This study identifies financial risk paths in digital-transformed enterprises.
problem Identifying financial risks in digital-transformed enterprises.
method DEMATEL-ISM-MICMAC method.
result Political and economic environment affects enterprise's financial structure.
A new kernel improves Volterra series model selection and prediction.
problem Hard identification of Volterra series from limited data.
method Proposes a novel regularization network using a multiplicative polynomial kernel.
result Better selection of influential monomials improves model prediction.
The paper addresses causal mediation analysis with post-treatment events, proposing robust estimators and efficient methods.
problem Assessing causal mediation in the presence of post-treatment events like noncompliance or clinical events.
method Identifies natural mediation effects for entire populations and principal strata, derives efficient influence functions, and proposes multiply robust estimators.
result Multiply robust estimators are consistent under four types of misspecifications and efficient when all models are correct.
ECGID research focuses on rest but not exercise, this study evaluates both.
problem ECGID performance under exercise is insufficiently studied.
method Various learning methods applied to ECG dataset.
result Current ECGID methods fail under exercise conditions.
ASTPN improves video-based person re-identification by jointly attending to spatial and temporal features.
problem Video-based person re-identification in surveillance and HCI.
method Joint Spatial and Temporal Attention Pooling Network (ASTPN).
result ASTPN outperforms state-of-the-art methods on multiple datasets.
This study improves UAV identification using RF signals with one-shot generative methods.
problem Limited RF environments and signal variability make traditional RF identification ineffective.
method Introduces one-shot generative methods to augment RF signals for UAV identification.
result One-shot generative methods outperform traditional methods in low-data regimes.
The paper analyzes methods to identify influential data points in deep models.
problem Interpreting deep learning models and debugging datasets.
method Curated experiments to analyze influence of data points on classifiers.
result Training loss-based sample selection outperformed other methods in detecting mislabels.
New methods for estimating causal effects in hidden variable DAGs.
problem Estimating causal effects in models with hidden variables.
method Influence function based estimators for causal effects in hidden variable DAGs.
result Achieves semiparametric efficiency bounds for identifiable effects.
New method reveals how training data influence diffusion model outputs.
problem Difficulty in assessing training data impact on diffusion model outputs.
method Use of ensembles trained on carefully engineered splits of training data to identify influential training examples.
result Demonstrated the viability of ensembles as generative models and validity of assessing influence.
The paper proves ML estimators are strongly consistent for identifying edge weights in BAR models.
problem Identifying edge weights in Bernoulli Autoregressive (BAR) models.
method Maximum Likelihood (ML) estimation for two variants of BAR models.
result ML estimators are strongly consistent for edge weight identification.
GGDA simplifies DA for large models, speeding up attribution by up to 50x.
problem Computational intensity of existing DA methods limits their applicability to large-scale models.
method Generalized Group Data Attribution (GGDA) framework attributing to groups of training points.
result GGDA achieves up to 50x speedups over standard DA methods while maintaining effectiveness.
Study identifies influences in VAR models with latent processes.
problem Identify influences among observed and latent processes in VAR models.
method Identify support of transition matrix and lengths of latent paths.
result Support of transition matrix and lengths of latent paths can be identified successfully under certain conditions.
A new encoding framework predicts brain activity from visual stimuli and intrinsic brain connections.
problem Traditional encoding models ignore brain inner states, limiting their performance in natural image identification.
method Proposes a novel encoding framework combining external stimuli and brain inner states, using a forward encoding model and an inner state model.
result The framework achieves better performance on natural image identification from fMRI responses than traditional models.
The paper identifies causal effects in latent variable models using higher-order cumulants.
problem Challenges in identifying causal effects in latent variable models with latent confounders.
method Using higher-order cumulants, the paper addresses two challenging setups: a single proxy variable and underspecified instrumental variables.
result Causal effects are identifiable with a single proxy or instrument.
Efficiently identifies best algorithms for game tasks.
problem Selecting optimal algorithms for game tasks efficiently.
method Best arm identification for multi-armed bandits with confidence intervals.
result Significantly improved performance in simple regret and error probability.
Framework ranks sectors influenced by Indian Union Budgets.
problem Real-time analysis of budgetary impacts on sector-specific equity performance.
method Fine-tuned embeddings and language models for sector identification and performance ranking.
result 0.997 NDCG score in predicting sector ranks based on post-budget performances.
