Interpretable text-response modelling for structured outcomes
problem Predicting structured responses alongside textual data
method Joint non-negative matrix factorisation and binomial regression
result Recovering stable response-relevant textual signals
Motivation: Analysis of relationships of drug structure to biological response is key to understanding off-target and unexpected drug effects, and for developing hypotheses on how to tailor drug thera-pies. New methods are required for integrated analyses of a large number of chemical features of drugs against the corr…
We consider the problem of estimating a sparse multi-response regression function, with an application to expression quantitative trait locus (eQTL) mapping, where the goal is to discover genetic variations that influence gene-expression levels. In particular, we investigate a shrinkage technique capable of capturing a…
New method for estimating and testing impulse responses in high-dimensional VAR systems.
problem Statistical inference for impulse responses in sparse, high-dimensional vector autoregressions.
method Local projection equations and de-sparsified estimators combined with a non-regularized contemporaneous impact matrix.
result Valid inference procedures for structural impulse responses in high-dimensional systems.
Improved accuracy in dynamic response variation analysis using multi-fidelity data fusion.
problem Inefficient characterization of dynamic response variation due to limited high-fidelity data.
method Composite Neural Network fusion approach for multi-level, heterogeneous datasets.
result Improved accuracy in frequency response variation characterization.
We propose a method for estimating coefficients in multivariate regression when there is a clustering structure to the response variables. The proposed method includes a fusion penalty, to shrink the difference in fitted values from responses in the same cluster, and an L1 penalty for simultaneous variable selection an…
Paper presents a method for imputing and forecasting structural response from incomplete sensor data.
problem Missing sensor data in structural health monitoring (SHM).
method Incremental Bayesian tensor learning for spatiotemporal missing data reconstruction and forecasting.
result The proposed method achieves accurate and robust imputation and prediction even with high rates of missing data.
We consider learning high-dimensional multi-response linear models with structured parameters. By exploiting the noise correlations among responses, we propose an alternating estimation (AltEst) procedure to estimate the model parameters based on the generalized Dantzig selector. Under suitable sample size and resampli…
FEA-Net uses physics knowledge to predict material responses efficiently.
problem Predicting material mechanical responses accurately and efficiently.
method Physics-guided deep learning with FEA integration.
result FEA-Net accurately predicts mechanical responses under external loading.
Study develops efficient algorithm for probabilistic penetration response of composite plates.
problem Probabilistic modeling of discrete structural response, focusing on binary events like buckling.
method Adaptive domain-based decomposition, sparse grid sampling, assumption of monotonic behavior.
result Efficient computational framework for probabilistic penetration response of composite plates.
Non-negative matrix factorization models based on a hierarchical Gamma-Poisson structure capture user and item behavior effectively in extremely sparse data sets, making them the ideal choice for collaborative filtering applications. Hierarchical Poisson factorization (HPF) in particular has proved successful for scala…
New method handles correlated responses and interaction effects in multi-response regression.
problem Handling correlated responses and interaction effects in multi-response regression.
method MADMMplasso, an ADMM-based approach for multi-response regression with overlapping groups and interaction effects.
result The proposed method outperforms in prediction and variable selection for correlated responses and interaction effects.
PEER tackles multi-response regression with incomplete outcomes efficiently.
problem Challenges in estimating, predicting, and computing with large-scale multi-response regression and incomplete outcomes.
method PEER converts multi-response regression into parallel univariate-response regressions.
result PEER achieves consistency in estimation, prediction, and variable selection.
We consider multi-response and multitask regression models, where the parameter matrix to be estimated is expected to have an unknown grouping structure. The groupings can be along tasks, or features, or both, the last one indicating a bi-cluster or "checkerboard" structure. Discovering this grouping structure along wi…
Treatment effects can be estimated from observational data as the difference in potential outcomes. In this paper, we address the challenge of estimating the potential outcome when treatment-dose levels can vary continuously over time. Further, the outcome variable may not be measured at a regular frequency. Our propos…
Unified framework for binary responses using AUC loss and low-rank constraint.
problem Statistical inefficiency and shared structure in fitting multiple binary responses.
method Pairwise AUC loss aggregation with low-rank constraint, scalable projected gradient descent.
result Unified framework outperforms likelihood-based approaches in challenging settings.
