Paper presents machine learning for multi-target localization using various sensors.
problem Accurate multi-target localization for unmanned systems.
method Machine learning algorithms (K-means clustering, SVM) applied to sensor data.
result Different machine learning algorithms perform differently in data association and localization.
Paper introduces MLKM for multi-sensor data association in target localization.
problem Data-target association for efficient multi-target localization.
method Multi-layer K-means approach leveraging K-means++, deep neural networks, and backpropagation.
result MLKM outperforms K-means, K-means++, and deep neural networks in data association.
The paper proposes a method for valid multi-target regression predictions.
problem Valid multi-variate predictions for multi-target regression.
method Copula functions applied to deep neural networks for inductive conformal prediction.
result The proposed method ensures efficiency and validity for multi-target regression problems.
Proposes a new online learning strategy for multi-target regression in data streams.
problem Challenges in learning from high-throughput data streams, especially in multi-target regression.
method Extends existing online decision tree learning algorithm to consider inter-target dependencies.
result SST-HT presents superior predictive accuracy compared to state-of-the-art algorithms.
Extends covariance estimation with multiple targets for better performance.
problem Improving covariance estimation for multiple targets.
method Combines multiple constant matrices with sample covariance matrix, derives estimators and proves convergence.
result The multi-target linear shrinkage estimator outperforms other estimators in various situations.
A new method quantizes output space for multi-target regression.
problem Predicting multiple continuous targets using shared predictors.
method MRQ method that quantizes output space to model dependencies and scale.
result MRQ achieves high scalability and competitive accuracy.
Meta-learning system recommends best multi-target regression method.
problem Improving predictive performance in multi-target regression problems.
method Developed a meta-learning system to recommend the best multi-target regression method for a given problem.
result Meta-models were able to recommend the best method with a balanced accuracy superior to 70%.
Meta-learning approach improves CNN architectures for concrete defect classification.
problem Challenging task of recognizing defects in concrete infrastructure.
method Two reinforcement learning based meta-learning approaches (MetaQNN and NAS) for finding suitable CNN architectures.
result Learned architectures have fewer parameters and better multi-target accuracy.
VSPS creates flexible prediction regions for multi-target regression with guaranteed coverage.
problem Uncertainty quantification in multi-target regression with complex distributions.
method Conditional normalizing flows with conformal calibration to identify dense regions.
result VSPS produces smaller, more informative prediction regions with robust coverage guarantees.
Expands HMRL to multi-target problem considering agent distances.
problem Learning state-action rules for multiple targets.
method Hierarchical Modular Reinforcement Learning with AT field and C4.5.
result Improved reinforcement learning for multi-agent scenarios.
New methods detect targets from imprecisely labeled hyperspectral data.
problem Challenges in acquiring labeled hyperspectral data.
method Multi-Target MI-ACE and MI-SMF methods that learn target signatures from imprecisely labeled samples.
result Effective at learning target signatures and performing target detection.
A new method for multi-target regression robust to outliers.
problem Outliers in regression data affect the performance of ELM-based methods.
method Generalized Outlier Robust ELM (GOR-ELM) using ℓ2,1 norm and Elastic Net theory. result GOR-ELM outperforms other ELM-based techniques in datasets with outliers.
DBMTL optimizes multiple e-commerce targets using Bayesian networks.
problem Multi-target optimization in e-commerce platforms.
method Bayesian modeling of target events in a Bayesian network, with hidden layers parameterized by neural networks.
result Significant improvement in recommendation performance over other methods.
Unified view on multi-target prediction combining various machine learning subfields.
problem Simultaneous prediction of multiple target variables of diverse type.
method Formal discussion and general framework of MTP problems, identification of key properties for MTP methods.
result Unified understanding of multi-target prediction across different machine learning subfields.
Improved prediction of soil parameters using Multi-target Stacked Generalisation on EDXRF spectra.
problem Challenges in predicting multiple soil parameters accurately from EDXRF spectra.
method Multi-target Stacked Generalisation (MTSG) method combining multiple regression models.
result MTSG significantly improved prediction accuracy for multiple soil parameters, reducing average error from 0.67 to 0.64.
Deep model tackles zero-inflated multi-species abundance estimation.
problem Predicting species distribution across landscapes with inflated zero counts.
method Proposes a novel deep learning model combining multivariate probit and log-normal distributions.
result Model outperforms existing methods on bird and fish population datasets.
