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.
MT-SGD samples from multiple target distributions using gradient descent.
problem Sampling from multiple unnormalized target distributions.
method Proposes MT-SGD, a flow of intermediate distributions to sample from multiple target distributions.
result Asymptotic analysis shows MT-SGD reduces to multiple-gradient descent for multi-objective optimization.
A new method debiases multiple target parameters without IFs.
problem Debiasing multiple target parameters in nonparametric models.
method Kernel Debiased Plug-in Estimation (KDPE) using TMLE and reproducing kernel Hilbert spaces.
result KDPE simultaneously debiases all pathwise differentiable target parameters.
A multiple instance dictionary learning method using functions of multiple instances (DL-FUMI) is proposed to address target detection and two-class classification problems with inaccurate training labels. Given inaccurate training labels, DL-FUMI learns a set of target dictionary atoms that describe the most distincti…
Paper tackles spooky effect in OSPA estimation, showing GOSPA solves it.
problem Spooky effect in optimal estimation of multiple targets with OSPA metric.
method Introduces GOSPA metric (α=2) to penalize false and missed targets. result GOSPA avoids the spooky effect and optimally lowers target estimation errors.
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, …
Study shows algorithms benefit from limited target data with many source domains.
problem Adapting to new domains with scarce labeled target data.
method New family of model selection algorithms.
result Beneficial guarantees in scenarios with limited target data.
New method uses limited labeled data and multiple starts to adapt models across domains.
problem Accurate predictions in target domain with few labeled data.
method Fine-tuning from multiple adaptive starts, extending UDA methods.
result Minimax-optimal target performance with limited labeled target data.
Paper develops upper-bounds for target general loss in multiple source DA and DG settings.
problem Complexity and trade-offs in multiple source domain adaptation and domain generalization.
method Defines two types of domain-invariant representations and studies their pros, cons, and trade-offs.
result Developed upper-bounds for target general loss offer insights into domain-invariant representations.
EnMDAP aligns conditional distributions for multi-source domain adaptation using pseudolabels.
problem Training a target model with no labeled data in the absence of target data labels.
method EnMDAP uses label-wise moment matching and ensemble learning with multiple feature extractors.
result EnMDAP achieves state-of-the-art performance in multi-source domain adaptation tasks.
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.
Paper proposes a new method to aggregate multiple sources with different label distributions.
problem Aggregating from multiple target-shifted sources with different label distributions.
method Unified framework to select relevant sources for domain adaptation with limited label, unsupervised, and label partial unsupervised scenarios.
result Empirical results significantly outperform baselines.
Transfer learning aims to faciliate learning tasks in a label-scarce target domain by leveraging knowledge from a related source domain with plenty of labeled data. Often times we may have multiple domains with little or no labeled data as targets waiting to be solved. Most existing efforts tackle target domains separa…
In target tracking, the estimation of an unknown weaving target frequency is crucial for improving the miss distance. The estimation process is commonly carried out in a Kalman framework. The objective of this paper is to examine the potential of using neural networks in target tracking applications. To that end, we pr…
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.
Training complex machine learning models for prediction often requires a large amount of data that is not always readily available. Leveraging these external datasets from related but different sources is therefore an important task if good predictive models are to be built for deployment in settings where data can be …
EGMU optimizes portfolios using KL divergence, ensuring positive solutions.
problem Constructing multi-factor target-exposure portfolios efficiently and accurately.
method Convex optimization framework minimizing KL divergence, with explicit solvers.
result Established feasibility and uniqueness of strictly positive solutions under convex-hull conditions.
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.
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.
Policy-gradient method controls multiple non-cohesive targets.
problem Controlling multiple non-cohesive targets in a decentralized manner.
method Proximal Policy Optimization for target selection and driving.
result Effective control of non-cohesive targets without prior dynamics knowledge.
Study examines CSO algorithm for 3D swarming and tracking multiple targets.
problem Simulating and tracking multiple targets in 3D space.
method Cyclic Stochastic Optimization (CSO) algorithm implemented by mobile sensing agents.
result CSO algorithm converges in 3D space, minimizing uncertainty in targets' state estimates.
Estimates target GGM using auxiliary studies with false discovery rate control.
problem Estimating high-dimensional GGMs from related studies.
method Transfer learning with Trans-CLIME and debiased Trans-CLIME estimators.
result Debiased Trans-CLIME estimator provides element-wise asymptotic normality and false discovery rate control.
Optimizes profit in targeted marketing across multiple markets with varying marketing expenditures.
problem Maximizing profit in a sequential marketing strategy with multiple markets and varying marketing costs.
method Near-optimal algorithms in an adversarial bandit setting, proving regret bounds for different demand curve types.
result Proved near-optimal regret bounds for the profit-maximization problem in targeted marketing.
Method generates intermediate domains to align source and target domains.
problem Challenges of domain adaptation with significant domain divergence.
method Progressive domain augmentation via domain interpolation and multiple subspace alignment.
result Achieves state-of-the-art performance on multiple domain adaptation tasks.
This paper improves traditional Markowitz optimization by considering variance at multiple time scales.
problem Traditional Markowitz optimization limits to a single time scale, ignoring variance across different frequencies.
method Introduces multifrequency optimization allowing specification of target Hurst exponents across multiple time scales.
result Effective risk management strategy that aligns with investor preferences at various time scales.
Bayesian fusion improves radar target recognition for UAVs.
problem Improving radar target recognition for UAVs using multistatic radar configurations.
method Proposes a fully Bayesian RATR framework using Optimal Bayesian Fusion (OBF) to aggregate classification probability vectors from multiple radars.
result Empirical results show that the OBF method significantly enhances classification accuracy compared to other fusion methods and single radar configurations.
