Develops multi-kernel regression for graph signal processing.
problem Smoothness of graph signals over a graph.
method Estimates linear weights to learn effective kernel function using graph smoothness.
result Optimization problem is convex and accelerated projected gradient descent solution proposed.
The paper develops a multi-kernel method with sparsity constraint for regression.
problem Developing a robust regression method with sparsity constraints.
method Banach-space formulation, generalized total-variation regularization, multi-kernel expansion, adaptive kernel positions, ℓ 1 \ell_1 ℓ 1 penalty on coefficients. result The method achieves sparsity in the kernel coefficients, reducing the number of active kernels to the number of data points.
A new multi-kernel RBFNN design improves performance and speed.
problem Improving the performance and speed of RBFNNs.
method Proposes a novel multi-kernel RBFNN where each base kernel has its own weight.
result Better performance gains including faster convergence, better local minima, and resilience against poor local minima.
Paper improves generalization bounds for multi-kernel learning with mixed datasets.
problem Improving generalization for multi-kernel learning with mixed Markov chain datasets.
method Developed novel generalization bounds with O ( log m ) O(\sqrt{\log m}) O ( log m ) and O ( 1 / n ) O(1/\sqrt{n}) O ( 1/ n ) dependencies. result Added terms compensate for dependency among samples in mixed datasets.
Study uses multi-kernel Hawkes models to analyze high-frequency price dynamics.
problem Understanding responsive speeds of market participants in high-frequency trading.
method Multi-kernel Hawkes models with conditional Hessian analysis for optimization.
result Existence of multi-kernels (UHF, VHF, HF) in high-frequency price dynamics.
Paper introduces a new multi-kernel algorithm for better gradient approximation.
problem Improving gradient approximation in high-dimensional problems.
method Develops a multi-kernel passive stochastic gradient algorithm with variance reduction.
result The multi-kernel algorithm performs better in high-dimensional problems.
Novel adaptive multi-kernel learning scheme for dynamic environments.
problem Learning nonlinear functions in environments with unknown dynamics.
method Random feature approximation and adaptive multi-kernel learning.
result Unique capability to track nonlinear functions in dynamic environments with performance guarantees.
New method preserves privacy while improving machine learning accuracy.
problem Privacy-preserving machine learning for daily data.
method Compressive Privacy and multi-kernel method.
result Improved utility classification accuracy with privacy preservation.
Localized Multiple Kernel Learning improves anomaly detection performance.
problem Anomaly detection in one-class classification tasks.
method Localized Multiple Kernel Learning (LMKAD) for One-class Classification (OCC).
result LMKAD achieves significantly better Gmean scores with fewer support vectors.
Improved deep learning for action recognition using multi-kernel SVM and deep neural networks.
problem Challenges in video understanding, especially action recognition, despite deep neural networks' success in image understanding.
method Combining multi-kernel SVM with a multi-stream deep convolutional neural network, including hand-crafted features.
result Achieved close to state-of-the-art performance on the HMDB-51 dataset.
SimpleMKKM improves multi-kernel clustering efficiency.
problem Efficient multi-kernel clustering.
method Re-formulated minimization-maximization problem into a smooth minimization, solved with gradient descent.
result Outperforms state-of-the-art multi-kernel clustering alternatives.
MKCapsnet improves schizophrenia identification using multi-kernels and dropout.
problem Identifying schizophrenia using existing methods requires two steps and large amounts of data.
method Developed a multi-kernel capsule network (MKCapsnet) inspired by brain anatomy.
result Outperformed state-of-the-art methods in schizophrenia identification.
A new kernel function centers at different points improves robust learning.
problem Robust learning in the presence of outliers.
method Introduces multi-kernel correntropy (MKC) with kernels centered at different points.
result Learning algorithms using MMKCC outperform those using MCC and MMCC.
Enhanced LSTM with multiple kernels and attention improves video action recognition.
problem Improving motion understanding in video analysis.
method Proposed a Network-in-LSTM approach with multiple convolutional kernels and layers, and an attention-based mechanism.
result Improves accuracy in supervised classification on UCF-101 and Sports-1M datasets.
MKPN predicts varying-sized kernels for burst image denoising.
problem Denoising burst images corrupted by noise.
method Deep neural network (MKPN) predicts and fuses kernels of varying sizes.
result MKPN outperforms state-of-the-art on synthetic datasets.
Paper develops a graph-based method for reconstructing spatio-temporal signals.
problem Reconstructing space-time varying signals on graphs given limited data.
method Multi-kernel Kriged Kalman Filter combining graph-aware kernels and online selection.
result Superior reconstruction performance compared to existing methods.
