The paper improves classification accuracy by leveraging a shared signal across domains in high-dimensional classification.
problem Improving classification accuracy in high-dimensional data with shared signals across domains.
method Transfer learning for linear discriminant analysis, decomposing mean differences into common and domain-specific components.
result Deterministic limits for transfer performance, leading to optimal weights and corrections for bias.
We propose a novel classification model for weak signal data, building upon a recent model for Bayesian multi-view learning, Group Factor Analysis (GFA). Instead of assuming all data to come from a single GFA model, we allow latent clusters, each having a different GFA model and producing a different class distribution…
Better signal detection in undersampled data using joint and cross covariances.
problem Detecting shared signals in high-dimensional data with limited samples.
method Analysis of three covariance matrices: individual, cross, and joint.
result Joint and cross covariance matrices detect signals earlier than individual covariances.
Study optimizes shared singular subspace estimation from noisy matrices.
problem Estimating shared singular subspaces across multiple noisy matrices.
method Low-rank matrix denoising framework with Stack-SVD and novel estimators.
result Stack-SVD achieves minimax rate-optimality for identical shared subspaces, and novel estimators for partial sharing.
Kernel method embeds noisy datasets, capturing shared structures.
problem Limited power in capturing nonlinear structures, noisiness, high-dimensionality, and interpretability issues.
method Kernel spectral joint embeddings using duo-landmark integral operators.
result Consistent recovery of low-dimensional noiseless signals and convergence to eigenfunctions of integral operators.
PCA++ improves robustness to background noise in contrastive learning.
problem Recovering shared signal subspaces from positive pairs in high-dimensional data with structured background noise.
method PCA++ uses hard uniformity-constrained contrastive learning to enforce identity covariance on projected features.
result PCA++ outperforms standard PCA and alignment-only PCA+ in simulations and real-world datasets.
SIMPLE-RC method tests group membership profiles in large networks with weak signals.
problem Testing group membership profiles in large networks with weak signals.
method Random coupling technique to construct maximum SIMPLE tests for subsampled node pairs.
result Asymptotic distributions of SIMPLE-RC test are derived, enabling delicate analysis.
Investor flows in Korean equity market transmit shared information, not private signals.
problem Whether investor flows transmit private information or only public signals.
method Transfer Entropy networks constructed from investor-type flows over
umNDates{} trading days.
result Investor flows transmit shared information, not private signals.
Researchers develop a method to measure treatment effects in settings with shared states.
problem Measuring treatment effects in settings with shared states like prices, recommendations, or social signals.
method Double machine learning (DML) theorem with conditions for efficient inference under shared-state interference.
result Efficient estimation of average direct effect (ADE) and global average treatment effect (GATE) in various models.
Enhancing spectral embedding for low-dimensional embeddings in rare disease cohorts
problem Representing clinical concepts and patients in electronic health records
method Spectral-based unsupervised learning with flexible knowledge transfer
result Outperforms competing approaches in challenging scenarios
Proposes a Bayesian approach for integrating multiple linked matrices.
problem Integrating multiple linked matrices for diverse applications.
method Empirical Bayes Linked Matrix Decomposition (EB-LMD).
result Efficient estimation algorithm with no tuning parameters.
Estimates signals from a continuous dictionary with sparse mixtures using optimization.
problem Estimating signals from a continuous dictionary with unknown mixtures and noise.
method Formulates a regularized optimization problem with data fidelity and (ℓ1,Lp)-penalty. result High probability bounds on prediction error for the Group-Nonlinear-Lasso solution.
We present the group fused Lasso for detection of multiple change-points shared by a set of co-occurring one-dimensional signals. Change-points are detected by approximating the original signals with a constraint on the multidimensional total variation, leading to piecewise-constant approximations. Fast algorithms are …
DFI maps covariates to latent representations for feature importance.
problem Feature importance when predictors are statistically dependent.
method Disentangled Feature Importance (DFI) using entropic optimal transport.
result DFI yields stable, interpretable, uncertainty-quantified attributions of shared predictive signal.
HCL learns shared and modality-specific latent representations for multimodal data.
problem Binary shared-private decomposition inadequately represents shared information across subsets of modalities.
method Hierarchical Contrastive Learning framework combining latent-variable formulation, structural sparsity, and contrastive objective.
result HCL accurately recovers hierarchical structure and improves predictive performance on multimodal data.
