Paper proposes BCNMCC for system identification with noisy input.
problem System identification with noisy input and impulsive output noise.
method Introduces BCV to NMCC algorithm to compensate for input noise bias.
result BCNMCC outperforms other algorithms in noisy input conditions.
New criterion ensures recovery of latent factors in NMF with mild conditions.
problem Identifying latent factors in nonnegative matrix factorization (NMF) under mild conditions.
method Proposed a new identification criterion based on the scatteredness of one factor's rows in the nonnegative orthant.
result Latent factors can be provably identified from the NMF model with minimal structural assumptions.
FDR criterion simplifies complex causal graphs to a standard front-door setting.
problem Complex causal graphs make identification of causal effects difficult and computationally infeasible.
method Front-door reducibility (FDR) criterion and FDR-TID algorithm.
result Many graphs can be simplified to a standard front-door setting, making causal effect identification simpler and more interpretable.
Study identifies parameters in causal models with latent confounding.
problem Parameter identification in linear non-Gaussian causal models with latent confounding.
method Graphical criterion for necessary and sufficient identifiability of direct causal effects, with polynomial-time algorithm.
result Developed a graphical criterion for identifying direct causal effects in latent variable models with arbitrary non-linear confounding.
Develops a method to identify causal effects in linear models with latent variables.
problem Identifying causal effects in models with latent variables that are not independent.
method A novel graphical criterion and an integer linear program algorithm.
result Sufficient condition for identifying causal effects by rational formulas in the covariance matrix.
Unified kernel-based methods improve nonlinear causal discovery.
problem Identifying nonlinear causal relationships between time series variables.
method Unified Kernel Principal Component Regression (KPCR) and Gaussian Process score-based model with Smooth Information Criterion.
result Improved performance in time series nonlinear causal discovery.
This paper presents the first theoretical results showing that stable identification of overcomplete μ μ μ -coherent dictionaries Φ ∈ R d × K Φ\in \mathbb{R}^{d\times K} Φ ∈ R d × K is locally possible from training signals with sparsity levels S S S up to the order O ( μ − 2 ) O(μ^{-2}) O ( μ − 2 ) and signal to noise ratios up to O ( d ) O(\sqrt{d}) O ( d ) . In particular the di…
IIC decouples causal identification into two phases, significantly reducing the HTC gap in linear SEMs.
problem Determining causal effect coefficients in linear SEMs with latent confounders using the Half-Trek Criterion (HTC) leaves a gap of inconclusive causal effects.
method Iterative Identification Closure (IIC) framework that decouples causal identification into two phases: a seed function S_0 and Reduced HTC propagation.
result IIC strictly subsumes both HTC and ancestor decomposition, reducing the HTC gap by over 80% with combined seeds.
A new filter design improves system identification accuracy.
problem Improving system identification accuracy for various system types.
method Generalized proportionate-type normalized subband adaptive filter (GPtNSAF) using least squares on subband errors with a sparsity penalty.
result GPtNSAF benefits from increasing subbands more than sparsity for quasi-sparse or dispersive systems, and both aspects are complementary for sparse systems.
A new clustering method for vector time series using autoregressive dynamics.
problem Clustering of vector time series based on their dynamics is challenging.
method System identification approach using mixture autoregressive models.
result Developed a computationally manageable algorithm k-LMVAR for clustering vector time series.
Researchers identify latent variables and causal structures from nonlinear hierarchical models.
problem Challenging task of identifying latent variables and causal structures from observational data, especially when relationships are nonlinear.
method Investigated nonlinear latent hierarchical causal models, developed identification criterion, and constructed an estimation procedure.
result Identifiability of causal structures and latent variables achieved under mild assumptions.
New methods estimate causal effects using front-door criterion in presence of unmeasured confounders.
problem Estimating causal effects in observational studies with unmeasured confounders.
method Developed novel one-step and targeted minimum loss-based estimators for front-door assumptions.
result Established conditions for root-n consistency and asymptotic linearity.
New infill criterion identifies local optima in multimodal models.
problem Identify challenging test scenarios for physical systems.
method Model-based optimization with efficient global optimization and infill criterion.
result New infill criterion outperforms existing methods in identifying local optima.
We introduce a new family of graphical models that consists of graphs with possibly directed, undirected and bidirected edges but without directed cycles. We show that these models are suitable for representing causal models with additive error terms. We provide a set of sufficient graphical criteria for the identifica…
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.
