Papers Sparse Learning
“Sparse Learning” 태그가 달린 논문 209편 · 필터 해제
Optimal Stochastic and Online Learning with Individual Iterates
Stochastic composite mirror descent (SCMD) is a simple and efficient method able to capture both geometric and composite structures of optimization problems in machine learning. Existing strategies require to take either…
Sparse LearningRigging the Lottery: Making All Tickets Winners
Many applications require sparse neural networks due to space or inference time restrictions. There is a large body of work on training dense networks to yield sparse networks for inference, but this limits the size of t…
AllImage ClassificationLanguage ModellingSparse LearningA Discriminative Gaussian Mixture Model with Sparsity
In probabilistic classification, a discriminative model based on the softmax function has a potential limitation in that it assumes unimodality for each class in the feature space. The mixture model can address this issu…
modelSparse LearningCollaborative Preference Embedding against Sparse Labels
Living in the era of the internet, we are now facing a big bang of online information. As a consequence, we often find ourselves troubling with hundreds and thousands of options before making a decision. As a way to impr…
Collaborative FilteringDecision MakingMetric LearningRecommendation Systems+1Learning Cluster Structured Sparsity by Reweighting
Recently, the paradigm of unfolding iterative algorithms into finite-length feed-forward neural networks has achieved a great success in the area of sparse recovery. Benefit from available training data, the learned netw…
Sparse LearningA Knowledge Transfer Framework for Differentially Private Sparse Learning
We study the problem of estimating high dimensional models with underlying sparse structures while preserving the privacy of each training example. We develop a differentially private high-dimensional sparse learning fra…
regressionSparse LearningTransfer LearningA Bayesian Lasso based Sparse Learning Model
The Bayesian Lasso is constructed in the linear regression framework and applies the Gibbs sampling to estimate the regression parameters. This paper develops a new sparse learning model, named the Bayesian Lasso Sparse …
Compressive SensingmodelregressionSparse LearningThe Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost
Privacy has raised considerable concerns recently, especially with the advent of information explosion and numerous data mining techniques to explore the information inside large volumes of data. In this context, a new d…
Anomaly DetectionFederated LearningSparse LearningSparse Networks from Scratch: Faster Training without Losing Performance
We demonstrate the possibility of what we call sparse learning: accelerated training of deep neural networks that maintain sparse weights throughout training while achieving dense performance levels. We accomplish this b…
Image ClassificationSparse LearningA unified approach to mixed-integer optimization problems with logical constraints
We propose a unified framework to address a family of classical mixed-integer optimization problems with logically constrained decision variables, including network design, facility location, unit commitment, sparse port…
Sparse LearningRobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering
Extracting the underlying trend signal is a crucial step to facilitate time series analysis like forecasting and anomaly detection. Besides noise signal, time series can contain not only outliers but also abrupt trend ch…
Anomaly DetectionSparse LearningTime SeriesTime Series AnalysisOptimal approximation for unconstrained non-submodular minimization
Submodular function minimization is well studied, and existing algorithms solve it exactly or up to arbitrary accuracy. However, in many applications, such as structured sparse learning or batch Bayesian optimization, th…
Bayesian OptimizationSparse LearningforgeNet: A graph deep neural network model using tree-based ensemble classifiers for feature extraction
A unique challenge in predictive model building for omics data has been the small number of samples $(n)$ versus the large amount of features $(p)$. This "$n\ll p$" property brings difficulties for disease outcome classi…
Classificationfeature selectionGeneral ClassificationRobust classification+1Concise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learning
The superior interpretability and uncertainty modeling ability of Takagi-Sugeno-Kang fuzzy system (TSK FS) make it possible to describe complex nonlinear systems intuitively and efficiently. However, classical TSK FS usu…
ClusteringSparse LearningSparse Learning for Variable Selection with Structures and Nonlinearities
In this thesis we discuss machine learning methods performing automated variable selection for learning sparse predictive models. There are multiple reasons for promoting sparsity in the predictive models. By relying on …
BIG-bench Machine LearningSparse LearningVariable SelectionThe State of Sparsity in Deep Neural Networks
We rigorously evaluate three state-of-the-art techniques for inducing sparsity in deep neural networks on two large-scale learning tasks: Transformer trained on WMT 2014 English-to-German, and ResNet-50 trained on ImageN…
Model CompressionSparse LearningStructure learning via unstructured kernel-based M-regression
In statistical learning, identifying underlying structures of true target functions based on observed data plays a crucial role to facilitate subsequent modeling and analysis. Unlike most of those existing methods that f…
quantile regressionregressionSparse LearningvalidProximal SCOPE for Distributed Sparse Learning
Distributed sparse learning with a cluster of multiple machines has attracted much attention in machine learning, especially for large-scale applications with high-dimensional data. One popular way to implement sparse le…
Sparse LearningDantzig Selector with an Approximately Optimal Denoising Matrix and its Application to Reinforcement Learning
Dantzig Selector (DS) is widely used in compressed sensing and sparse learning for feature selection and sparse signal recovery. Since the DS formulation is essentially a linear programming optimization, many existing li…
compressed sensingDenoisingfeature selectionReinforcement Learning+2Cross-Modal Ranking with Soft Consistency and Noisy Labels for Robust RGB-T Tracking
Due to the complementary benefits of visible (RGB) and thermal infrared (T) data, RGB-T object tracking attracts more and more attention recently for boosting the performance under adverse illumination conditions. Existi…
ObjectObject TrackingRgb-T TrackingSparse Learning