Bayesian coresets improve scalable Bayesian inference.
problem Efficiently approximating posterior inference with a subset of data.
method Sparsity constrained optimization and accelerated optimization methods.
result Explicit convergence rate guarantees and superior performance compared to state-of-the-art.
Novel bounds for logistic regression coreset construction and feature selection.
problem Efficiently summarize and reduce logistic regression inputs.
method Feature space sketching for logistic regression.
result Tight bounds for coreset construction and feature selection.
This paper develops a coreset method for GNNs that speeds up training on large graphs.
problem Training Graph Neural Networks (GNNs) on large-scale graphs is computationally expensive.
method The paper proposes a spectral greedy coreset (SGGC) method that selects ego-graphs based on spectral embeddings.
result SGGC significantly speeds up GNN training on large graphs and outperforms other coreset methods.
New method for multi-modal depth prediction challenges.
problem Challenges in applying unimodal coreset selection to multi-modal data.
method Adapted state-of-the-art coreset selection technique for multimodal data.
result Challenges in extending unimodal algorithms to multi-modal scenarios.
FAST selects coresets more efficiently by matching distributions in the frequency domain.
problem Efficiently selecting representative subsets of large datasets for deep learning.
method FAST uses spectral graph theory and CFD to match distributions, addressing limitations of existing methods.
result FAST significantly outperforms state-of-the-art coreset selection methods in accuracy and energy efficiency.
CO2 algorithm creates coresets for generic smooth divergences efficiently.
problem Efficiently creating coresets for generic smooth divergences.
method CO2 algorithm using functional Taylor expansion and maximum mean discrepancy minimization.
result Poly-logarithmically many data points suffice for Sinkhorn divergence approximation.
New framework for variational coresets simplifies Bayesian inference for complex models.
problem Efficient Bayesian inference for complex models like neural networks.
method Black-box variational inference for coresets that handle intractable posterior distributions.
result Principled application of variational coresets to Bayesian neural networks.
New statistical framework for coresets in density estimation.
problem Improving computational efficiency in density estimation.
method Developed a statistical framework for coresets in nonparametric density estimation.
result Practical coreset kernel density estimators are near-minimax optimal.
Dependent MMD coresets help compare multiple related datasets.
problem Comparing multiple related datasets for insights into model generalization.
method Dependent MMD coresets for collections of datasets.
result Dependent MMD coresets facilitate comparison and understanding of multiple related datasets.
Efficiently factorize tensors in streaming data with coreset selection.
problem Efficiently factorize tensors in streaming data.
method Online filtering and kernelization techniques to select a coreset of vectors.
result CP decomposition of coreset approximates full data tensor decomposition.
A new framework for coreset selection in machine learning models.
problem Learning models under resource constraints.
method Formulates coreset selection as a bilevel optimization problem.
result Framework applies to any twice differentiable model, including neural networks.
DRCS selects a subset of data to minimize worst-case test error under covariate shift.
problem Selecting a subset of data that performs well across different deployment scenarios when data distributions differ.
method DRCS derives an upper bound for the worst-case test error assuming covariate shift and selects instances to minimize this bound.
result DRCS achieves distributionally robust training instance selection.
Optimizes model training efficiency with core subset selection.
problem Escalating computational costs in large dataset training.
method Core subset selection for reweighting.
result Efficiently minimizes computational time and improves model performance.
This paper shows DPPs can outperform random coresets in machine learning tasks.
problem Building efficient coresets for machine learning models.
method Using determinantal point processes (DPPs) to construct coresets with provable improvements over random sampling.
result DPPs can provably outperform independently drawn coresets in terms of approximation of total loss.
COPS optimizes deep learning by selecting informative samples with uncertainty estimation.
problem Mitigating high costs in labeling and computational resources for deep learning.
method COPS (unCertainty based OPtimal Sub-sampling) selects data with input and output uncertainty for linear softmax regression.
result COPS outperforms baseline methods in deep learning tasks, minimizing expected loss.