In this paper we develop a method for learning nonlinear systems with multiple outputs and inputs. We begin by modelling the errors of a nominal predictor of the system using a latent variable framework. Then using the maximum likelihood principle we derive a criterion for learning the model. The resulting optimization…
V-SysId identifies keypoints and 3D system from unlabeled videos.
problem Identifying keypoints and 3D system from unlabeled videos.
method Alternates between parameter estimation and extrinsic camera calibration, using motion equations as weak supervision.
result Utility of the approach demonstrated across various settings.
New method identifies latent components in PNL mixtures without strong assumptions.
problem Identifying latent components in PNL mixtures under unknown nonlinear functions.
method Carefully designed UML criterion to identify a null space associated with the mixing system.
result Identification/removal of unknown nonlinearity under minimal conditions.
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.
This work compares data reduction criteria for online Gaussian Processes.
problem The computational complexity of Gaussian Processes limits their applicability to small datasets and streaming scenarios.
method Unified comparison of several data reduction criteria, analyzing computational complexity and reduction behavior.
result Practical guidelines for choosing a suitable data reduction criterion for online Gaussian Processes.
New algorithms for best arm identification in bandits robust to misspecified parameters.
problem Inconsistent learning performance of traditional MAB algorithms when parameters are misspecified.
method Proposes two classes of asymptotically near-optimal algorithms for statistically robust MAB under fixed-budget pure exploration.
result Establishes fundamental performance limits and proposes algorithms that are asymptotically near-optimal.
SEM-DNN learns reciprocal interactions from observational data without external instruments.
problem Estimating bidirectional interactions from endogenous data.
method Heteroscedastic neural simultaneous-equation estimator (SEM-DNN) that learns reciprocal structural interactions.
result SEM-DNN recovers structural effects more reliably than other methods under increasing information.
Paper analyzes and improves GPSP algorithm for block sparse signal recovery.
problem Recovering block sparse signals from noisy data.
method Group Projected Subspace Pursuit (GPSP) with convergence analysis and feature selection criteria.
result GPSP exactly recovers true block sparse signals under certain conditions.
Modeling air pollutants using data-driven techniques and sparse identification of nonlinear dynamics.
problem Predicting concentrations of air pollutants using hidden physical laws.
method Sparse identification of nonlinear dynamics (SINDy) for parsimonious systems of ordinary differential equations.
result More than half of the critical points are saddle points, indicating system instability.
Novel model selection method outperforms current state-of-the-art in high-dimensional graphical models.
problem Accurate model selection in high-dimensional graphical models.
method Graphical Neighbour Information (GNI) criterion.
result Demonstrates oracle performance in high-dimensional model selection, outperforming current methods.
Two operators are equivalent in geometric scattering theory under certain conditions.
problem Equivalence of identification operators in geometric scattering theory.
method Proving a criterion for the equality of two wave operators using asymptotic equivalence of operators.
result Equality of wave operators under specific conditions in geometric settings.
This study evaluates and compares novelty detection algorithms for discrete sequences.
problem Identifying anomalies in temporal data.
method Experimental comparison of state-of-the-art novelty detection methods on various public and industrial datasets.
result Recommendations for efficient and appropriate methods based on extensive experiments and scalability tests.
This work addresses identifiability of nonlinear mixtures and proposes a practical algorithm.
problem Identifiability of nonlinear mixtures under unknown nonlinear distortions.
method Identification criterion based on neural network and effective learning algorithm.
result Proposes a practical method for identifying nonlinear mixtures with identifiability guarantees.
We extend causal calculus to models with cycles, latent confounders, and selection bias.
problem Causal reasoning in the presence of cycles, latent confounders, and selection bias.
method Prove rules of causal calculus for i/o structural causal models, generalize adjustment criteria, and extend ID algorithm.
result Enable causal reasoning in complex models with cycles, latent confounders, and selection bias.
Improved sample complexity for identifying best policies in risk-sensitive reinforcement learning.
problem Identifying approximately optimal policies in risk-sensitive reinforcement learning with exponential horizon dependence.
method Forward-model based algorithm with KL-based exploration bonuses adapted for entropic criterion, leveraging smoothness properties of exponential utility and a new stopping rule.
result Achieved sample complexity matching the lower bound, closing the gap between upper and lower bounds.
New method targets relative risk heterogeneity in clinical trials.
problem Identifying treatment effects across subgroups with absolute risk differences.
method Modified causal forests using a novel node-splitting procedure based on relative risk.
result Relative risk causal forests can capture heterogeneity not detected by absolute risk methods.