The paper proposes a new method to evaluate LLM agent responses using ECDF clustering.
problem The standard evaluation of LLM agent responses via majority voting obscures response quality and distribution.
method The paper introduces a novel evaluation framework based on ECDF of cosine similarities and clustering of ECDFs using distances and k-medoids algorithm. result ECDF clustering reveals interpretable group structures in LLM responses, offering insights into agent settings.
We introduce thermodynamic response functions for singular Bayesian models.
problem Singular Bayesian models violate regular asymptotics due to non-identifiability and degenerate Fisher geometry.
method Posterior tempering induces thermodynamic response functions, linking WAIC, WBIC, and singular fluctuation.
result WAIC, WBIC, and singular fluctuation are unified within a thermodynamic response framework.
Proposes a method to identify subgroup structure and estimate covariate effects for multivariate response data.
problem Identifying subgroup structure and estimating covariate effects in multivariate response data.
method Joint heterogeneity and reduced-rank learning framework using rank-constrained pairwise fusion penalization.
result Established the asymptotic properties of the estimators and proposed a predictive information criterion for rank selection.
Approach uses machine learning to identify structural modal parameters from output-only data.
problem Identifying modal parameters from output-only data for structural health monitoring.
method Unsupervised learning using a self-coding deep neural network to separate modal responses from vibration data.
result The approach effectively identifies structural modal parameters from system responses.
Motivated by applications in neuroimaging analysis, we propose a new regression model, Sparse TensOr REsponse regression (STORE), with a tensor response and a vector predictor. STORE embeds two key sparse structures: element-wise sparsity and low-rankness. It can handle both a non-symmetric and a symmetric tensor respo…
Digital personas improve survey results for stable attributes but fail for subjective responses.
problem When can digital personas reliably approximate human survey findings?
method Using LISS panel, constructed personas from background variables and survey histories, tested against held-out post-cutoff answers.
result Digital personas improve alignment with human response distributions for stable attributes but fail for subjective responses.
A new model for latent class analysis with weighted responses.
problem Limitation of latent class model for real-world data with continuous or negative responses.
method Proposed a novel generative model, the weighted latent class model (WLCM).
result The proposed WLCM is more realistic and general than the latent class model.
Neural networks simplify uncertainty quantification of locally nonlinear systems.
problem Estimating statistics of responses in large-scale locally nonlinear dynamical systems.
method Decomposes response into nominal linear system and a neural network-estimated pseudoforce.
result Neural networks can efficiently estimate pseudoforce containing nonlinear and uncertain information.
The paper predicts responses on out-of-sample nodes using latent positions on unknown curves.
problem Predicting responses on out-of-sample nodes with latent positions on unknown curves.
method Manifold learning and graph embedding technique using latent positions.
result Convergence guarantees for predicting responses on out-of-sample nodes.
A new model estimates mixed memberships for categorical data with weighted responses.
problem Limited applicability of existing GoM model to weighted categorical data.
method Proposes Weighted Grade of Membership (WGoM) model, relaxing distribution constraints.
result WGoM can describe any response matrix with finite distinct elements.
A hybrid method combines model-based and data-driven approaches for multiscale constitutive responses.
problem High computational costs and inaccuracies in nonlinear multiscale methods.
method Hybrid methodology combining model-based constitutive laws, data-driven corrections, and computational multiscale approaches.
result Model-data-driven approach improves macroscale simulations with similar accuracy and computational cost.
Study finds anomalies in high-frequency S&P 500 price changes.
problem Anomalies in high-frequency S&P 500 price changes.
method Using NBBO event-time data, the study forms pairs of backward and forward price increments, standardizes them, and estimates expected responses on a fine grid of push magnitudes.
result Persistent structural shift in expected responses: near zero for short lags, pronounced tails for long lags, indicating correlation between larger historical pushes and nonzero responses.
DeepONets enhance spatial-temporal surrogates for structural dynamics.
problem Creating full spatial-temporal surrogates for dynamical systems under uncertainty.
method Proposed Full-Field Extended DeepONet (FExD) to learn full solution operator across multiple degrees of freedom.
result FExD achieves superior accuracy and computational efficiency compared to other models.
Hyperparameter optimization can be formulated as a bilevel optimization problem, where the optimal parameters on the training set depend on the hyperparameters. We aim to adapt regularization hyperparameters for neural networks by fitting compact approximations to the best-response function, which maps hyperparameters …
Improves regression efficiency by separating material and immaterial parts of responses.
problem Improving estimation efficiency in nonlinear multivariate regressions.
method Kernel envelope (KENV) estimator for nonparametric response envelopes in reproducing kernel Hilbert space.
result KENV achieves lower in-sample prediction risk than kernel ridge regression in non-trivial immaterial components.