Bayesian coresets improved with random sampling and quasi-Newton optimization.
problem Efficiently approximate Bayesian posterior distributions for computationally expensive inference.
method Randomly select a subset of data points, then optimize weights using quasi-Newton method.
result First algorithm with high-probability KL divergence bound on coreset quality.
In2Core selects a coreset for efficient LLM fine-tuning with reduced data.
problem Costly fine-tuning of large language models due to extensive parameters and data requirements.
method Analyzes model gradients to estimate training sample influence, optimizing for efficiency.
result Achieves similar performance with 50% of training data using In2Core.
Recent work by Brock et al. (2018) suggests that Generative Adversarial Networks (GANs) benefit disproportionately from large mini-batch sizes. Unfortunately, using large batches is slow and expensive on conventional hardware. Thus, it would be nice if we could generate batches that were effectively large though actual…
In a number of situations, collecting a function value for every data point may be prohibitively expensive, and random sampling ignores any structure in the underlying data. We introduce a scalable optimization algorithm with no correction steps (in contrast to Frank-Wolfe and its variants), a variant of gradient ascen…
Develops a robust training framework to detect backdoor attacks in DNNs.
problem Vulnerability of DNNs to backdoor attacks by poisoned training data.
method Collider framework selects prominent samples based on geometric structures and coreset selection objective.
result Significantly reduces backdoor success rate in various poisoned datasets.
Proposes coreset method for robust training of neural networks with noisy labels.
problem Overfitting of neural networks trained with noisy labels.
method Selects weighted subsets (coresets) of clean data points to approximate low-rank Jacobian matrix.
result Gradient descent applied to coreset subsets does not overfit noisy labels.
New method reduces computational cost for Bayesian inference.
problem High computational cost in Bayesian inference.
method Coreset MCMC, a Markov chain targeting coreset posterior.
result Coreset MCMC provides higher quality posterior approximations and reduced computational cost.
We analyze coresets for regularized regression problems and propose a modified lasso that yields smaller coresets.
problem Analyzing coresets for regularized regression problems.
method Examined coresets for ridge regression and proposed a modified lasso problem.
result No coreset for regularized regression can be smaller than the unregularized version when reqs. Unified framework for accurate coresets in latent variable models and regularized regression.
problem Efficiently training models on large datasets.
method Unified framework for constructing accurate coresets for latent variable models and ℓp-regularized regression. result Unified framework reduces coreset size for latent variable models and ℓp-regularized regression. New method creates coresets for deep neural networks efficiently.
problem Efficiently handling large data streams with limited resources.
method Cardinality-constrained bilevel optimization for deep neural networks.
result Demonstrated efficient generation of coresets for deep neural networks.
Paper introduces robust, dynamic coreset for outlier-resilient learning problems.
problem Dealing with outliers in continuous-and-bounded learning problems.
method Proposes a novel robust coreset method for fully-dynamic environments.
result First robust and fully-dynamic coreset construction method for optimization problems.
New coreset method for near-convex functions.
problem Efficiently approximating loss functions for machine learning.
method Generic framework for computing sensitivities of near-convex functions using f-SVD factorization.
result Significantly improved coresets for various machine learning models.
Coreset, which is a summary of the original dataset in the form of a small weighted set in the same sample space, provides a promising approach to enable machine learning over distributed data. Although viewed as a proxy of the original dataset, each coreset is only designed to approximate the cost function of a specif…
The paper develops coresets for panel data regression problems.
problem Efficiently summarize panel data regression problems.
method Introduced coreset construction for panel data regression problems using the Feldman-Langberg framework.
result Constructs coresets of size polynomial in 1/ε and number of parameters, independent of panel data size. An ε-coreset for a given set D of n points, is usually a small weighted set, such that querying the coreset \emph{provably} yields a (1+ε)-factor approximation to the original (full) dataset, for a given family of queries. Using existing techniques, coresets can be maintained for streaming, …
Coherent uncertainty quantification is a key strength of Bayesian methods. But modern algorithms for approximate Bayesian posterior inference often sacrifice accurate posterior uncertainty estimation in the pursuit of scalability. This work shows that previous Bayesian coreset construction algorithms---which build a sm…
We develop coreset techniques for noisy clustering with provable guarantees.
problem Clustering with stochastic noise in datasets.
method Surrogate error metrics and coreset construction algorithm.
result Improved coreset size and better guarantees on true clustering cost.
