Study on VIX futures portfolios to track VIX index, finding dynamic strategy superior.
problem Tracking VIX index with VIX futures lagging.
method Optimization methods for static portfolios, dynamic trading strategy for VIX futures.
result Dynamic trading strategy outperforms static portfolios and VXX in tracking VIX.
Online learners track optimal solutions with constant step-size.
problem Tracking optimal solutions in online learning settings.
method Established a link between steady-state performance and tracking performance using analogies with adaptive filters.
result Inferred tracking performance from steady-state expressions directly.
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, …
A new neural network approach reduces tracking error in index replication.
problem Efficiently replicating an index with cardinality constraints.
method Reparametrisation and stochastic neural networks for optimisation.
result Our model achieves the lowest tracking error compared to benchmarks.
This paper studies the empirical tracking performance of leveraged ETFs on gold, and their price relationships with gold spot and futures. For tracking the gold spot, we find that our optimized portfolios with short-term gold futures are highly effective in replicating prices. The market-traded gold ETF (GLD) also exhi…
A new method tracks index using topological data analysis for sparse portfolios.
problem Sparse index tracking with robust risk management.
method Topological learning via Vietoris-Rips filtration for sparse regularization.
result The method outperforms state-of-the-art techniques in various market conditions.
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…
The tracking method based on the extreme learning machine (ELM) is efficient and effective. ELM randomly generates input weights and biases in the hidden layer, and then calculates and computes the output weights by reducing the iterative solution to the problem of linear equations. Therefore, ELM offers the satisfying…
Robust index tracking strategy improves performance during market downturns.
problem Optimizing asset management with uncertainty in asset distributions.
method Forward-looking approach using Bregman divergence for robust optimization.
result The robust index tracking strategy outperforms the non-robust strategy during market downturns.
Improved tracking and prediction of moving objects in visual data streams.
problem Tracking and predicting multiple moving objects in visual data streams.
method Disentangled latent state-space model with amortized variational Bayesian inference.
result Significantly improved long-term prediction and object decomposition in the presence of occlusions.
This paper reviews and analyzes various modeling approaches for financial index tracking.
problem Efficient replication of market index performance in financial markets.
method Categorization into three frameworks: optimization, statistical, and machine learning; empirical study on S&P 500 dataset.
result Optimization-based models deliver the most precise index tracking, statistical-based models achieve the strongest return-risk balance, and data-driven models provide competitive performance.
Hybrid approach combines transformer and Bayesian filtering for robust multiple particle tracking.
problem Challenges in tracking multiple particles in noisy scenes due to combinatorial explosion of hypotheses.
method Attention-Bayesian hybrid framework using transformer for association and Bayesian filtering for pruning hypotheses.
result Improved tracking accuracy and robustness against spurious detections.
A new tracking method using expert selection and feature fusion.
problem Efficient visual tracking with multiple component trackers.
method Pre-event selection of experts based on past performance and feature fusion.
result Superior performance compared to ensembled trackers on public datasets.
Paper improves Lasso for S&P500 index tracking with post-selection inference.
problem Index tracking for S&P500 with many applications.
method Used Lasso for dimension reduction and post-selection inference.
result Lasso method for S&P500 index tracking shows high performance.
Dynamic tracking error framework shows similar performance but varying volatility across different constraints.
problem Differences in governance parameters between Total Portfolio Approach and Strategic Asset Allocation.
method Portfolio simulations using U.S. equity and bond data from 2000 to 2026, spanning 2004 to 2026.
result Realized tracking error volatility varies 12-fold across different constraints, with costs highest during crises.
HKF uses neural networks to adapt Kalman filters for dynamic channel tracking.
problem Tracking channels with varying dynamics and Doppler values.
method Combines Kalman filters with hypernetworks for dynamic adaptation.
result HKF achieves up to 2dB gain over Kalman filters at high Doppler values.
Framework for monitoring ML model performance in production without labels.
problem Monitoring real-time prediction quality of ML models in production without labels.
method ML Health framework using diagnostic methods to generate alerts for further investigation.
result The method outperforms standard distance metrics at detecting issues with mismatched data sets.
BootsTAP uses real-world data to improve TAP tracking performance.
problem Lack of large-scale groundtruth training data for TAP.
method Bootstrapped training with a self-supervised student-teacher setup.
result State-of-the-art performance on TAP-Vid benchmarks.
IrisNet tracks tongue contours in ultrasound videos using deep learning.
problem Automatic and real-time tracking of tongue contours in ultrasound videos.
method Convolutional neural network mimicking peripheral vision.
result Outstanding generalization on various ultrasound tongue datasets.
Two adaptive algorithms improve tracking regret in dynamic expert advice problems.
problem Prediction with expert advice in dynamic environments.
method Developed two adaptive and efficient algorithms using online mirror descent framework.
result Achieved data-dependent tracking regret bounds for both algorithms.
AdaPart efficiently samples permanent distributions, improving tracking performance.
problem Computing the permanent of non-negative matrices efficiently.
method AdaPart: simple, efficient method for sampling from unnormalized distributions.
result AdaPart provides tight bounds on the permanent with high probability and polynomial runtime.
Equations track profits and losses in trading algorithms.
problem Evaluating the performance of trading algorithms.
method Formal equations incorporating spread.
result Evaluate trading model algorithms' performance.
A novel tracking algorithm models dynamic objects as ellipsoids with time-varying orientation.
problem Tracking dynamic objects with time-varying orientation.
method Random matrix framework with variational Bayes for non-linear inference.
result The method outperforms state-of-the-art methods in accuracy and robustness.
MPP predicts model performance in real-time ML deployments.
problem Lack of labels in production prevents tracking model performance.
method Ensemble of metrics to create a score for prediction quality.
result Automates monitoring and enables scale ML deployments.