Next generation networks are expected to be ultradense and aim to explore spectrum sharing paradigm that allows users to communicate in licensed, shared as well as unlicensed spectrum. Such ultra-dense networks will incur significant signaling load at base stations leading to a negative effect on spectrum and energy ef…
Optimal distributed testing under communication constraints with shared randomness.
problem Signal detection in a distributed system with limited communication.
method Derivation of minimax testing errors, distributed testing algorithms, and theoretical lower bounds.
result Consistent nonparametric distributed testing is possible even with minimal communication.
We present a novel and hierarchical approach for supervised classification of signals spanning over a fixed graph, reflecting shared properties of the dataset. To this end, we introduce a Convolutional Cluster Pooling layer exploiting a multi-scale clustering in order to highlight, at different resolutions, locally con…
Boosts share routing for multi-task learning with flexible sparse connections.
problem Designing suitable sharing mechanisms among multiple tasks in multi-task learning.
method Proposes MTNAS framework to modularize sharing into sub-networks with sparse connections and gating.
result Demonstrates consistent improvement over single-task and typical multi-task methods while maintaining efficiency.
Financial economic models often assume that investors know (or agree on) the fundamental value of the shares of the firm, easing the passage from the individual to the collective dimension of the financial system generated by the Share Exchange over time. Our model relaxes that heroic assumption of one unique "true val…
This work formalizes and extends parameter sharing in multi-agent reinforcement learning.
problem Parameter sharing limits multi-agent learning to a single policy, preventing different tasks or action spaces.
method Introduces agent indication and extends parameter sharing to heterogeneous observation and action spaces.
result Proves convergence to optimal policies for parameter sharing in heterogeneous environments.
LLT-ECG classifies ECG signals without backpropagation using linear laws.
problem ECG signal classification efficiency and verifiability.
method Forward linear law identification for ECG signal features.
result State-of-the-art performance on real-world ECG datasets.
Unified analysis of neural networks for sparse signal recovery.
problem Sparse signal recovery from few linear measurements.
method Introduces a general class of neural networks with weight-sharing, analyzes their Rademacher complexity, and derives generalization bounds.
result Derives generalization bounds that depend linearly on the number of parameters and depth, applicable to various neural network types.
The paper optimizes sensor selection for network time series data.
problem Optimizing sensor selection for network time series data with minimal error.
method Data-driven strategies to turn off sensors or select a sampling set of nodes.
result Proposes and compares various data-driven strategies for sensor selection.
This paper introduces a cross adversarial source separation (CASS) framework via autoencoder, a new model that aims at separating an input signal consisting of a mixture of multiple components into individual components defined via adversarial learning and autoencoder fitting. CASS unifies popular generative networks l…
Surveying joint Gaussian graphical models to identify shared structures across domains.
problem Estimating shared structures across different data sources.
method Statistical inference of joint Gaussian graphical models.
result Improved estimation power for high-dimensional data.
Study optimal trading strategies with differing views and market prices.
problem Maximizing portfolio value with subjective asset value vs market price.
method Mean-field game approach to analyze interactions among agents with differing signals.
result Cross-sectional distribution of agents' inventories and price distribution dependence on shared information.
CNNs require fewer samples than LCNs and FCNs for image-based tasks due to locality and weight sharing.
problem Quantifying statistical benefits of locality and weight sharing in CNNs over LCNs and FCNs.
method Dynamic Signal Distribution (DSD) task and information theoretic tools.
result CNNs require fewer samples than LCNs and FCNs for image-based tasks.
New method disentangles shared and private latent factors in multimodal data.
problem Challenges in disentangling shared and private latent factors in multimodal data.
method Proposes a modification to existing multimodal Variational Autoencoders (MMVAE) to better handle modality-specific variation.
result Demonstrates improved robustness of modified MMVAE to modality-specific variation.
'Sharing of statistical strength' is a phrase often employed in machine learning and signal processing. In sensor networks, for example, missing signals from certain sensors may be predicted by exploiting their correlation with observed signals acquired from other sensors. For humans, our hands move synchronously with …
PLS-SVD struggles with missing data in multimodal datasets, showing a phase transition in performance.
problem Missing data in PLS-SVD for multimodal datasets.
method Replica-symmetric analysis of spiked rectangular random matrices with missing entries.
result PLS-SVD performance transitions from uninformative to informative singular vectors at a critical signal-to-noise threshold.
This paper analyzes AJIVE for estimating shared subspace across multiple datasets, revealing its strengths and limitations.
problem Estimating shared subspace across multiple datasets with varying degrees of misalignment.
method Angle-based Joint and Individual Variation Explained (AJIVE) method, a two-stage spectral approach.
result AJIVE's performance in high signal-to-noise ratio (SNR) regimes and its non-diminishing error in low-SNR settings.