New framework uses score-based priors to solve ill-conditioned polynomial equations, improving signal recovery from noisy data.
problem Recovering signals from low-order moments in inverse problems, especially ill-conditioned polynomial equations.
method Integrates score-based diffusion priors with moment-based estimators to regularize and solve nonlinear inverse problems.
result Diffusion priors improve recovery from third-order moments and make super-resolution MTD feasible.
Paper detects review abuse using tensor decomposition.
problem Detecting review abuse by sellers and reviewers.
method Semi-supervised binary multi-target tensor decomposition.
result The model achieves higher precision and recall.
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.
Algorithm speeds up search for stationary targets with guaranteed accuracy.
problem Minimize search time while ensuring high detection accuracy of stationary targets.
method Multi-fidelity Gaussian process model and EMTS algorithm.
result Guaranteed performance in target detection accuracy and search time.
Paper proposes MCC to reduce class confusion for versatile DA.
problem Class confusion in DA methods limits their performance across different scenarios.
method Introduces Minimum Class Confusion (MCC) loss function to handle various DA scenarios.
result MCC significantly improves performance on diverse DA scenarios, including Multi-Source and Multi-Target DA.
Enhances particle filters with neural augmentation for multi-sub-state tracking.
problem Particle filters struggle with complex or approximated models and low latency requirements.
method Learning Flock (LF) uses a neural network to correct particle weights based on sub-particle relationships.
result LF improves performance, robustness, and latency in radar multi-target tracking.
Optimistic bounds for multi-output learning using self-bounding Lipschitz condition.
problem Learning vector-valued functions from supervised data.
method Introducing self-bounding Lipschitz condition and proving optimistic bounds using local Rademacher complexity and Srebro's inequality.
result Minimax optimal generalization bounds for multi-output learning, up to logarithmic factors.
The paper explores new rules for analyzing label rankings and pairwise preferences.
problem Mining patterns in multi-target relations for label ranking.
method Developed two types of association rules: Label Ranking Association Rules (LRAR) and Pairwise Association Rules (PAR). Conducted sensitivity analysis on similarity measures.
result Both LRAR and PAR show potential in analyzing multi-target relations.
Stein showed that the multivariate sample mean is outperformed by "shrinking" to a constant target vector. Ledoit and Wolf extended this approach to the sample covariance matrix and proposed a multiple of the identity as shrinkage target. In a general framework, independent of a specific estimator, we extend the shrink…
Strong baseline for medical imaging domain adaptation.
problem Improving generalization in medical imaging across different datasets.
method Training on diverse chest X-ray datasets.
result Empirical demonstration of model generalization to out-of-sample domains.
Proposes a deep tree-ensemble model for multi-output prediction.
problem Lack of efficient solutions for multi-output prediction.
method Integrates tree-embeddings into deep tree-ensembles for structured output prediction.
result Superior performance in multi-label classification and multi-target regression tasks.
Framework tracks and predicts multiple objects in autonomous driving.
problem Challenges in multi-target tracking due to object number fluctuation and occlusion.
method Constrained mixture sequential Monte Carlo (CMSMC) method with a mixture representation.
result Framework can track and predict multiple objects simultaneously without explicit data association.
Framework for multi-view redescription mining overcomes limitations of existing approaches.
problem Challenges in revealing non-trivial associations between different subsets of attributes (views).
method Memory-efficient, extensible framework using multi-target regression or multi-label classification algorithms.
result Framework can generate redescriptions from multiple views, improving over existing two-view approaches.
Paper proposes a reinforcement learning method for efficient information acquisition.
problem Solving the Active Information Acquisition problem for robotic systems.
method Reinforcement Learning approach to choose actions for acquiring information.
result Efficient policies for online execution and real-time control of robotic systems.
Joint sensing and communication network improves target localization and reduces communication load.
problem Efficiently localize multiple targets with reduced communication overhead.
method Multi-base station cooperative sensing with AI-aided clustering and tracking.
result Optimal sub-pattern assignment (OSPA) error less than 60 cm with reduced communication capacity.
