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.
IBPF algorithm tackles high-dimensional parameter learning for complex systems.
The Hodrick-Prescott (HP) filter is one of the most widely used econometric methods in applied macroeconomic research. Like all nonparametric methods, the HP filter depends critically on a tuning parameter that controls the degree of smoothing. Yet in contrast to modern nonparametric methods and applied work with these…
We propose a novel method for maximum likelihood-based parameter inference in nonlinear and/or non-Gaussian state space models. The method is an iterative procedure with three steps. At each iteration a particle filter is used to estimate the value of the log-likelihood function at the current parameter iterate. Using …
Kernel learning FBSDE filter improves nonlinear filtering efficiency.
We introduce the variational filtering EM algorithm, a simple, general-purpose method for performing variational inference in dynamical latent variable models using information from only past and present variables, i.e. filtering. The algorithm is derived from the variational objective in the filtering setting and cons…
This paper refines the Gaussian Sinkhorn algorithm for general multivariate models.
We present a filter correlation based model compression approach for deep convolutional neural networks. Our approach iteratively identifies pairs of filters with the largest pairwise correlations and drops one of the filters from each such pair. However, instead of discarding one of the filters from each such pair naï…
This paper improves collaborative filtering by integrating user and item embeddings with attention.
New Gibbs sampling reduces GLMB filtering complexity to linear time.
We present a general theory of fractal transformations and show how it leads to a new type of method for filtering and transforming digital images. This work substantially generalizes earlier work on fractal tops. The approach involves fractal geometry, chaotic dynamics, and an interplay between discrete and continuous…
Convolutional neural networks (CNNs) achieve state-of-the-art performance in a wide variety of tasks in computer vision. However, interpreting CNNs still remains a challenge. This is mainly due to the large number of parameters in these networks. Here, we investigate the role of compression and particularly pruning fil…
The paper develops a theory for iterative self-improvement of models, proving conditions for better performance with easy-to-hard curricula.
Improved Kalman filtering with hierarchical variational approach.
Study on discrepancy principle for learning algorithms in nonparametric regression.
Compressing convolutional neural networks (CNNs) is essential for transferring the success of CNNs to a wide variety of applications to mobile devices. In contrast to directly recognizing subtle weights or filters as redundant in a given CNN, this paper presents an evolutionary method to automatically eliminate redunda…
In this paper, we propose a probabilistic optimization method, named probabilistic incremental proximal gradient (PIPG) method, by developing a probabilistic interpretation of the incremental proximal gradient algorithm. We explicitly model the update rules of the incremental proximal gradient method and develop a syst…
AGE improves graph embedding by smoothing features and iteratively enhancing node embeddings.
ED-Filter improves eating disorder classification on Twitter.
Paper analyzes robustness of data-selective Volterra NLMS algorithm.
Method finds multiple noisy graph templates in large graphs.
We model leverage as stochastic but independent of return shocks and of volatility and perform likelihood-based inference via the recently developed iterated filtering algorithm using S&P500 data, contributing new evidence to the still slim empirical support for random leverage variation.
A new Bayesian filtering method speeds up stochastic Newton optimization.
Byzantine-resilient federated learning with local iterations and robust mean estimation.
A new surgery formula for knot lattice homology.
We devise a USDCHF trading strategy using the dynamics of gold as a filter. Our strategy involves modelling both USDCHF and gold using a coupled hidden Markov model (CHMM). The observations will be indicators, RSI and CCI, which will be used as triggers for our trading signals. Upon decoding the model in each iteration…
New algorithms robustly estimate mean with near-optimal error rates.
New method improves Kalman filtering and smoothing for large state spaces.
Combines neural networks with splitting-up method for filtering equations.
Although deep neural networks (NNs) have achievedstate-of-the-art accuracy in many visual recognition tasks,the growing computational complexity and energy con-sumption of networks remains an issue, especially for ap-plications on platforms with limited resources and requir-ing real-time processing. Filter pruning tech…
We study additive models built with trend filtering, i.e., additive models whose components are each regularized by the (discrete) total variation of their th (discrete) derivative, for a chosen integer . This results in th degree piecewise polynomial components, (e.g., gives piecewise constant co…
A new filter adapts to heavy-tailed data without tuning, improving performance in challenging conditions.
Appropriately designing the proposal kernel of particle filters is an issue of significant importance, since a bad choice may lead to deterioration of the particle sample and, consequently, waste of computational power. In this paper we introduce a novel algorithm adaptively approximating the so-called optimal proposal…
The paper tackles reward-relevance in offline RL with sparse decision dynamics.
New algorithm for decomposing multidimensional, non-stationary signals.
KOALA optimizes neural networks by treating loss as noisy measurements.
Collaborative filtering is an important technique for recommendation. Whereas it has been repeatedly shown to be effective in previous work, its performance remains unsatisfactory in many real-world applications, especially those where the items or users are highly diverse. In this paper, we explore an ensemble-based f…
Ens-CGP synthesizes ensemble-based inference with Gaussian processes.
Generative Bayesian Filtering improves inference in complex models without explicit density evaluations.
New AD methods improve likelihood estimation for partially observed systems.
We consider the problem of simultaneous reduction of acoustic echo, reverberation and noise. In real scenarios, these distortion sources may occur simultaneously and reducing them implies combining the corresponding distortion-specific filters. As these filters interact with each other, they must be jointly optimized. …
In this work, we study stability of distributed filtering of Markov chains with finite state space, partially observed in conditionally Gaussian noise. We consider a nonlinear filtering scheme over a Distributed Network of Agents (DNA), which relies on the distributed evaluation of the likelihood part of the centralize…
A recently-introduced class of probabilistic (uncertainty-aware) solvers for ordinary differential equations (ODEs) applies Gaussian (Kalman) filtering to initial value problems. These methods model the true solution and its first derivatives \emph{a priori} as a Gauss--Markov process , which is…
We present a filter pruning approach for deep model compression, using a multitask network. Our approach is based on learning a a pruner network to prune a pre-trained target network. The pruner is essentially a multitask deep neural network with binary outputs that help identify the filters from each layer of the orig…
Dynamic pruning during training reduces deep network complexity without significant accuracy loss.
New algorithm reduces contamination in supervised learning.
This paper reviews graph convolutional neural networks (GCNNs) through the lens of edge-variant graph filters. The edge-variant graph filter is a finite order, linear, and local recursion that allows each node, in each iteration, to weigh differently the information of its neighbors. By exploiting this recursion, we fo…
This paper explores online learning of dynamics and state using ensemble Kalman filters.
Deep learning explained through spectral filtering of hierarchical features.