Enhances robustness of MOGP regression for multiple correlated outputs.
problem Model misspecification and outliers in MOGP regression.
method Extends RCGP framework to multi-output setting.
result Provable robust MOGP with joint correlation capture.
The paper presents a novel approach to multi-output regression using probabilistic circuits.
problem Capturing correlations between multiple output dimensions in large-scale regression problems.
method Employing a mixture of single-output Gaussian process experts encoded via a probabilistic circuit.
result The method can capture correlations between output dimensions and often outperforms other approaches.
Proposes a flexible MGP model for dynamic, sparse correlations.
problem Handling dynamic and sparse correlations in multivariate data.
method Non-stationary MGP with dynamic spike-and-slab prior and EM algorithm.
result Captures dynamic and sparse correlations effectively.
CNN detects multipath in GNSS signals using correlator output.
problem Multipath contamination errors in GNSS signals.
method Deep Convolutional Neural Networks (CNN) applied to GNSS correlator output.
result CNN accurately detects multipath in GNSS signals.
Gradient boosting adapted for multi-label and multi-output tasks.
problem Joint prediction of multiple classification or regression outputs.
method Gradient tree boosting with random output projections.
result Random projection improves adaptation to different output correlation patterns.
LaCIM avoids spurious correlation by modeling latent causal factors.
problem Avoiding spurious correlation in supervised learning.
method Introducing latent variables for causal prediction and optimizing over latent space.
result Improved interpretability, robustness, and prediction power on OOD scenarios.
The paper finds a surprising positive correlation between upstreamness and downstreamness in global value chains.
problem The puzzling positive correlation between upstreamness and downstreamness in industries and countries.
method Analysis of a simple model of random Input/Output tables and experiments on empirical data.
result Upstreamness and downstreamness of the same industrial sector/country are positively correlated with a slope close to +1.
Data mining techniques on the biological analysis are spreading for most of the areas including the health care and medical information. We have applied the data mining techniques, such as KNN, SVM, MLP or decision trees over a unique dataset, which is collected from 16,380 analysis results for a year. Furthermore we h…
Automates machine learning of correlations between knot invariants.
problem Discovering and validating new relationships between knot invariants.
method Trained a neural network on 200,000 sets of knot invariants to predict an output invariant.
result Found novel correlations not explained by known results in knot theory.
Wide Boosting improves GB's performance on multivariate output tasks.
problem Lack of flexibility in fitting probabilistic multi-dimensional outputs.
method Inserts matrix multiplication between GB output and loss function.
result Wide Boosting outperforms Gradient Boosting on multivariate output tasks.
This paper introduces a multi-output Gaussian process for censored data.
problem Modeling bias in censored data using correlations between multiple outputs.
method Heteroscedastic multi-output Gaussian process with input-dependent noise and variational inference.
result The model better estimates the true process under complex censoring dynamics.
New kernel models multi-output Gaussian processes accurately.
problem Challenges in modelling cross-covariances for multiple-output Gaussian processes.
method Replaced Gaussian components with block components of finite bandwidth in spectral mixture kernel.
result First multi-output generalization of spectral mixture kernel that can approximate any stationary multi-output kernel to arbitrary precision.
We examine Deep Canonically Correlated LSTMs as a way to learn nonlinear transformations of variable length sequences and embed them into a correlated, fixed dimensional space. We use LSTMs to transform multi-view time-series data non-linearly while learning temporal relationships within the data. We then perform corre…
We propose a family of multivariate Gaussian process models for correlated outputs, based on assuming that the likelihood function takes the generic form of the multivariate exponential family distribution (EFD). We denote this model as a multivariate generalized Gaussian process model, and derive Taylor and Laplace al…
We investigate how simultaneously recorded long-range power-law correlated multi-variate signals cross-correlate. To this end we introduce a two-component ARFIMA stochastic process and a two-component FIARCH process to generate coupled fractal signals with long-range power-law correlations which are at the same time lo…
Non-symmetric rectangular correlation matrices occur in many problems in economics. We test the method of extracting statistically meaningful correlations between input and output variables of large dimensionality and build a toy model for artificially included correlations in large random time series.The results are t…
Enhances sensitivity analysis for correlated inputs.
problem Estimating sensitivity indices in models with correlated inputs.
method Proposes an extension of Sobol' estimator using a linear correlation model.
result Improves accuracy in variance-based sensitivity analysis.