Signal processing tasks as fundamental as sampling, reconstruction, minimum mean-square error interpolation and prediction can be viewed under the prism of reproducing kernel Hilbert spaces. Endowing this vantage point with contemporary advances in sparsity-aware modeling and processing, promotes the nonparametric basi…
Improved neural network accuracy with multi-Kernel activation functions.
problem Designing effective activation functions for neural networks.
method Developed multi-Kernel activation functions (multi-KAF) combining multiple kernel models.
result Multi-KAFs enhance convolutional networks' accuracy on handwritten Latin OCR tasks.
Paper proposes Langevin dynamics for adaptive IRL of stochastic gradient algorithms.
problem Estimating reward functions from noisy gradient estimates of stochastic gradient agents.
method Generalized Langevin dynamics algorithm for IRL.
result Proposed algorithms asymptotically generate samples proportional to exp(R(θ)).
A number of applications in engineering, social sciences, physics, and biology involve inference over networks. In this context, graph signals are widely encountered as descriptors of vertex attributes or features in graph-structured data. Estimating such signals in all vertices given noisy observations of their values…
The paper develops a neural network for learning dynamics from data.
problem Trade-off between representational capacity and overfitting in EDMD.
method Linear recurrent autoencoder network for Koopman operator approximation.
result Improved model reduction and nonlinear reconstruction techniques.
New framework reduces LLM complexity by directly finetuning in Boolean domain.
problem Reducing the complexity of large language models (LLMs) while maintaining performance.
method Proposes a novel framework using multi-kernel Boolean parameters for direct finetuning in the Boolean domain.
result Significantly reduces complexity during both finetuning and inference, outperforming recent techniques.
Combining distributed Gaussian Processes with deep CNNs improves performance on action recognition.
problem Improving performance on action recognition datasets.
method Combining distributed Gaussian Processes with multi-stream deep CNNs, treating each CNN as an expert and combining predictions using a Product of Experts (PoE) framework.
result Improves performance on HMDB-51 dataset by 0.4\% compared to hand-crafted feature frameworks.
The smart grid vision entails advanced information technology and data analytics to enhance the efficiency, sustainability, and economics of the power grid infrastructure. Aligned to this end, modern statistical learning tools are leveraged here for electricity market inference. Day-ahead price forecasting is cast as a…
ARM improves multivariate time series forecasting by better capturing series-wise relationships.
problem Challenges in handling complex temporal-contextual relationships in multivariate time series forecasting.
method ARM is an enhanced multivariate LTSF architecture that employs Adaptive Univariate Effect Learning, Random Dropping, and Multi-kernel Local Smoothing.
result ARM outperforms vanilla Transformers on multiple benchmarks without significantly increasing computational costs.
Kernel k-Means algorithm improves clustering of non-linear data.
problem Non-convexity of kernel k-Means objective function leads to local minima.
method Generalizes MM approach to solve non-convex problem in kernel and multi-kernel settings.
result Establishes strong consistency guarantees for Kernel Power k-Means.
The medical research facilitates to acquire a diverse type of data from the same individual for particular cancer. Recent studies show that utilizing such diverse data results in more accurate predictions. The major challenge faced is how to utilize such diverse data sets in an effective way. In this paper, we introduc…
A deep neural network for spatial time series forecasting.
problem Challenges in forecasting spatial time series with specific patterns and curse of dimensionality.
method Spatial-temporal decomposition, fuzzy clustering, multi-kernel convolution, convolution-LSTM, denoising autoencoder.
result Model outperforms baseline and state-of-the-art models in traffic flow prediction.
Study identifies five AD subtypes using graph diffusion and similarity learning.
problem Identifying homogeneous AD subtypes to improve diagnosis and treatment.
method Unsupervised clustering with graph diffusion and similarity learning.
result Five distinct AD subtypes identified with significant differences in biomarkers and clinical features.
Experiment evaluates hospital case cost prediction models using Azure ML.
problem Accurate hospital case cost modelling for efficient financial management.
method Azure Machine Learning Studio tool for comparing 14 regression models.
result Robust regression, boosted decision tree, and decision forest models outperformed others.
Efficiently private regression for unbounded data.
problem Privacy constraints in regression settings with unbounded covariates.
method Differential privacy techniques on mean and covariance estimation extended to sub-gaussian regime.
result Unbiased estimate of true regression vector learned up to a scaling factor.
