arXiv research

Efficiently selects data points for deep learning using smaller proxy models.

Publish a core-set of data to protect against adversarial use.

This paper proposes a new method to select labeled data points using VAEs for active learning.

Convolutional neural networks (CNNs) have been successfully applied to many recognition and learning tasks using a universal recipe; training a deep model on a very large dataset of supervised examples. However, this approach is rather restrictive in practice since collecting a large set of labeled images is very expen…

We study a spectral generalization of classical combinatorial graph spanners to the spectral setting. Given a set of vectors $V\subseteq \Re^d$, we say a set $U\subseteq V$ is an $α$-spectral spanner if for all $v\in V$ there is a probability distribution $μ_v$ supported on $U$ such that $$vv^\intercal \preceq α\cdot\m…

Subset selection in multiple linear regression aims to choose a subset of candidate explanatory variables that tradeoff fitting error (explanatory power) and model complexity (number of variables selected). We build mathematical programming models for regression subset selection based on mean square and absolute errors…

Optimal CL requires perfect memory and is NP-hard.

New algorithm approximates maximum of certain distributions on subsets.

A new algorithm finds optimal centers for sets in metric spaces.

Small-GAN speeds up GAN training by using coresets.

Human labeling of data can be very time-consuming and expensive, yet, in many cases it is critical for the success of the learning process. In order to minimize human labeling efforts, we propose a novel active learning solution that does not rely on existing sources of unlabeled data. It uses a small amount of labeled…

We design and mathematically analyze sampling-based algorithms for regularized loss minimization problems that are implementable in popular computational models for large data, in which the access to the data is restricted in some way. Our main result is that if the regularizer's effect does not become negligible as th…

Unified notation simplifies information-theoretic concepts in machine learning.

This paper reviews techniques for distributed learning with non-convex models.

Tick is a statistical learning library for Python~3, with a particular emphasis on time-dependent models, such as point processes, and tools for generalized linear models and survival analysis. The core of the library is an optimization module providing model computational classes, solvers and proximal operators for re…

A typical way in which network data is recorded is to measure all the interactions among a specified set of core nodes; this produces a graph containing this core together with a potentially larger set of fringe nodes that have links to the core. Interactions between pairs of nodes in the fringe, however, are not recor…

Functional connectivity refers to the temporal statistical relationship between spatially distinct brain regions and is usually inferred from the time series coherence/correlation in brain activity between regions of interest. In human functional brain networks, the network structure is often inferred from functional m…

We study the min-max optimization problem where each function contributing to the max operation is strongly-convex and smooth with bounded gradient in the search domain. By smoothing the max operator, we show the ability to achieve an arbitrarily small positive optimality gap of $δ$ in $\tilde{O}(1/\sqrtδ)$ computation…

Unified framework connects physical laws and machine learning.

The $\ell_p$ linear regression problem is to minimize $f(x)=||Ax-b||_p$ over $x\in\mathbb{R}^d$, where $A\in\mathbb{R}^{n\times d}$, $b\in \mathbb{R}^n$, and $p>0$. To avoid overfitting and bound $||x||_2$, the constrained $\ell_p$ regression minimizes $f(x)$ over every unit vector $x\in\mathbb{R}^d$. This makes the pr…

We present a streaming model for large-scale classification (in the context of $\ell_2$-SVM) by leveraging connections between learning and computational geometry. The streaming model imposes the constraint that only a single pass over the data is allowed. The $\ell_2$-SVM is known to have an equivalent formulation in …

We suggest a new optimization technique for minimizing the sum $\sum_{i=1}^n f_i(x)$ of $n$ non-convex real functions that satisfy a property that we call piecewise log-Lipschitz. This is by forging links between techniques in computational geometry, combinatorics and convex optimization. As an example application, we …

This paper introduces accurate coresets for basic problems.

This work introduces a geometric approach to probability representation and option pricing.

New method estimates selection event for black-box models.

Cluster stability selection improves feature selection in correlated data.

New method corrects selection bias in post-selective inference for Group LASSO.

Proposes PEMI for online selective conformal prediction with asymmetric rules.

The paper addresses errors in online selective conformal prediction and proposes new strategies to ensure valid inference.

This work develops scalable model selection methods with fast update and selection.

Online method selects candidates from data streams, ensuring irreversible decisions.

Paper improves $k$-NN predictive performance with efficient variable selection.

Optimizes biomarker selection for cost-effective treatment rules.

Value selection reduces model size while maintaining accuracy.

Proposes HSIC-Lasso for selective inference in non-linear data.

Paper proposes a statistical test for feature selection pipelines using selective inference.

New Bitcoin coin selection method improves cost savings.

More powerful feature selection tests using selective inference.

New method improves feature selection by integrating stability paths.

UniFeat is an open-source Java tool for feature selection.

We develop a general approach to valid inference after model selection. At the core of our framework is a result that characterizes the distribution of a post-selection estimator conditioned on the selection event. We specialize the approach to model selection by the lasso to form valid confidence intervals for the sel…

Proposes a new method using Copula Entropy for variable selection.

Flexible selective inference using flow-based transport maps.

Recently, many regularized procedures have been proposed for variable selection in linear regression, but their performance depends on the tuning parameter selection. Here a criterion for the tuning parameter selection is proposed, which combines the strength of both stability selection and cross-validation and therefo…

Develops a more powerful selective inference method for stepwise feature selection.

Online selection of dynamic features has attracted intensive interest in recent years. However, existing online feature selection methods evaluate features individually and ignore the underlying structure of feature stream. For instance, in image analysis, features are generated in groups which represent color, texture…

Paper tackles selective regression using uncertainty estimation.

A new method selects clean samples to train DNNs with noisy labels.