Novel beamforming method reduces errors in wireless networks.
problem Mitigating channel errors in wireless networks with relays.
method Low-rank and cross-correlation techniques for robust distributed beamforming.
result The proposed LRCC-RDB technique significantly improves SINR performance.
In this paper, we focus on weakly supervised learning with noisy training data for both classification and regression problems.We assume that the training outputs are collected from a mixture of a target and correlated noise distributions.Our proposed method simultaneously estimates the target distribution and the qual…
New neural process models produce correlated predictions for better estimation tasks.
problem Need for models that can handle correlated predictions for tasks like weather forecasting.
method Developed new Neural Process models that can produce correlated predictions and support exact maximum likelihood training.
result Improved predictive performance on various experiments with synthetic and real data.
Paper proposes active learning for structured output design, improving Gaussian process model predictions.
problem Finding optimal input parameters for achieving desired structured outputs.
method Developed new acquisition functions to minimize prediction error of Gaussian process model, incorporating output correlations.
result Effectiveness demonstrated in synthetic and real data experiments, including materials informatics.
New neural network captures spatial correlations in wind speed predictions.
problem Uncertainty quantification in neural network predictions for high-dimensional, correlated data.
method Training neural networks with multidimensional Gaussian loss, preserving spatial correlation and computational tractability.
result Demonstrated super-resolution of surface wind speed with explicit correlation modeling.
A simple method reduces bias in LLM auto-evaluators by controlling output length.
problem Bias in LLM auto-annotators, particularly length bias.
method Simple regression analysis to control for length difference and other mediators.
result Improved robustness and increased correlation with human preferences.
In this study, the fluctuation-dissipation theory is invoked to shed light on input-output interindustrial relations at a macroscopic level by its application to IIP (indices of industrial production) data for Japan. Statistical noise arising from finiteness of the time series data is carefully removed by making use of…
Much recent machine learning research has been directed towards leveraging shared statistics among labels, instances and data views, commonly referred to as multi-label, multi-instance and multi-view learning. The underlying premises are that there exist correlations among input parts and among output targets, and the …
Gaussian processes (GPs), or distributions over arbitrary functions in a continuous domain, can be generalized to the multi-output case: a linear model of coregionalization (LMC) is one approach. LMCs estimate and exploit correlations across the multiple outputs. While model estimation can be performed efficiently for …
Bayesian Optimization improved for high-dimensional outputs.
problem Optimizing many correlated outcomes efficiently.
method Efficient multi-task Gaussian Process sampling.
result Substantial improvements in sample efficiency.
Biological neural network mimics CCA for multi-channel data.
problem Implementing CCA in a biologically plausible neural network.
method Derive an online CCA algorithm with local synaptic updates for multi-compartmental neurons.
result The derived neural network architecture and synaptic updates resemble cortical pyramidal neuron behavior.
ExCIR provides efficient, consistent, and scalable explainability for complex models.
problem Complex models lack transparency and require efficient, stable, and scalable explainability methods.
method ExCIR uses correlation-aware feature attribution with robust centering and groupwise aggregation.
result ExCIR delivers trustworthy agreement with global baselines and full model rankings, reduces runtime, and scales to large datasets.
New model predicts multiple outputs with missing labels.
problem Missing group labels in multi-output regression.
method Weakly-supervised multi-output model using correlated Gaussian processes.
result Model excels in multi-output settings with missing labels.
Safe active learning for multi-output Gaussian processes reduces data acquisition costs and ensures safety.
problem Expensive data acquisition and safety concerns in multi-output regression problems.
method Proposes a safe active learning approach considering data informativeness and safety constraints.
result Improved convergence compared to competitors on simulated and real-world datasets.
Bayesian model fuses multiple classifiers with explicit correlation modeling.
problem Combining outputs of multiple classifiers with explicit correlation.
method Hierarchical Bayesian model with correlated Dirichlet distribution.
result Fused classifier performance can be Bayes optimal even for highly correlated base classifiers.
