficient strategies to handle this problem. In previous studies, researchers make more fo- cus on subsampling with replacement (SSR) than on subsampling without replacement (SSWR). In this paper we investigate a kind of…
Recent developments in differentially private (DP) machine learning and DP Bayesian learning have enabled learning under strong privacy guarantees for the training data subjects. In this paper, we further extend the applicability of DP Bayesian learning by presenting the first general DP Markov chain Monte Carlo (MCMC)…
A significant hurdle for analyzing large sample data is the lack of effective statistical computing and inference methods. An emerging powerful approach for analyzing large sample data is subsampling, by which one takes a random subsample from the original full sample and uses it as a surrogate for subsequent computati…
We study the problem of subsampling in differential privacy (DP), a question that is the centerpiece behind many successful differentially private machine learning algorithms. Specifically, we provide a tight upper bound on the Rényi Differential Privacy (RDP) (Mironov, 2017) parameters for algorithms that: (1) subsamp…
A new model-free subsampling method using uniform designs is proposed.
A new method reduces variance in SGMCMC by preferentially subsampling data.
For massive data, the family of subsampling algorithms is popular to downsize the data volume and reduce computational burden. Existing studies focus on approximating the ordinary least squares estimate in linear regression, where statistical leverage scores are often used to define subsampling probabilities. In this p…
A new method for streaming PCA provides confidence intervals for eigenvector entries.
The Sampled Gaussian Mechanism's noise level decreases with larger subsampling rates, improving privacy-utility trade-offs.
Group-equivariant subsampling layers improve CNNs' equivariance.
Given a loss function that can be written as the sum of losses over a large set of inputs , it is often desirable to approximate by subsampling the input points. Strong theoretical guarantees require taking into account the importance of each point, measured by how …
Develops a method to optimize hyperparameters for subsampling methods.
New equivalences found between subsampling and ridge regularization methods.
Markov chain Monte Carlo methods are often deemed too computationally intensive to be of any practical use for big data applications, and in particular for inference on datasets containing a large number of individual data points, also known as tall datasets. In scenarios where data are assumed independent, various…
A deep learning subsampling technique improves modulation classification accuracy.
We describe an adaptation of the simulated annealing algorithm to nonparametric clustering and related probabilistic models. This new algorithm learns nonparametric latent structure over a growing and constantly churning subsample of training data, where the portion of data subsampled can be interpreted as the inverse …
Hamiltonian Monte Carlo (HMC) samples efficiently from high-dimensional posterior distributions with proposed parameter draws obtained by iterating on a discretized version of the Hamiltonian dynamics. The iterations make HMC computationally costly, especially in problems with large datasets, since it is necessary to c…
Speeding up Markov Chain Monte Carlo (MCMC) for datasets with many observations by data subsampling has recently received considerable attention. A pseudo-marginal MCMC method is proposed that estimates the likelihood by data subsampling using a block-Poisson estimator. The estimator is a product of Poisson estimators,…
The paper analyzes the risk of bagging regularized M-estimators under proportional asymptotics.
A new method subsamples data without weights to improve model performance.
New research finds many coreset methods for logistic regression are not better than simple sampling.
New analysis shows SNG's effectiveness in small samples.
A new Markov subsampling strategy based on Huber criterion improves data processing from noisy full data.