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.
Sparse-penalized deep neural networks improve performance in weakly dependent processes.
A new method builds sparse polynomial chaos expansions for models with dependent inputs.
The paper tackles reward-relevance in offline RL with sparse decision dynamics.
In many problem settings, parameter vectors are not merely sparse but dependent in such a way that non-zero coefficients tend to cluster together. We refer to this form of dependency as "region sparsity." Classical sparse regression methods, such as the lasso and automatic relevance determination (ARD), which model par…
New algorithm reduces sample complexity for sparse linear regression.
New model captures insurance risk dependencies efficiently.
CoT improves transformer sample efficiency by reducing input token dependencies and attention sparsity.
The paper develops a deep neural network estimator for weakly dependent processes with various loss functions.
Self-attention prefers sparse functions of input sequences, reducing sample complexity.
New method for disentangling latent factors with sparse dependencies.
Fast robust subspace tracking in sparse data-dependent noise with near-optimal delay.
New method infers graph from dependent matrix data.
Sparse graph learning for dependent time series using ADMM.
We propose a regression algorithm that utilizes a learned dictionary optimized for sparse inference on a D-Wave quantum annealer. In this regression algorithm, we concatenate the independent and dependent variables as a combined vector, and encode the high-order correlations between them into a dictionary optimized for…
New method uses randomized sparse neural networks to solve time-dependent PDEs more accurately and efficiently.
Regular variation provides a convenient theoretical framework to study large events. In the multivariate setting, the dependence structure of the positive extremes is characterized by a measure - the spectral measure - defined on the positive orthant of the unit sphere. This measure gathers information on the localizat…
A major drawback of backpropagation through time (BPTT) is the difficulty of learning long-term dependencies, coming from having to propagate credit information backwards through every single step of the forward computation. This makes BPTT both computationally impractical and biologically implausible. For this reason,…
Sparse neural networks can match dense models on Lipschitz functions.
We discuss a clustering method for Gaussian mixture model based on the sparse principal component analysis (SPCA) method and compare it with the IF-PCA method. We also discuss the dependent case where the covariance matrix is not necessarily diagonal.
Sparse feature selection improves batch RL efficiency.
The paper examines logistic regression in sparse network settings, improving inference under varying degrees of dyadic dependence.
Reduces function approximation dimensions from high to low with sparse data.
Sparse Hopfield model improves memory retrieval with fewer connections.
Optimal sketching bounds for sparse linear regression under various loss functions are established.
Spectral mixture (SM) kernels comprise a powerful class of generalized kernels for Gaussian processes (GPs) to describe complex patterns. This paper introduces model compression and time- and phase (TP) modulated dependency structures to the original (SM) kernel for improved generalization of GPs. Specifically, by adop…
We propose Sparse Neural Network architectures that are based on random or structured bipartite graph topologies. Sparse architectures provide compression of the models learned and speed-ups of computations, they can also surpass their unstructured or fully connected counterparts. As we show, even more compact topologi…
SAEs struggle with curved activation manifolds, revealing layer-dependent scaling laws.
In this paper, we address the challenging problem of selecting tuning parameters for high-dimensional sparse regression. We propose a simple and computationally efficient method, called path thresholding (PaTh), that transforms any tuning parameter-dependent sparse regression algorithm into an asymptotically tuning-fre…
The paper improves machine learning for heavy-tailed panel data.
Sparse deep learning improves prediction uncertainty for time series data.
Recently, the decentralized optimization problem is attracting growing attention. Most existing methods are deterministic with high per-iteration cost and have a convergence rate quadratically depending on the problem condition number. Besides, the dense communication is necessary to ensure the convergence even if the …
Several classification methods assume that the underlying distributions follow tree-structured graphical models. Indeed, trees capture statistical dependencies between pairs of variables, which may be crucial to attain low classification errors. The resulting classifier is linear in the log-transformed univariate and b…
New method finds sparse networks without labels, improving performance.
BPASGM uses sparse graphical models to optimize portfolio selection.
We present an algorithm to identify sparse dependence structure in continuous and non-Gaussian probability distributions, given a corresponding set of data. The conditional independence structure of an arbitrary distribution can be represented as an undirected graph (or Markov random field), but most algorithms for lea…
SPARTAN learns sparse interaction graphs between objects in scenes.
The power of sparse signal modeling with learned over-complete dictionaries has been demonstrated in a variety of applications and fields, from signal processing to statistical inference and machine learning. However, the statistical properties of these models, such as under-fitting or over-fitting given sets of data, …
We propose and analyze a novel framework for learning sparse representations, based on two statistical techniques: kernel smoothing and marginal regression. The proposed approach provides a flexible framework for incorporating feature similarity or temporal information present in data sets, via non-parametric kernel sm…
In this study, we analyzed the activity of monkey V1 neurons responding to grating stimuli of different orientations using inference methods for a time-dependent Ising model. The method provides optimal estimation of time-dependent neural interactions with credible intervals according to the sequential Bayes estimation…
The presence of a sparse "truth" has been a constant assumption in the theoretical analysis of sparse PCA and is often implicit in its methodological development. This naturally raises questions about the properties of sparse PCA methods and how they depend on the assumption of sparsity. Under what conditions can the r…
In this paper, we consider the block-sparse signals recovery problem in the context of multiple measurement vectors (MMV) with common row sparsity patterns. We develop a new method for recovery of common row sparsity MMV signals, where a pattern-coupled hierarchical Gaussian prior model is introduced to characterize bo…
There has been considerable advance in understanding the properties of sparse regularization procedures in high-dimensional models. In time series context, it is mostly restricted to Gaussian autoregressions or mixing sequences. We study oracle properties of LASSO estimation of weakly sparse vector-autoregressive model…
Improves sparse recovery with non-linear Fourier features.
The Lasso is suboptimal in sparse linear regression due to design matrix constraints.
Sparse Gaussian Processes improve scalability by learning inducing points from data.
New DP optimization methods for sparse gradients, improving on existing algorithms.
EiGLasso speeds up sparse Kronecker-sum covariance estimation.
In this paper we formally analyse the use of sparse filtering algorithms to perform covariate shift adaptation. We provide a theoretical analysis of sparse filtering by evaluating the conditions required to perform covariate shift adaptation. We prove that sparse filtering can perform adaptation only if the conditional…