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…
A new method tracks market performance without active management.
problem Active portfolio management does not outperform benchmarks.
method Developed a hybrid PCA-based tracking portfolio strategy.
result The hybrid PCA strategy outperforms optimization-based approaches.
DeepDA uses LSTM to track multiple targets in clutter.
problem NP-hard combinatorial optimization in multi-target tracking with clutter.
method LSTM-based deep learning for data association.
result Significant performance on association ratio, target ID switching, and time-consuming tracking.
EggNet reconstructs particle tracks from hits using evolving graph attention networks.
problem Particle track reconstruction is computationally expensive and combinatorial.
method EggNet uses a one-shot object condensation approach with evolving graph attention networks.
result EggNet outperforms methods requiring fixed input graphs on TrackML dataset.
Algorithm detects changes online using expert tracking.
problem Online change point detection in nonparametric settings.
method Sequential score function estimation and tracking the best expert approach.
result Algorithm performs well in artificial and real-world data.
We present a framework for supervised subspace tracking, when there are two time series xt and yt, one being the high-dimensional predictors and the other being the response variables and the subspace tracking needs to take into consideration of both sequences. It extends the classic online subspace tracking work…
Hybrid quantum-classical method optimizes financial index tracking.
problem Optimizing asset weights for financial index replication.
method Hybrid quantum-classical optimization with pruning algorithm.
result Improved performance through quantum and classical optimization.
A new method for tracking objects using diverse templates.
problem Improving visual tracking performance and robustness.
method Proposes a framework that uses additional object templates and a new diversity measure in siamese feature space.
result Achieves strong empirical results on tracking benchmarks, improving performance and robustness.
Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be highly time consuming. In motor control studies, humans or other …
This work presents GROUSE (Grassmanian Rank-One Update Subspace Estimation), an efficient online algorithm for tracking subspaces from highly incomplete observations. GROUSE requires only basic linear algebraic manipulations at each iteration, and each subspace update can be performed in linear time in the dimension of…
New data-driven Cartan connection tracks complex vascular structures.
problem Tracking complex vascular structures in multi-orientation images.
method Formulated a data-driven Cartan connection on M2 for geodesic tracking. result Improved geodesic tracking of vascular trees with globally optimal curves.
This paper presents GRASTA (Grassmannian Robust Adaptive Subspace Tracking Algorithm), an efficient and robust online algorithm for tracking subspaces from highly incomplete information. The algorithm uses a robust l1-norm cost function in order to estimate and track non-stationary subspaces when the streaming data …
Optimizes sliding window approach for tracking Gaussian densities.
problem Improving tracking performance of Gaussian density estimation.
method Theoretical analysis of sliding window Gaussian Kernel Density Estimators.
result Empirical evidence shows improved tracking performance with optimal weight sequence.
The paper analyzes constrained optimal portfolios in high dimensions using novel statistical learning techniques.
problem Forming optimal portfolios with constraints in high-dimensional asset spaces.
method CROWN method integrating factor models with nodewise regression for estimation in large dimensions.
result Demonstrates estimation consistency and convergence rates for constrained portfolio weights, risk, and Sharpe Ratio.
A new model tracks indices without rebalancing, solving NP-hard problems.
problem Tracking indices without rebalancing and minimizing deviations.
method Metaheuristic algorithms and local branching for solving mixed integer linear programming.
result The heuristic generates portfolios that outperform commercial solvers in both in-sample and out-of-sample data.
Generative model predicts menstrual cycle lengths accounting for self-tracking artifacts.
problem Uncertainty in self-tracked health data due to user adherence.
method Hierarchical, generative model using machine learning.
result Model yields state-of-the-art performance in predicting menstrual cycle lengths.
In this work, we introduce a deep-structured conditional random field (DS-CRF) model for the purpose of state-based object silhouette tracking. The proposed DS-CRF model consists of a series of state layers, where each state layer spatially characterizes the object silhouette at a particular point in time. The interact…
A robust visual tracking system requires an object appearance model that is able to handle occlusion, pose, and illumination variations in the video stream. This can be difficult to accomplish when the model is trained using only a single image. In this paper, we first propose a tracking approach based on affine subspa…
This paper optimizes decarbonized indices for financial tracking, balancing risk and environmental impact.
problem Balancing financial performance with environmental responsibilities in the context of climate risks.
method Develops decarbonized indices using mean-VaR and mean-ES optimization methods.
result Optimized indices reduce financial risk and carbon footprint, providing a balanced investment option.
Federated learning adapts to data and model drifts.
problem Performance guarantees in federated learning for dynamic problems.
method Random subset updates, non-stationary random walk model, tracking term.
result Performance depends on data variability, model variability, and learning rate.
The automatic reconstruction of three-dimensional particle tracks from Active Target Time Projection Chambers data can be a challenging task, especially in the presence of noise. In this article, we propose a non-parametric algorithm that is based on the idea of clustering point triplets instead of the original points.…
Optimal text-based indices track VIX and inflation.
problem Maximizing contemporaneous relation or predictive performance with target variables.
method Optimizing text-based indices focusing on VIX and inflation expectations.
result Superior performance compared to existing indices.
SOOTT framework optimizes target tracking with robust and learning-augmented algorithms.
problem Optimizing target tracking in dynamic environments with adversarial perturbations.
method Integrates robust and learning-augmented algorithms for online decision-making.
result CoRT learning-augmented algorithm strictly improves over robust BEST when predictions are accurate.
PiVoT improves real-time multi-object detection and tracking in clutter.
problem Challenges in multi-object detection and tracking from noisy point clouds.
method Variational inference for fast, clutter-resilient multi-object tracking.
result Substantial performance improvement over existing Bayesian trackers.