Efficiently trains SVM models on large datasets using coreset technology.
problem Training large-scale SVM models efficiently on Big Data.
method Developed an algorithm to create a coreset, a small representative subset of data.
result Proved the size of coreset required for SVM models and showed its applicability to streaming data.
This work improves bounds on Bayesian coreset quality.
problem Limitations of existing theoretical analysis of Bayesian coresets.
method Develops general upper and lower bounds on KL divergence.
result Demonstrates flexibility of new theoretical bounds in various models.
How can we train a statistical mixture model on a massive data set? In this work we show how to construct coresets for mixtures of Gaussians. A coreset is a weighted subset of the data, which guarantees that models fitting the coreset also provide a good fit for the original data set. We show that, perhaps surprisingly…
This paper defines the notion of class discrepancy for families of functions. It shows that low discrepancy classes admit small offline and streaming coresets. We provide general techniques for bounding the class discrepancy of machine learning problems. As corollaries of the general technique we bound the discrepancy …
We investigate coresets - succinct, small summaries of large data sets - so that solutions found on the summary are provably competitive with solution found on the full data set. We provide an overview over the state-of-the-art in coreset construction for machine learning. In Section 2, we present both the intuition be…
Algorithm constructs coresets for clustering time series data from Gaussian mixtures.
problem Clustering time series data from Gaussian mixtures with autocorrelations.
method Developed an efficient algorithm to construct coresets for the maximum likelihood objective.
result Size of the coreset is independent of N and k, polynomial in d, k, and 1/ε. Coresets are compact representations of data sets such that models trained on a coreset are provably competitive with models trained on the full data set. As such, they have been successfully used to scale up clustering models to massive data sets. While existing approaches generally only allow for multiplicative appro…
The use of Bayesian methods in large-scale data settings is attractive because of the rich hierarchical models, uncertainty quantification, and prior specification they provide. Standard Bayesian inference algorithms are computationally expensive, however, making their direct application to large datasets difficult or …
The proliferation of large data sets and Bayesian inference techniques motivates demand for better data sparsification. Coresets provide a principled way of summarizing a large dataset via a smaller one that is guaranteed to match the performance of the full data set on specific problems. Classical coresets, however, n…
A coreset (or core-set) of an input set is its small summation, such that solving a problem on the coreset as its input, provably yields the same result as solving the same problem on the original (full) set, for a given family of problems (models, classifiers, loss functions). Over the past decade, coreset constructio…
New method finds coresets for faster machine learning.
problem Improving training speed and understanding results.
method Iterative optimization using multi-objective evolutionary algorithms.
result Results deliver lower error and better generalization.
Incremental gradient (IG) methods, such as stochastic gradient descent and its variants are commonly used for large scale optimization in machine learning. Despite the sustained effort to make IG methods more data-efficient, it remains an open question how to select a training data subset that can theoretically and pra…
The proliferation of automated inference algorithms in Bayesian statistics has provided practitioners newfound access to fast, reproducible data analysis and powerful statistical models. Designing automated methods that are also both computationally scalable and theoretically sound, however, remains a significant chall…
The automation of posterior inference in Bayesian data analysis has enabled experts and nonexperts alike to use more sophisticated models, engage in faster exploratory modeling and analysis, and ensure experimental reproducibility. However, standard automated posterior inference algorithms are not tractable at the scal…
We develop a coreset for robust geometric median, reducing size dependency on outliers.
problem Robust geometric median problem in Euclidean space with outliers.
method Construction of a compact coreset with size dependency on m eliminated. result Elimination of O(m) dependency in coreset size, achieving O(ε−2⋅min{ε−2,d}) size. Coresets are one of the central methods to facilitate the analysis of large data sets. We continue a recent line of research applying the theory of coresets to logistic regression. First, we show a negative result, namely, that no strongly sublinear sized coresets exist for logistic regression. To deal with intractable…