The vast majority of current machine learning algorithms are designed to predict single responses or a vector of responses, yet many types of response are more naturally organized as matrices or higher-order tensor objects where characteristics are shared across modes. We present a new machine learning algorithm BaTFLE…
HierGP improves emulator efficiency for sparse, structured data.
problem Sparse, structured data in expensive simulations.
method Hierarchical shrinkage GP framework with cumulative shrinkage priors.
result HierGP identifies structured sparse features from limited data.
A new model for complex cells accounts for insensitivity to image shifts.
problem Complex cells' response to image shifts.
method Linear combination of Gaussian derivatives at a single position.
result Maximum response insensitive to small shifts of the image.
A central challenge in neuroscience is to understand neural computations and circuit mechanisms that underlie the encoding of ethologically relevant, natural stimuli. In multilayered neural circuits, nonlinear processes such as synaptic transmission and spiking dynamics present a significant obstacle to the creation of…
A new measure of dependence for various data types.
problem Measuring dependence in multivariate, functional, and structured data.
method Combines local normalization with RKHS flexibility.
result Validates the measure's properties and competitive performance.
Flexible models predict US Census survey response rates.
problem Predicting survey response rates in the US Census Bureau.
method Nonparametric additive models with structured interactions using ℓ0-based penalization.
result Models lead to predictions comparable to black-box methods but are interpretable.
CAG method predicts nonlinear solid mechanics responses in real-time with high accuracy and efficiency.
problem Real-time prediction of nonlinear solid mechanics responses.
method Clustering adaptive Gaussian process regression (CAG) method.
result Offers predictions within a second with high precision using only 20 samples.
Proposes SROF for row-wise fusion in federated learning for multivariate responses.
problem Heterogeneous client models with shared variable-level structure.
method Sparse Row-wise Fusion (SROF) regularizer and RowFed algorithm.
result Empirically shows consistent error reduction and stronger variable-level cluster recovery.
In the co-sparse analysis model a set of filters is applied to a signal out of the signal class of interest yielding sparse filter responses. As such, it may serve as a prior in inverse problems, or for structural analysis of signals that are known to belong to the signal class. The more the model is adapted to the cla…
New algorithm estimates eigenspace with faulty nodes, matching performance of existing methods.
problem Estimating eigenspace in distributed systems with node failures.
method Develops an eigenspace estimation algorithm for distributed environments with arbitrary node failures.
result Matches performance of existing non-robust estimator up to an additive error.
An efficient algorithm selects the correct number of latent dimensions in multidimensional probit models.
problem Determining the correct number of latent dimensions in multidimensional probit graded response models.
method Adaptive Bayesian dimension selection framework using cumulative ordered spike-and-slab (COSS) prior and Albert--Chib latent response augmentation.
result The proposed method accurately recovers latent structures and avoids repeated model fitting.
Researchers developed a generic model to account for structural variability in SHM.
problem Variability in natural frequency due to operational and environmental conditions limits SHM technologies.
method An overlapping mixture of Gaussian processes (OMGP) was used to generate a generic representation of normal condition.
result The OMGP model provided a generic representation (form) to characterise the normal condition of structures.
FPP creates interpretable 2D embeddings for high-dimensional data.
problem Discovering interpretable relationships in high-dimensional data.
method Function preserving projections (FPP) for scalable linear embeddings.
result FPP reveals non-linear patterns of user-selected response functions.
Computer simulations have become a popular tool of assessing complex skills such as problem-solving skills. Log files of computer-based items record the entire human-computer interactive processes for each respondent. The response processes are very diverse, noisy, and of nonstandard formats. Few generic methods have b…
There are non-vanishing price responses across different stocks in correlated financial markets. We further study this issue by performing different averages, which identify active and passive cross-responses. The two average cross-responses show different characteristic dependences on the time lag. The passive cross-r…
New Bayesian method for sparse multidimensional item response theory.
problem Sparse interpretable explanations for questionnaire data.
method Bayesian EM algorithm for sparse factor loadings.
result Reliable recovery of factor dimensionality and latent structure.
Language models exhibit low-rank structure, which can be used for generation.
problem Understanding the low-dimensional structure of large language models.
method Empirical demonstration and theoretical analysis of the approximate rank of language models' logits.
result Language models can generate responses using linear combinations of unrelated prompts.