This paper studies robust regression in the settings of Huber's ε ε ε -contamination models. We consider estimators that are maximizers of multivariate regression depth functions. These estimators are shown to achieve minimax rates in the settings of ε ε ε -contamination models for various regression problems including nonpa…
The study explores nonparametric regression with shape constraints using least squares estimation.
problem Nonparametric regression under shape constraints.
method Least squares estimation (LSE) with focus on isotonic, unimodal, convex, and additive shape-restricted regression.
result Adaptive nature of the LSE and its risk behavior, with pointwise limiting distribution theory for isotonic regression.
This paper studies the nonparametric modal regression problem systematically from a statistical learning view. Originally motivated by pursuing a theoretical understanding of the maximum correntropy criterion based regression (MCCR), our study reveals that MCCR with a tending-to-zero scale parameter is essentially moda…
Proposes FARM model combining latent factor and sparse regression.
problem Testing adequacy of latent factor and sparse regression models.
method Factor Augmented sparse linear Regression Model (FARM) with FabTest and ANOVA type tests.
result Model robustness and effectiveness validated through experiments.
Neural regression trees convert regression to classification more effectively.
problem Suboptimal approaches for regression via classification.
method Joint optimization framework for learning optimal discretization thresholds and feature selection in a neural regression tree.
result Empirically validated as state-of-the-art on challenging regression tasks.
Survey of SDR methods for high-dimensional regression and embedding.
problem Reducing dimensionality in high-dimensional data.
method Involves both statistical and machine learning approaches, covering inverse and forward regression methods.
result Supervised Kernel Dimension Reduction is equivalent to supervised PCA.
This paper reviews SDR methods for multivariate response regression.
problem Handling sufficient dimension reduction for multivariate response regression.
method Characterizes SDR estimators as inverse or forward regression methods.
result Pooled marginal, projective resampling, distance-based, ordinary least squares, partial least squares, and semiparametric SDR estimators are discussed.
Paper introduces semi-supervised linear extremile regression for high-dimensional data.
problem Challenges in high-dimensional extremile regression due to data sparsity and overfitting.
method Proposes semi-supervised learning for linear extremile regression, achieving n \sqrt{n} n -consistency. result Demonstrates improved estimation efficiency and performance in high-dimensional settings.
Improves logistic regression performance with nonconvex programming.
problem Stochastic generalized linear regression with chance constraints.
method Nonconvex programming techniques, clustering, quantile estimation.
result Over 1 to 2 percent improvement in model performance.
Study improves H H H -consistency bounds for regression analysis.
problem Improving H H H -consistency bounds for regression analysis. method Generalized theorems and novel H H H -consistency bounds for various surrogate loss functions. result Derives principled surrogate losses for adversarial regression.
Prevalidated ridge regression simplifies logistic regression for high-dimensional data.
problem Efficient probabilistic classification in high-dimensional data with logistic regression.
method Developed a prevalidated ridge regression model that matches logistic regression's performance but is more computationally efficient.
result Prevalidated ridge regression achieves similar classification error and log-loss to logistic regression for high-dimensional data.
Introduces a new model for mapping matrices to matrices, subsuming linear regression.
problem Learning matrix-to-matrix mappings from data.
method Partial trace regression model, leveraging quantum information theory.
result Relevance demonstrated in matrix-to-matrix regression and positive semidefinite matrix completion.
Meta-theorems validate fair regression algorithms under demographic parity constraints.
problem Regression under demographic parity constraints.
method Meta-theorems and post-processing methods.
result Fair minimax optimal regression can be achieved through post-processing.
We analyzed optimism in linear and kernel regression models.
problem Understanding predictive complexity in regression models.
method Derived closed-form asymptotic optimism for linear and kernel regression models.
result Scaled optimism is a useful measure for model complexity.
A new optimizer, MVO, improves nonlinear regression performance.
problem Finding optimal coefficients in nonlinear regression models.
method Multi-Verse Optimizer (MVO) compared to Particle Swarm Optimizer (PSO).
result MVO statistically outperforms PSO in 10 nonlinear regression problems.
Brenier isotonic regression extends multi-output isotonic regression using optimal transport.
problem Enforcing cyclic monotonicity in multi-output regression.
method Leverage Kantorovich's optimal transport to find cyclically monotone couplings.
result Brenier isotonic regression outperforms baselines in probability calibration.
We simplify complex regression coefficients using linearization and feature comparison.
problem Interpreting high-dimensional regression coefficients from nonlinear responses.
method Developed a linearization method to derive feature coefficients and compare them with regression coefficients.
result Shows how regression coefficients relate to linearized feature coefficients and how they change under regularization.