A method for constructing tight prediction intervals for multiple numerical outputs.
problem Constructing tight prediction intervals for multiple related numerical outputs.
method A novel coordinate-wise standardization procedure that makes residuals comparable across output dimensions, estimating suitable scaling parameters using calibration data.
result The method produces tighter prediction intervals than existing baselines while maintaining valid simultaneous coverage.
LEAPS uses semantic models to improve reinforcement learning in diverse environments.
problem Generalizing and adapting to unseen environments in reinforcement learning.
method Hybrid model-based and model-free approach with a multi-target sub-policy and a Bayesian semantic model.
result LEAPS outperforms baselines in visual navigation tasks using diverse indoor scenes.
One-shot VC model converts voices without parallel data.
problem Limited VC applicability due to training data restriction.
method Disentangles speaker and content representations with instance normalization.
result Model converts voices from unseen speakers with high similarity.
JoLT uses LLMs to make probabilistic predictions on tabular data.
problem Making probabilistic predictions on tabular data efficiently and without preprocessing.
method JoLT leverages LLMs' in-context learning to define joint distributions over tabular data.
result JoLT outperforms other methods on tabular classification and regression tasks.
MO-CBO optimizes multiple outcomes in causal systems with minimal data.
problem Optimizing multiple outcomes in causal systems with limited data.
method Decomposes MO-CBO into multi-objective optimization tasks and uses relative hypervolume improvement for sequential intervention balancing.
result MO-CBO outperforms traditional multi-objective Bayesian optimization in causal settings.
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.
Deep neural networks classify T2D from retinal images with high accuracy.
problem Detecting early-stage Type 2 Diabetes from retinal images.
method Employed deep neural networks and multi-target learning to differentiate T2D from healthy individuals.
result Classification performance improved to AUC = 0.758 [±0.003] using images from both eyes. Neural network speeds up atmospheric chemistry modeling 4250x.
problem Computational expense of simulating atmospheric chemistry.
method Created a neural network to emulate a complex chemical mechanism.
result Achieved a 250x computational speedup.
Optimization framework for reconstructing missing mandible segments.
problem Missing mandible geometry in jaw reconstructive surgeries.
method Conditional variational autoencoder (CVAE) with weighted multi-target probabilistic solution.
result Statistically significant performance improvement over CVAE.
Framework transfers knowledge across multiple target domains without shared categories.
problem Learning unlabeled target domains without shared categories.
method Model parameter adaptation (PA-1SmT) to transfer knowledge through a common model parameter dictionary.
result Framework demonstrates superiority on three domain adaptation benchmark datasets.
New measures quantify uncertainty in survival models for maintenance tasks.
problem Uncertainty in survival models for maintenance tasks.
method Formal measures of ambiguity, discrepancy, and obscurity introduced.
result Multiple accurate survival models may yield conflicting risk estimates.
Improved malware detection by adding auxiliary loss terms to a neural network.
problem Malware detection accuracy with a single label.
method Fit deep neural networks to multiple auxiliary prediction targets derived from metadata.
result Significant improvement in detection performance, reducing false negatives by 42.6% at a low false positive rate.
BayesAME automatically determines coreset size for efficient model evaluation.
problem Time-consuming and computationally expensive evaluation of large generative models.
method Bayesian active model evaluation (BayesAME) that automatically selects coreset size.
result BayesAME consistently outperforms existing methods in diverse benchmarks.
Proposes semi-supervised feature ranking for handling high-dimensional, unlabeled data.
problem Handling high-dimensional, unlabeled data in machine learning.
method Tree ensembles and Relief family algorithms for semi-supervised feature ranking.
result Semi-supervised feature ranking outperforms supervised methods across most datasets.
AMEAN tackles BTDA by learning meta-sub-targets to bridge domain gaps and misalignments.
problem Blending-target Domain Adaptation (BTDA) with multiple sub-targets that are hard to distinguish.
method AMEAN uses two adversarial processes: first to align source and mixed target domains, second to learn meta-sub-targets.
result AMEAN significantly outperforms existing DA algorithms in BTDA scenarios.
We study locally compact contractive local groups, that is, locally compact local groups with a contractive pseudo-automorphism. We prove that if such an object is locally connected, then it is locally isomorphic to a Lie group. We also prove a related structure theorem for locally compact contractive local groups whic…
Every locally compact local group is locally isomorphic to a topological group.