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.
Unified framework for shrinkage, thresholding, and regularization in normal mean estimation and linear regression.
Noise in linear networks minimizes sharpness and leads to shrinkage-thresholding.
This review summarizes five Lasso optimization algorithms.
Rank minimization (RM) is a wildly investigated task of finding solutions by exploiting low-rank structure of parameter matrices. Recently, solving RM problem by leveraging non-convex relaxations has received significant attention. It has been demonstrated by some theoretical and experimental work that non-convex relax…
Significant attention has been given to minimizing a penalized least squares criterion for estimating sparse solutions to large linear systems of equations. The penalty is responsible for inducing sparsity and the natural choice is the so-called norm. In this paper we develop a Momentumized Iterative Shrinkage Th…
Iterative shrinkage/thresholding algorithm (ISTA) is a well-studied method for finding sparse solutions to ill-posed inverse problems. In this letter, we present a data-driven scheme for learning optimal thresholding functions for ISTA. The proposed scheme is obtained by relating iterations of ISTA to layers of a simpl…
The paper accelerates ISTA and FISTA algorithms for composite optimization problems.
We propose a nonparametric method for detecting nonlinear causal relationship within a set of multidimensional discrete time series, by using sparse additive models (SpAMs). We show that, when the input to the SpAM is a -mixing time series, the model can be fitted by first approximating each unknown function with a …
Sparse coding is typically solved by iterative optimization techniques, such as the Iterative Shrinkage-Thresholding Algorithm (ISTA). Unfolding and learning weights of ISTA using neural networks is a practical way to accelerate estimation. In this paper, we study the selection of adapted step sizes for ISTA. We show t…
This paper converts ADMM to proximal gradient for efficient sparse estimation.
In recent years, unfolding iterative algorithms as neural networks has become an empirical success in solving sparse recovery problems. However, its theoretical understanding is still immature, which prevents us from fully utilizing the power of neural networks. In this work, we study unfolded ISTA (Iterative Shrinkage…
This paper introduces the factorial marked temporal point process model and presents efficient learning methods. In conventional (multi-dimensional) marked temporal point process models, event is often encoded by a single discrete variable i.e. a marker. In this paper, we describe the factorial marked point processes w…
Solving inverse problems with iterative algorithms is popular, especially for large data. Due to time constraints, the number of possible iterations is usually limited, potentially affecting the achievable accuracy. Given an error one is willing to tolerate, an important question is whether it is possible to modify the…
Metalearning optimizes autoencoder dimensions for efficient data representation.
Multivariate functional data from a complex system are naturally high-dimensional and have complex cross-correlation structure. The complexity of data structure can be observed as that (1) some functions are strongly correlated with similar features, while some others may have almost no cross-correlations with quite di…
We consider machine learning techniques to develop low-latency approximate solutions to a class of inverse problems. More precisely, we use a probabilistic approach for the problem of recovering sparse stochastic signals that are members of the -balls. In this context, we analyze the Bayesian mean-square-error …
HyperLISTA simplifies LISTA training with adaptive hyperparameters.
Paper introduces MGLasso for multiscale graph inference in clustering and network analysis.
New insights show NAG and FISTA converge linearly without knowing strong convexity modulus.
This paper develops a novel deep recurrent neural network for sequential signal reconstruction.
Proposes a novel neural architecture for sparse coding using learned greedy pursuit.
This paper approximates scattered data using samplet coordinates with sparsity constraints.
New model for shape graph registration with partial matching constraints.
Paper shows linear convergence of ISTA and FISTA for ill-conditioned images.
A new family of momentum coefficients improves the convergence rate of accelerated algorithms.