arXiv research

RL policy tracks dynamic targets in partially known environments robustly.

Bayesian deep learning improves seismic imaging uncertainty.

This research develops a dual-level reinforcement learning strategy to track daily VWAP accurately.

Detecting a specific horizon in seismic images is a valuable tool for geological interpretation. Because hand-picking the locations of the horizon is a time-consuming process, automated computational methods were developed starting three decades ago. Older techniques for such picking include interpolation of control po…

We address tracking and prediction of multiple moving objects in visual data streams as inference and sampling in a disentangled latent state-space model. By encoding objects separately and including explicit position information in the latent state space, we perform tracking via amortized variational Bayesian inferenc…

RL approach for target tracking with unknown dynamics and sensor control.

New RL method improves financial index tracking accuracy.

We consider predicting the user's head motion in 360-degree videos, with 2 modalities only: the past user's positions and the video content (not knowing other users' traces). We make two main contributions. First, we re-examine existing deep-learning approaches for this problem and identify hidden flaws from a thorough…

Commodity exchange-traded funds (ETFs) are a significant part of the rapidly growing ETF market. They have become popular in recent years as they provide investors access to a great variety of commodities, ranging from precious metals to building materials, and from oil and gas to agricultural products. In this article…

Optimal portfolio tracking with dynamic capital injection into a ratcheting benchmark.

This paper optimizes tracking portfolios in incomplete markets using reinforcement learning.

New algorithm tracks changes in infinite action space rewards.

In this paper, we propose a novel Reinforcement Learning approach for solving the Active Information Acquisition problem, which requires an agent to choose a sequence of actions in order to acquire information about a process of interest using on-board sensors. The classic challenges in the information acquisition prob…

New approach simplifies proof of wave equations on black holes.

A new algorithm removes stale observations in dynamic Bayesian optimization.

Improved stochastic approximation method reduces residual error.

We consider prediction with expert advice under the log-loss with the goal of deriving efficient and robust algorithms. We argue that existing algorithms such as exponentiated gradient, online gradient descent and online Newton step do not adequately satisfy both requirements. Our main contribution is an analysis of th…

New meta-reinforcement learning method improves performance in finite-horizon MDPs.

This work uses SVM to identify track component failures in AC Track Circuits.

New train tracks for complex homeomorphisms found.

New method uses cluster shapes to improve track finding in particle collisions.

This handbook translates lead time analysis into R code.

The use of neural networks to predict airport passenger activity choices inside the terminal is presented in this paper. Three network architectures are proposed: Feedforward Neural Networks (FNN), Long Short-Term Memory (LSTM) networks, and a combination of the two. Inputs to these models are both static (passenger an…

Algorithmic trading systems on DEXs reject most candidate tokens, but the counterfactual outcome of rejected candidates is rarely measured.

This paper optimizes object tracking on edge devices with small matrices.

New framework shifts bandit algorithms from expected reward to preference metrics, optimizing mixtures of arms.

A DRL-based strategy improves vehicle tracking accuracy while saving energy.

Paper introduces TAP-Vid, a benchmark for tracking any point in videos.

The efficiency of a modern economy depends on what we call the Value-Tracking Hypothesis: that market prices of key assets broadly track some underlying value. This can be expected if a sufficient weight of market participants are valuation-based traders, buying and selling an asset when its price is, respectively, bel…

We show that the subsurface projection of a train track splitting sequence is an unparameterized quasi-geodesic in the curve complex of the subsurface. For the proof we introduce induced tracks, efficient position, and wide curves. This result is an important step in the proof that the disk complex is Gromov hyperbolic…

Train track automata for fully irreducible elements in Out(F_r).

Dynamic tracking error framework shows similar performance but varying volatility across different constraints.

Paper improves Lasso for S&P500 index tracking with post-selection inference.

Bayesian approach for constructing and rebalancing sparse index-tracking portfolios.

Automated method selects eye tracking variables for categorization tasks.

We propose a new Bayesian tracking and parameter learning algorithm for non-linear non-Gaussian multiple target tracking (MTT) models. We design a Markov chain Monte Carlo (MCMC) algorithm to sample from the posterior distribution of the target states, birth and death times, and association of observations to targets, …

Paper translates train track concepts to cluster algebras for pseudo-Anosov mapping classes.

This paper reviews and analyzes various modeling approaches for financial index tracking.

New data-driven Cartan connection tracks complex vascular structures.

A new method tracks retinal vessels more accurately than existing methods.

Online Multi-Object Tracking (MOT) from videos is a challenging computer vision task which has been extensively studied for decades. Most of the existing MOT algorithms are based on the Tracking-by-Detection (TBD) paradigm combined with popular machine learning approaches which largely reduce the human effort to tune a…

We study a series of static and dynamic portfolios of VIX futures and their effectiveness to track the VIX index. We derive each portfolio using optimization methods, and evaluate its tracking performance from both empirical and theoretical perspectives. Among our results, we show that static portfolios of different VI…

Perfect tracking control for real-world Euler-Lagrange systems is challenging due to uncertainties in the system model and external disturbances. The magnitude of the tracking error can be reduced either by increasing the feedback gains or improving the model of the system. The latter is clearly preferable as it allows…

Online learners track optimal solutions with constant step-size.

Hybrid approach combines transformer and Bayesian filtering for robust multiple particle tracking.

The main theorem of this paper classifies the quasi-geodesics in a Coxeter group that are tracked by geodesics. As corollaries, we show that if a Coxeter group acts geometrically on a CAT(0) space X then CAT(0) rays (and lines) are tracked by Cayley graph geodesics, all special subgroups of the Coxeter group are quasi-…

Paper introduces EnDKF for more accurate pose tracking.

We investigate the use of self-tracking data and unsupervised mixed-membership models to phenotype endometriosis. Endometriosis is a systemic, chronic condition of women in reproductive age and, at the same time, a highly enigmatic condition with no known biomarkers to monitor its progression and no established staging…