arXiv research

The article improves prediction by aggregating Kalman recursions online.

The paper sets limits for sequential prediction and recursive algorithms using entropy analysis.

Proposes a recursive MPC scheme with probabilistic safety guarantees for uncertain dynamic systems.

Recursive prediction of graph signals with new nodes added.

The paper sets limits on prediction accuracy and generalization.

In its simplest form, the traffic flow prediction problem is restricted to predicting a single time-step into the future. Multi-step traffic flow prediction extends this set-up to the case where predicting multiple time-steps into the future based on some finite history is of interest. This problem is significantly mor…

Tab-TRM uses recursive model for insurance pricing on tabular data.

We introduce Recurrent Predictive State Policy (RPSP) networks, a recurrent architecture that brings insights from predictive state representations to reinforcement learning in partially observable environments. Predictive state policy networks consist of a recursive filter, which keeps track of a belief about the stat…

AdaVol adapts QML for real-time GARCH volatility prediction.

Paper uses averaging from many particle filters to approximate posterior predictive distributions.

Two new feature selection algorithms improve on RFE.

Paper analyzes time series prediction using empirical risk minimization.

Formulates Markov property for risk-sensitive dynamic optimisation.

The problem at the heart of this tutorial consists in modeling the path choice behavior of network users. This problem has been extensively studied in transportation science, where it is known as the route choice problem. In this literature, individuals' choice of paths are typically predicted using discrete choice mod…

Develops BPDS for better financial portfolio decisions.

We introduce a recursive adaptive group lasso algorithm for real-time penalized least squares prediction that produces a time sequence of optimal sparse predictor coefficient vectors. At each time index the proposed algorithm computes an exact update of the optimal $\ell_{1,\infty}$-penalized recursive least squares (R…

Bayesian method for multivariate autoregressive models with exogenous inputs.

While it has become common to perform automated translations on natural language, performing translations between different representations of mathematical formulae has thus far not been possible. We implemented the first translator for mathematical formulae based on recursive neural networks. We chose recursive neural…

RocketStack integrates predictions from multiple base learners using a recursive stacking architecture up to ten levels.

The class of Gaussian Process (GP) methods for Temporal Difference learning has shown promise for data-efficient model-free Reinforcement Learning. In this paper, we consider a recent variant of the GP-SARSA algorithm, called Sparse Pseudo-input Gaussian Process SARSA (SPGP-SARSA), and derive recursive formulas for its…

Dimensionality reduction is one of the key issues in the design of effective machine learning methods for automatic induction. In this work, we introduce recursive maxima hunting (RMH) for variable selection in classification problems with functional data. In this context, variable selection techniques are especially a…

Oblique BART improves tree-based predictions.

This study presents a rapid multiple incremental and decremental mechanism based on Weight-Error Curves (WECs) for support-vector analysis. Recursion-free computation is proposed for predicting the Lagrangian multipliers of new samples. This study examines Ridge Support Vector Models, subsequently devising a recursion-…

Molecule property prediction is a fundamental problem for computer-aided drug discovery and materials science. Quantum-chemical simulations such as density functional theory (DFT) have been widely used for calculating the molecule properties, however, because of the heavy computational cost, it is difficult to search a…

In this paper we develop a method for learning nonlinear systems with multiple outputs and inputs. We begin by modelling the errors of a nominal predictor of the system using a latent variable framework. Then using the maximum likelihood principle we derive a criterion for learning the model. The resulting optimization…

This article proposes and evaluates a technique to predict the level of interference in wireless networks. We design a recursive predictor that estimates future interference values by filtering measured interference at a given location. The predictor's parameterization is done offline by translating the autocorrelation…

In this work, we propose a simple but effective method to interpret black-box machine learning models globally. That is, we use a compact binary tree, the interpretation tree, to explicitly represent the most important decision rules that are implicitly contained in the black-box machine learning models. This tree is l…

Recursive neural networks have widely been used by researchers to handle applications with recursively or hierarchically structured data. However, embedded control flow deep learning frameworks such as TensorFlow, Theano, Caffe2, and MXNet fail to efficiently represent and execute such neural networks, due to lack of s…

Paper defines Farey Recursive Functions and explores their properties.

The paper explores generalizations of Mirzakhani's recursion and computes volumes for physical gravity models.

Recently, graph neural networks (GNNs) have proved to be suitable in tasks on unstructured data. Particularly in tasks as community detection, node classification, and link prediction. However, most GNN models still operate with static relationships. We propose the Graph Learning Network (GLN), a simple yet effective p…

New algorithm speeds up online mapping of unknown terrains.

Paper proposes a recursive GPSSM for efficient online learning.

In a recurrent setting, conventional approaches to neural architecture search find and fix a general model for all data samples and time steps. We propose a novel algorithm that can dynamically search for the structure of cells in a recurrent neural network model. Based on a combination of recurrent and recursive neura…

We revisit the development of grid based recursive approximate filtering of general Markov processes in discrete time, partially observed in conditionally Gaussian noise. The grid based filters considered rely on two types of state quantization: The \textit{Markovian} type and the \textit{marginal} type. We propose a s…

The paper proposes a new probability distribution for rooted trees.

This work presents an explicit-implicit procedure to compute a model predictive control (MPC) law with guarantees on recursive feasibility and asymptotic stability. The approach combines an offline-trained fully-connected neural network with an online primal active set solver. The neural network provides a control inpu…

New recursion formula for non-orientable surfaces resolves divergences.

Harer and Zagier proved a recursion to enumerate gluings of a $2d$-gon that result in an orientable genus $g$ surface, in their work on Euler characteristics of moduli spaces of curves. Analogous results have been discovered for other enumerative problems, so it is natural to pose the following question: how large is t…

Model predicts credit portfolio losses with contagion effects.

QB-Vine extends Quasi-Bayesian methods to high dimensions using vine copulas.

Predict covariance from features using convex optimization.

ORFit trains models on streaming data with one pass, minimizing memory and computational costs.

This paper studies recursive ensembles driven by Fibonacci updates, improving learning dynamics.

Solves a recursion for Gromov-Witten invariants of the unknot.

New recursion found for hyperbolic sphere volumes.

This work generalizes a formula linking Seiberg-Witten prepotential and topological recursion.

LASER compresses recursive model activations by exploiting their low-dimensional structure.