We study the problem of structured output learning from a regression perspective. We first provide a general formulation of the kernel dependency estimation (KDE) problem using operator-valued kernels. We show that some of the existing formulations of this problem are special cases of our framework. We then propose a c…
We present a general method to detect and extract from a finite time sample statistically meaningful correlations between input and output variables of large dimensionality. Our central result is derived from the theory of free random matrices, and gives an explicit expression for the interval where singular values are…
A new method for real-time CCA on streaming data.
problem Finding correlated features in online data streams.
method Sliding Window Informative Canonical Correlation Analysis (SWICCA) using streaming PCA.
result SWICCA provides real-time CCA components in high dimensions with theoretical guarantees.
Stochastic neural networks can approximate any function, even with correlated outputs.
problem Approximating functions with stochastic outputs and correlations.
method Investigating deep sigmoid belief networks to approximate any stochastic mapping.
result Minimal number of layers and units needed for approximation.
A new method detects hallucinations in medical image restoration using Fourier Ring Correlation.
problem Detecting hallucinations in DL outputs for medical image restoration.
method sFRC (s(c)can FRC) over small patches of DL outputs and reference images.
result sFRC effectively detects hallucinations in CT and MRI restoration problems.
Fine-tuning improves information conveyance in language models by reorganizing uncertainty into more informative sequences.
problem Uncertainty reduction in large language models through fine-tuning is not fully understood, especially regarding output length.
method Proposed Canopy Entropy (CE⋆) to measure uncertainty in both output length and sequence, capturing total Shannon entropy. result Fine-tuned models exhibit stronger positive correlation between entropy rate and semantic diversity, indicating more informative and semantically meaningful generations.
Recently there has been an increasing interest in methods that deal with multiple outputs. This has been motivated partly by frameworks like multitask learning, multisensor networks or structured output data. From a Gaussian processes perspective, the problem reduces to specifying an appropriate covariance function tha…
The paper introduces a method to decorrelate circular coordinates using lattice reduction.
problem Geometric correlation between circle-valued maps when multiple cohomology classes are used.
method Systematic procedure using the Lenstra--Lenstra--Lovász algorithm for constructing low energy torus-valued maps.
result A method to obtain less correlated maps from cohomology classes using integer linear combinations.
We consider the problem of learning a structured multi-task regression, where the output consists of multiple responses that are related by a graph and the correlated response variables are dependent on the common inputs in a sparse but synergistic manner. Previous methods such as l1/l2-regularized multi-task regressio…
Mutually interacting components form complex systems and the outputs of these components are usually long-range cross-correlated. Using wavelet leaders, we propose a method of characterizing the joint multifractal nature of these long-range cross correlations, a method we call joint multifractal analysis based on wavel…
Improved multivariate conformal prediction by standardizing residuals.
problem Weak conditional coverage in heteroskedastic multivariate settings.
method Natural extension of univariate normalization to multivariate setting, whitening residuals and standardizing local variance.
result Standardized residuals yield asymptotic conditional coverage under certain distributions.
Sig-PCA integrates model outputs and observations to correct model biases.
problem Improving model accuracy and reliability by correcting biases and numerical approximations.
method Sig-PCA framework that combines summary statistics from model outputs with localized observations via a neural network.
result Corrects model outputs to align closely with observational data, preserving essential statistical information.
Interpreting the prediction mechanism of complex models is currently one of the most important tasks in the machine learning field, especially with layered neural networks, which have achieved high predictive performance with various practical data sets. To reveal the global structure of a trained neural network in an …
New framework for inference with LAR, explaining variable contributions and providing stopping rules.
problem LAR's lack of well-understood termination point and basic behavioral properties.
method Developed a novel framework for inference with LAR, providing new mathematical properties and stopping rules.
result LAR estimates of non-zero population correlations have independent normal distributions for inference, and zero-valued correlations have a non-normal joint distribution.
New rationalization method avoids spurious correlations.
problem Spurious correlations in rationalization criteria.
method Invariant rationalization using game theory constraints.
result Rationales generalize better and align with human judgments.
This work simplifies Gaussian process regression for multiple outputs.
problem Exponential computational complexity in Gaussian process regression.
method Approximating the covariance kernel using eigenvalues and functions.
result Significant reduction in training and regression complexity.