Unified framework for estimating indirect effects in observational studies with unmeasured confounding.
problem Challenges in evaluating indirect effects due to unmeasured confounding and unethical exposures.
method Developed a unified identification and estimation framework using proximal causal inference.
result Unified identification and estimation of PIIE and causal effect of an intervening variable in settings with pervasive unmeasured confounding.
A novel criterion selects optimal distance metrics for cell profile analysis.
problem Determining the most accurate distance metric for high-dimensional cell profiles.
method Generalized proposition and corollaries to evaluate and select distance metrics.
result Wasserstein and cosine similarity metrics are optimal for general cases.
Optimal strategy identified for minimizing regret in fixed-budget best arm selection.
problem Minimizing expected simple regret in fixed-budget best arm selection.
method Two-Stage (TS)-Hirano-Imbens-Ridder (HIR) strategy using HIR estimator.
result TS-HIR strategy is asymptotically minimax optimal.
New algorithm identifies dominant arm with high probability.
problem Identifying the arm with the highest realized reward in multi-armed bandits.
method Dominance score criterion and joint mixing and recycling mechanism.
result Identifies the best dominant arm with nearly optimal sample complexity.
The paper derives CVaR concentration bounds for light-tailed and heavy-tailed distributions.
problem Estimating CVaR for light-tailed and heavy-tailed distributions.
method Derives concentration bounds for CVaR using empirical and truncation-based estimators.
result Concentration bounds with exponential decay in sample size for both light-tailed and heavy-tailed distributions.
Improved Random Forests detect pure interactions better.
problem Random Forests struggle with certain pure interactions.
method Alternative partitioning schemes during tree construction.
result Improved Random Forests enhance fitting ability in scenarios with pure interactions.
Classifiers trained on data sets possessing an imbalanced class distribution are known to exhibit poor generalisation performance. This is known as the imbalanced learning problem. The problem becomes particularly acute when we consider incremental classifiers operating on imbalanced data streams, especially when the l…
Algorithm minimizes regret in multi-criteria bandits with constraints.
problem Optimize primary attribute while respecting secondary constraints.
method Con-LCB algorithm that guarantees logarithmic regret and feasibility identification.
result Logarithmic regret and feasibility identification with high probability.
Study compares variable selection methods for model evaluation and search.
problem Understanding underlying mechanisms in scientific questions through variable selection.
method Comprehensive comparison of BIC and AIC for model evaluation and various search methods (exhaustive, greedy, LASSO path, stochastic search) for model space exploration.
result Exhaustive search BIC and stochastic search BIC outperform other methods in small and large model spaces, respectively, improving correct identification rate and reducing false discovery rate.
Mining discriminative subgraph patterns from graph data has attracted great interest in recent years. It has a wide variety of applications in disease diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the graph representation alone. However, in many real-world applications, the side information …
Paper proposes a differentially private test for joint dependence among random vectors.
problem Detecting joint dependence among sensitive data while maintaining privacy.
method Differentially private permutation methodology for dHSIC test.
result Proposed test attains minimax optimal power across privacy regimes.
This tutorial introduces causal modeling methods for researchers.
problem Understanding causal relationships in research studies.
method Integrates potential outcomes and graphical methods for causal modeling.
result Clear notation and practical examples for applied researchers.
The problem of active diagnosis arises in several applications such as disease diagnosis, and fault diagnosis in computer networks, where the goal is to rapidly identify the binary states of a set of objects (e.g., faulty or working) by sequentially selecting, and observing, (noisy) responses to binary valued queries. …
The paper proposes a gradient-based method for multi-penalty Ridge regression.
problem Optimizing multiple regularization hyperparameters for linear regression.
method Gradient-based optimization through matrix differential calculus.
result The method outperforms traditional regularization techniques like LASSO and Ridge.
New method speeds up model selection for complex scientific tasks.
problem Exhaustive model selection is computationally infeasible for large model spaces.
method Branch-and-bound algorithm with non-monotonic criteria.
result Guaranteed identification of optimal models with significant computational speedups.
Paper explores using EEG for better speaker identification, even in noisy environments.
problem Speaker identification performance degrades in background noise.
method Uses EEG signals to enhance speaker identification systems, comparing with acoustic features.
result Speaker identification system using only EEG features outperforms one using only acoustic features in high background noise.
MOB-dS uses permutation to correct for dependency in discrete survival data.
problem Identifying subgroups in discrete event time data with potential spurious results.
method Model-based recursive partitioning (MOB) with modified data matrix and permutation test.
result MOB-dS controls type I error rate better than standard MOB for discrete survival data.