This paper studies the equilibrium pricing of asset shares in the presence of dynamic private information. The market consists of a risk-neutral informed agent who observes the firm value, noise traders, and competitive market makers who set share prices using the total order flow as a noisy signal of the insider's inf…
CNN model for efficient wireless spectrum sensing and signal identification.
problem Efficient utilization of scarce wireless spectrum.
method Convolutional Neural Network (CNN) based on spectral correlation function.
result Significant performance gains over existing methods.
A new method for analyzing multi-source, multi-way data reduces dimensionality and reveals shared and individual structures.
problem Analyzing multi-source, multi-way data from different high-throughput technologies.
method Multiple Linked Tensor Factorization (MULTIFAC) extending CP decomposition with L2 penalties and EM algorithm for incomplete data.
result MULTIFAC approximates underlying signal, identifies shared and unshared structures, and imputes missing data.
The paper examines A/B tests in recommendation systems to detect biased algorithm comparisons due to shared data.
problem Bias in comparing recommendation algorithms due to shared data.
method Formalized as a multi-armed bandit problem, analyzed the sign of difference-in-means estimator vs true GTE.
result Data sharing can lead to biased comparisons of recommendation algorithms, and a detection procedure is proposed.
The increased availability of the multi-view data (data on the same samples from multiple sources) has led to strong interest in models based on low-rank matrix factorizations. These models represent each data view via shared and individual components, and have been successfully applied for exploratory dimension reduct…
Research shows eco-innovation boosts earnings management, especially in constrained firms.
problem The impact of eco-innovation on earnings management in firms with financial constraints.
method Multi-method approach including entropy balancing, PSM, and Heckman Test correction.
result Eco-innovation positively correlates with earnings management, especially in firms facing financial constraints.
Proposes a new graph trend filtering model for inhomogeneous graph signals.
problem Estimating piecewise smooth signals over a graph with varying smoothness levels.
method Introduces a l2,0 norm penalized Graph Trend Filtering (GTF) model and two solution methods: spectral decomposition and simulated annealing.
result The GTF model performs better than existing approaches in denoising, support recovery, and semi-supervised classification.
Advances in molecular "omics'" technologies have motivated new methodology for the integration of multiple sources of high-content biomedical data. However, most statistical methods for integrating multiple data matrices only consider data shared vertically (one cohort on multiple platforms) or horizontally (different …
Attention mechanism combines bottom-up and top-down signals in neural networks.
problem Combining robust perception with bottom-up and top-down signals.
method Attention mechanism over modulated recurrent neural networks.
result Bidirectional information flow leads to improved performance in various tasks.
Proposes a federated learning approach for industrial asset failure prediction.
problem Lack of data and privacy concerns in industrial prognostics.
method Two-stage federated learning: dimension reduction and parameter estimation.
result Validated the approach using simulated and real data.
Change detection involves segmenting sequential data such that observations in the same segment share some desired properties. Multivariate change detection continues to be a challenging problem due to the variety of ways change points can be correlated across channels and the potentially poor signal-to-noise ratio on …
Proposes a federated learning approach for RUL prediction from nonparametric degradation and failure signals.
problem Cost-effective RUL prediction from limited, non-shared CM signals with unknown parametric forms.
method Joint modeling of nonlinear degradation signals and failure events using federated learning.
result Superior RUL prediction compared to alternatives, validated through simulations and real data.
Study improves KRR for non-i.i.d. data, with applications in denoising.
problem Kernel regression in structured non-i.i.d. settings.
method Developed a blockwise decomposition method for dependent data, deriving excess risk bounds.
result Established generalization guarantees for KRR in non-i.i.d. settings.
CARE improves LLM aggregation by accounting for shared confounders.
problem LLM judges' correlated errors due to shared latent confounders.
method CARE explicitly models judges' scores as true quality and confounders.
result CARE reduces aggregation error by up to 26.8% across various benchmarks.
This paper proposes a nonparametric Bayesian method for exploratory data analysis and feature construction in continuous time series. Our method focuses on understanding shared features in a set of time series that exhibit significant individual variability. Our method builds on the framework of latent Diricihlet alloc…
Sparse modeling is a powerful framework for data analysis and processing. Traditionally, encoding in this framework is performed by solving an L1-regularized linear regression problem, commonly referred to as Lasso or Basis Pursuit. In this work we combine the sparsity-inducing property of the Lasso model at the indivi…