Introduces a new feature importance measure using Gram-Schmidt decorrelation.
problem Determining feature influence strength and nature in datasets.
method Gram-Schmidt decorrelation and random forest regression.
result Empirical comparison of new estimators with established methods.
Generates realistic person images for re-id, overcoming pose variations.
problem Lack of cross-view paired training data and pose variations in person re-identification.
method Pose-normalization GAN (PN-GAN) for generating images conditioned on pose.
result Synthesized images enable learning invariant features free of pose variations.
New method uses neural networks to identify sources from limited data in complex systems.
problem Identifying sources from noisy and limited data in high-dimensional systems.
method Calibrating deep neural network surrogates to ensemble simulations and using Bayesian optimization for source identification.
result Reliable source identification with uncertainty quantification using limited data and auxiliary processes.
Develops methods to identify and estimate causal effects with instrumental variables.
problem Causal inference with confounded treatment assignment and unobserved variables.
method General nonparametric causal framework, debiased machine learning, semiparametric theory.
result Consistent and asymptotically normal estimators for average treatment effect.
A novel hybrid method identifies unknown sources of advection-diffusion transport.
problem Identifying multiple sources with unknown number and properties in advection-diffusion systems.
method Hybrid method combining NMF decomposition and Green function inverse analysis.
result Successfully identifies advection velocity, dispersivity, and source characteristics from mixed data.
Develops methods to estimate causal effects using instrumental variables.
problem Estimating causal effects from observational data with confounders.
method General Control Function (GCF) method, variational decoupling (VDE), semi-supervised GCF.
result General control functions can be constructed to estimate causal effects without strong assumptions.
Breath sounds can identify speakers with high accuracy.
problem Identifying speakers from breath sounds.
method Examined breath sounds during continuous speech, focusing on inhalation phase.
result Breath sounds carry unique speaker-specific information, enabling accurate speaker identification.
New framework IDOL identifies latent causal processes with instantaneous relations from time series data.
problem Identifying latent causal processes with instantaneous relations from time series data.
method Sparse influence constraint and variational inference architecture with sparsity regularization.
result Our method can identify latent causal processes with instantaneous relations.
New RL method handles hidden actions in offline learning.
problem Learning from unseen actions in real-world RL datasets.
method LURE (Learning from the Unseen: Robust Estimator) method using next-state variable as proxy.
result Valid statistical inference and improved RL conclusions with hidden actions.
Recursive neural networks improve quark/gluon tagging performance.
problem Improving quark/gluon tagging accuracy using machine learning.
method Recursive neural networks (RecNNs) that embed jet clustering history recursively.
result RecNNs outperform traditional boosted decision tree (BDT) by a few percent in gluon rejection rate.
Recent developments in system identification have brought attention to regularized kernel-based methods, where, adopting the recently introduced stable spline kernel, prior information on the unknown process is enforced. This reduces the variance of the estimates and thus makes kernel-based methods particularly attract…
A widely applied approach to causal inference from a non-experimental time series X X X , often referred to as "(linear) Granger causal analysis", is to regress present on past and interpret the regression matrix B ^ \hat{B} B ^ causally. However, if there is an unmeasured time series Z Z Z that influences X X X , then this approach…
Method uses KL-divergence to explain deep image classification.
problem Understanding deep neural network predictions.
method KL-divergence to focus on relevant pixels.
result Improves interpretation of deep network predictions.
Study uses ANN with LM for ASD screening.
problem Lack of kinematic measures for ASD diagnosis.
method Artificial Neural Network (ANN) with Levenberg-Marquardt algorithm.
result Predictive accuracy of ANN for ASD detection.
Extends causal inference to hidden mediators with proxies.
problem Identifying causal effects with hidden mediators and error-prone proxies.
method Established causal hidden mediation analysis and hidden front-door criterion.
result Identification of population intervention indirect effect possible with hidden mediators.
New method disentangles latent variables in nonstationary data.
problem Disentangling latent variables in nonstationary sequential data.
method NCTRL framework exploiting Markov assumption and temporal structure.
result Independent latent components can be recovered from nonlinear mixture without auxiliary variables.