Generative model predicts multiple brain graphs from one, preserving topology.
problem Predicting multiple brain graphs from a single one, preserving topology.
method MultiGraphGAN architecture, graph adversarial auto-encoder, cluster-specific decoders, topological loss.
result Significantly outperformed variants in multi-view brain graph generation.
Unsupervised domain adaptation (UDA) aims to learn the unlabeled target domain by transferring the knowledge of the labeled source domain. To date, most of the existing works focus on the scenario of one source domain and one target domain (1S1T), and just a few works concern the scenario of multiple source domains and…
Improves domain adaptation by combining multiple source domains and target domain data.
problem Poor performance of empirical risk minimization in distributionally shifted target domains.
method Distributionally robust model optimizing adversarial reward based on explained variance across multiple source domains.
result The robust model is a weighted average of conditional outcome models from source domains.
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…
Proposes MDDA for multi-source domain adaptation.
problem Performance decay in deep neural networks due to domain shift between labeled and unlabeled data.
method Multi-source distilling domain adaptation (MDDA) network considering multiple source distributions and target similarities.
result Significantly outperforms state-of-the-art approaches on public DA benchmarks.
We present a novel approach for supervised domain adaptation that is based upon the probabilistic framework of Gaussian processes (GPs). Specifically, we introduce domain-specific GPs as local experts for facial expression classification from face images. The adaptation of the classifier is facilitated in probabilistic…
This work analyzes two methods for combining multiple binary labels in bipartite ranking.
problem Combining multiple binary labels for optimal bipartite ranking.
method Loss aggregation vs. label aggregation approaches.
result Label aggregation is preferable to loss aggregation due to label dictatorship issues.
Malware detection is a popular application of Machine Learning for Information Security (ML-Sec), in which an ML classifier is trained to predict whether a given file is malware or benignware. Parameters of this classifier are typically optimized such that outputs from the model over a set of input samples most closely…
We consider the problem of search through comparisons, where a user is presented with two candidate objects and reveals which is closer to her intended target. We study adaptive strategies for finding the target, that require knowledge of rank relationships but not actual distances between objects. We propose a new str…
New algorithm combines Geostatistics and Quantile Random Forests for non-stationary spatial modelling.
problem Non-stationary spatial modelling with multiple secondary variables.
method Combines Geostatistics and Quantile Random Forests to estimate conditional distributions and simulate spatial data.
result Consistent results similar to geostatistical and Quantile Random Forests, allowing for embedding simpler interpolation techniques.
Paper proposes a method to learn linear regression models using multiple pre-trained models.
problem Learning a linear regression model with limited target data.
method Representation transfer learning method using multiple pre-trained models.
result The method achieves better sample complexity compared to baseline methods.
Non-orthogonal joint diagonalization (NJD) free of prewhitening has been widely studied in the context of blind source separation (BSS) and array signal processing, etc. However, NJD is used to retrieve the jointly diagonalizable structure for a single set of target matrices which are mostly formulized with a single da…
Data-target pairing is an important step towards multi-target localization for the intelligent operation of unmanned systems. Target localization plays a crucial role in numerous applications, such as search, and rescue missions, traffic management and surveillance. The objective of this paper is to present an innovati…
A long standing problem in visual object categorization is the ability of algorithms to generalize across different testing conditions. The problem has been formalized as a covariate shift among the probability distributions generating the training data (source) and the test data (target) and several domain adaptation …
In this paper, we present a new approach to Transfer Learning (TL) in Reinforcement Learning (RL) for cross-domain tasks. Many of the available techniques approach the transfer architecture as a method of speeding up the target task learning. We propose to adapt and reuse the mapped source task optimal-policy directly …
While domain adaptation has been actively researched in recent years, most theoretical results and algorithms focus on the single-source-single-target adaptation setting. Naive application of such algorithms on multiple source domain adaptation problem may lead to suboptimal solutions. As a step toward bridging the gap…
The paper develops methods to identify stable associations across multiple studies.
problem Identifying stable associations across multiple studies with possible distributional shifts.
method Modeling heterogeneous multi-source data with multiple high-dimensional regressions and devising a novel sampling method for valid confidence intervals of maximin effects.
result Significant maximin effects indicate stable associations that can be generalized to target populations.
Paper compares machine learning methods for predicting target motion.
problem Challenges in accurately modeling target dynamics due to mismatch between assumed and true motion.
method Three machine learning methods (GPs, IMM, LSTM) compared against EKF.
result LSTM network outperforms other methods in real-world scenarios.
An algorithm learns from multiple models to match an oracle's risk.
problem Learning from multiple noisy models to estimate a target parameter.
method Elimination rounds algorithm for adaptive learning.
result Risk of weak-oracle learner matches that of an oracle in multiple source case.
Two-layer neural networks learn features through a few gradient descent steps, improving approximation capacity.
problem Improving approximation capacity of two-layer neural networks.
method Theoretical investigation of a two-layer neural network's adaptation to target function through a few gradient descent steps.
result Learning multiple target directions requires a larger batch size and more gradient steps, improving approximation capacity.
In silico drug-target interaction (DTI) prediction is an important and challenging problem in biomedical research with a huge potential benefit to the pharmaceutical industry and patients. Most existing methods for DTI prediction including deep learning models generally have binary endpoints, which could be an oversimp…
Bayesian Experience Reuse improves learning from multiple experts.
problem Learning from multiple experts with conflicting goals.
method Bayesian neural networks with shared features to model uncertainty and derive a probability distribution over expert models.
result BERS method effectively samples demonstrations from the derived distribution to reuse them in new tasks.