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.
Paper improves generalization bounds for multi-kernel learning with mixed datasets.
New method preserves privacy while improving machine learning accuracy.
Paper introduces a new multi-kernel algorithm for better gradient approximation.
We develop a multi-kernel based regression method for graph signal processing where the target signal is assumed to be smooth over a graph. In multi-kernel regression, an effective kernel function is expressed as a linear combination of many basis kernel functions. We estimate the linear weights to learn the effective …
A new multi-kernel RBFNN design improves performance and speed.
Kernel-based methods exhibit well-documented performance in various nonlinear learning tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task-specific prior information. Especially when the latter is not available, multi-kernel learning has gained popularity thanks to its flexibility in ch…
Study uses multi-kernel Hawkes models to analyze high-frequency price dynamics.
As a novel similarity measure that is defined as the expectation of a kernel function between two random variables, correntropy has been successfully applied in robust machine learning and signal processing to combat large outliers. The kernel function in correntropy is usually a zero-mean Gaussian kernel. In a recent …
In this paper, we provide a Banach-space formulation of supervised learning with generalized total-variation (gTV) regularization. We identify the class of kernel functions that are admissible in this framework. Then, we propose a variation of supervised learning in a continuous-domain hybrid search space with gTV regu…
SimpleMKKM improves multi-kernel clustering efficiency.
Inference of space-time varying signals on graphs emerges naturally in a plethora of network science related applications. A frequently encountered challenge pertains to reconstructing such dynamic processes, given their values over a subset of vertices and time instants. The present paper develops a graph-aware kernel…
Multi-kernel learning has been well explored in the recent past and has exhibited promising outcomes for multi-class classification and regression tasks. In this paper, we present a multiple kernel learning approach for the One-class Classification (OCC) task and employ it for anomaly detection. Recently, the basic mul…
Objective: Schizophrenia seriously affects the quality of life. To date, both simple (linear discriminant analysis) and complex (deep neural network) machine learning methods have been utilized to identify schizophrenia based on functional connectivity features. The existing simple methods need two separate steps (i.e.…
Image understanding using deep convolutional network has reached human-level performance, yet a closely related problem of video understanding especially, action recognition has not reached the requisite level of maturity. We combine multi-kernels based support-vector-machines (SVM) with a multi-stream deep convolution…
Action recognition greatly benefits motion understanding in video analysis. Recurrent networks such as long short-term memory (LSTM) networks are a popular choice for motion-aware sequence learning tasks. Recently, a convolutional extension of LSTM was proposed, in which input-to-hidden and hidden-to-hidden transitions…
In low light or short-exposure photography the image is often corrupted by noise. While longer exposure helps reduce the noise, it can produce blurry results due to the object and camera motion. The reconstruction of a noise-less image is an ill posed problem. Recent approaches for image denoising aim to predict kernel…
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…
ARM improves multivariate time series forecasting by better capturing series-wise relationships.
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…
In recent work, it was shown that combining multi-kernel based support vector machines (SVMs) can lead to near state-of-the-art performance on an action recognition dataset (HMDB-51 dataset). This was 0.4\% lower than frameworks that used hand-crafted features in addition to the deep convolutional feature extractors. I…
This paper describes a method for learning low-dimensional approximations of nonlinear dynamical systems, based on neural-network approximations of the underlying Koopman operator. Extended Dynamic Mode Decomposition (EDMD) provides a useful data-driven approximation of the Koopman operator for analyzing dynamical syst…
Paper proposes Langevin dynamics for adaptive IRL of stochastic gradient algorithms.
New framework reduces LLM complexity by directly finetuning in Boolean domain.
Kernel k-Means algorithm improves clustering of non-linear data.
Study identifies five AD subtypes using graph diffusion and similarity learning.
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 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…
Spatial time series forecasting problems arise in a broad range of applications, such as environmental and transportation problems. These problems are challenging because of the existence of specific spatial, short-term and long-term patterns, and the curse of dimensionality. In this paper, we propose a deep neural net…
Meta-learning adapts models for unseen tasks across AI, robotics, and NLP.
Meta-learning improves neural networks by adapting learning algorithms.
Survey explores how transfer learning improves deep reinforcement learning.
Machine learning models adapt to motor learning but face challenges.
New method uses bi-level optimization to learn useful representations for imitation learning.
Tabular Q-Learning with learned state abstractions solves continuous control tasks.
Study Whittle index learning algorithms for restless bandits with constant stepsizes.
Paper discusses flaws in traditional RL for lifelong learning.
AI learns to learn sequentially without forgetting.
Poisson learning doesn't solve graph semi-supervised learning issues.
New unsupervised learning technique learns independent kernels for better machine learning tasks.
Meta-learning helps models learn quickly from few samples.
New self-imitation learning method improves performance in continuous control tasks.
Deep reinforcement learning finds optimal learning policies for adaptive systems.
Unified framework explains all types of learning, including brain.
Cyclical learning rates improve DRL performance without manual tuning.
Study batch reinforcement learning methods for personalized medical treatments.
A new meta-meta classification method tackles few-shot learning tasks.
The paper proposes a learning algorithm that improves adaptability and generalization.
Bayesian meta-learning improves few-shot learning with deep kernels.