Proximal Mediation Analysis with Hidden Recanting Witnesses
problem Identifying path-specific effects in mediation analysis when recanting witnesses are unknown
method Proximal causal inference and semiparametric inference framework
result Developed three novel identification strategies and a semiparametric inference framework
New methods estimate causal effects through mediators, handling confounding without strict assumptions.
problem Estimating causal effects through mediators while accounting for unmeasured confounding.
method Developed four nonparametric identification strategies using proximal confounding bridge functions, efficient influence function, and quadruply robust estimator. Proposed proximal debiased machine learning approach for high-dimensional nuisance parameters.
result Achieved n \sqrt{n} n -consistency and asymptotic normality for path-specific effect estimation. Develops tools to decompose spurious variations in causal models.
problem Understanding and decomposing spurious variations in causal relationships.
method Formal tools for decomposing spurious effects in Markovian and Semi-Markovian models.
result First results on non-parametric decomposition of spurious effects and sufficient conditions for identification.
This study uses complex networks to analyze influential spreaders and their effects on different market sectors.
problem Existing methods failed to distinguish between positive and negative influences of market sectors.
method LIEST (Local Influential Effects for Specific Target) method using complex network analysis.
result LIEST effectively distinguishes positive and negative influences of market sectors during different periods.
CRNN improves artist classification with temporal audio features.
problem Artist classification using deep learning with temporal structure.
method Convolutional Recurrent Neural Network (CRNN) applied to music artist identification dataset.
result Best model achieves an average F1 score of 0.937.
Study uses three sources to evaluate language models fairly.
problem Bias in offline model evaluation due to confounded model choice.
method Combines observational logs, randomized experiments, and simulators.
result Randomized experiment and simulator together recover causal model values.
A technique to quickly fix mistakes in neural networks.
problem Fixing model errors in neural networks quickly and without affecting other samples.
method Editable Training, a model-agnostic training technique.
result Effectiveness demonstrated on large-scale image classification and machine translation tasks.
This work aims mainly to present a project of research about the identification of the determinants that affect the mobility of labor. The empirical part of the work will be performed for the NUTS II and NUTS III of Portugal, from 1996 to 2002 and for 1991 and 2001, respectively (given the availability of statistical d…
Algorithm detects influential observations in high-dimensional data.
problem Challenges in identifying influential observations in high-dimensional datasets.
method Three-step algorithm based on expectiles and asymmetric correlations.
result Higher detection power than competing methods.
Study optimizes identifying the best arm with fixed rounds and Gaussian outcomes.
problem Designing efficient experiments to identify the best arm with fixed rounds and Gaussian outcomes.
method Developed worst-case lower bounds and the GNA-EBA strategy for optimal identification.
result GNA-EBA strategy is asymptotically worst-case optimal.
A new causal graph framework identifies treatment effects without adjusting for confounders.
problem Invalid identification of causal effects due to unmeasured confounders.
method Developed the Napkin graph to identify causal effects through a ratio of g-formulas, using influence-function-based estimators.
result Demonstrated substantial efficiency gains in estimating causal effects using the Napkin graph.
DSSM separates domain-invariant dynamics from domain-specifics in sequential data.
problem Learning cross-domain sequence representations from diverse data domains.
method Introduce disentangled state space models (DSSM) using unsupervised VAE-based training.
result Improves knowledge transfer and robust prediction across domains.
Word representations induced from models with discrete latent variables (e.g.\ HMMs) have been shown to be beneficial in many NLP applications. In this work, we exploit labeled syntactic dependency trees and formalize the induction problem as unsupervised learning of tree-structured hidden Markov models. Syntactic func…
The paper detects and identifies bias in data using a counterfactual approach.
problem Detecting and identifying bias in data, especially in medical image classification.
method A global explanation framework using the counterfactual approach to identify bias causing artifacts.
result Black frames significantly influence Convolutional Neural Network's prediction, changing benign to malignant.
A vast amount of textual web streams is influenced by events or phenomena emerging in the real world. The social web forms an excellent modern paradigm, where unstructured user generated content is published on a regular basis and in most occasions is freely distributed. The present Ph.D. Thesis deals with the problem …
Given a set of experiments in which varying subsets of observed variables are subject to intervention, we consider the problem of identifiability of causal models exhibiting latent confounding. While identifiability is trivial when each experiment intervenes on a large number of variables, the situation is more complic…