Enhanced Bayesian target encoding uses sampling techniques to improve model performance.
problem Improving target encoding for better model performance in machine learning.
method Using sampling techniques in Bayesian target encoding to extract intra-category distribution information.
result Improves generalization and reduces target leakage in machine learning models.
We identify direct causes of a target variable from observational data without full DAG identifiability.
problem Learning direct causes of a target variable from observational data.
method Developed algorithms under relaxed identifiability assumptions for one environment without interventions.
result Identifiable set of direct causes from observational data under specific assumptions.
Proposes a new method for variable importance using targeted learning.
problem Uncertainty quantification in variable importance metrics.
method Employing the targeted learning (TL) framework for conditional permutation variable importance.
result Improved accuracy in finite sample contexts compared to traditional methods.
For recurrent neural networks trained on time series with target and exogenous variables, in addition to accurate prediction, it is also desired to provide interpretable insights into the data. In this paper, we explore the structure of LSTM recurrent neural networks to learn variable-wise hidden states, with the aim t…
CIB compresses variables causally, preserving key causal interactions.
problem Constructing causal variable abstractions in complex systems.
method Causal Information Bottleneck (CIB) method, extending IB to include causal structures.
result CIB produces causally interpretable abstractions that accurately capture causal relations.
New method detects causal relationships from noisy measurements.
problem Discover causal relationships from noisy, imperfect measurements.
method Transformed Independent Noise (TIN) condition and ordered group decomposition.
result Identifies causal graph structure without over-complete ICA.
Proposes a method to generate diverse translations by conditioning on target domain.
problem NMT models lack diversity in translations, even with search algorithms.
method Condition the decoder on a latent variable representing target domain, generated by a target encoder.
result Generated diverse translations without affecting performance or training time.
Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data. While emerging successes have been made, existing methods need to search a large space to distinguish direct causes from direct effects of a target variable \emph{T…
Method identifies unknown intervention targets in structural causal models from diverse data.
problem Identifying unknown intervention targets in structural causal models from heterogeneous data.
method Two-phase approach: first recovers exogenous noises, second matches with endogenous variables.
result Proposed method uniquely identifies intervention targets under causal sufficiency assumption.
Adapts to shifts in latent subgroup distributions without labeled target data.
problem Adapting to domain shifts when latent subgroup distributions differ.
method Uses concept and proxy variables from source domain, and unlabeled target data.
result Optimal target predictor can be identified and estimated.
The use of target networks has been a popular and key component of recent deep Q-learning algorithms for reinforcement learning, yet little is known from the theory side. In this work, we introduce a new family of target-based temporal difference (TD) learning algorithms and provide theoretical analysis on their conver…
In regression modelling approach, the main step is to fit the regression line as close as possible to the target variable. In this process most algorithms try to fit all of the data in a single line and hence fitting all parts of target variable in one go. It was observed that the error between predicted and target var…
The MAXENT principle helps merge datasets to infer causal effects.
problem Inferring causal effects from unobserved variables.
method Using the maximum entropy principle with causal sufficiency and faithfulness assumptions.
result Identifies causal edges among variables from merged datasets.
Recently there has been a significant interest in learning disentangled representations, as they promise increased interpretability, generalization to unseen scenarios and faster learning on downstream tasks. In this paper, we investigate the usefulness of different notions of disentanglement for improving the fairness…
Optimizes predictions for specific tasks using parametrized decision analysis.
problem Optimizing predictions for specific decision tasks of interest.
method Designs a class of parametrized actions for Bayesian decision analysis.
result Derives efficient and interpretable solutions for various action parametrizations and loss functions.
SNAP efficiently identifies causal effects without needing full graph learning.
problem Efficiently estimating causal effects on a subset of variables.
method Sequential Non-Ancestor Pruning (SNAP) framework.
result SNAP reduces independence tests and computation time without sacrificing causal effect estimations.
New estimators improve efficiency in two-phase designs with coarsened data.
problem Efficient estimation in two-phase designs with incomplete data.
method Developed new estimators within the TMLE framework.
result New estimators are asymptotically equivalent and more efficient.
Learning Markov blanket (MB) structures has proven useful in performing feature selection, learning Bayesian networks (BNs), and discovering causal relationships. We present a formula for efficiently determining the number of MB structures given a target variable and a set of other variables. As expected, the number of…
New algorithm combines Geostatistics and Quantile Random Forests for non-stationary spatial modelling.
problem Non-stationary spatial modelling with multiple secondary variables.
method Combines Geostatistics and Quantile Random Forests to estimate conditional distributions and simulate spatial data.
result Consistent results similar to geostatistical and Quantile Random Forests, allowing for embedding simpler interpolation techniques.
Local method identifies causal relations in Markov equivalent DAGs.
problem Identifying causal relations when multiple DAGs are Markov equivalent.
method Graphical condition and local criteria for identifying causal paths.
result Local learning algorithm efficiently identifies causal variables.
Motivation: Algorithms that discover variables which are causally related to a target may inform the design of experiments. With observational gene expression data, many methods discover causal variables by measuring each variable's degree of statistical dependence with the target using dependence measures (DMs). Howev…
New method infers causal factors from large-scale data without full graph reconstruction.
problem Inferring causal variables from large-scale systems without full causal graph reconstruction.
method Supervised learning on simulated data using a neural network and subsampled-ensemble inference.
result Efficiently identifies causal relationships in large-scale gene regulatory networks.
Novel framework for contextual anomaly detection models uncertainty.
problem Identifying anomalies in target variables influenced by contextual variables.
method Normalcy score (NS) framework using heteroscedastic Gaussian process regression.
result NS outperforms state-of-the-art methods in detection accuracy and interpretability.
Regularized target encoding beats traditional methods for high cardinality features in ML.
problem Efficiently encoding high cardinality categorical variables for ML algorithms.
method Regularized target encoding compared to traditional encodings like integer and one-hot encoding.
result Regularized target encoding consistently provided the best results in a large-scale benchmark experiment.
We describe a novel non-parametric statistical hypothesis test of relative dependence between a source variable and two candidate target variables. Such a test enables us to determine whether one source variable is significantly more dependent on a first target variable or a second. Dependence is measured via the Hilbe…
GraphTEE estimates treatment effects on graph-structured targets, mitigating bias.
problem Understanding treatment effects on graph-structured targets with observational bias.
method GraphTEE framework focusing on confounding variable sets and new regularization.
result GraphTEE mitigates bias better than previous methods.
SMART-FAN-Lasso fine-tunes neural networks for high-dimensional nonparametric regression.
problem Fine-tuning neural networks for high-dimensional nonparametric regression with variable selection.
method Source-model-augmented residual tuning (SMART) framework for neural Lasso.
result SMART-FAN-Lasso achieves statistical acceleration over single-task learning under precise conditions.
Domain adaptation framework identifies latent variables for target distribution identifiability.
problem Unsupervised domain adaptation without identifiable joint distribution of features and labels.
method Formulated latent variable model with invariant and changing components, constrained domain shift to influence only changing components.
result Joint distribution of data and labels in target domain is identifiable under mild conditions.
Radio frequency (RF) sensors are used alongside other sensing modalities to provide rich representations of the world. Given the high variability of complex-valued target responses, RF systems are susceptible to attacks masking true target characteristics from accurate identification. In this work, we evaluate differen…
Structured entropy improves classification performance on structured targets.
problem Cross-entropy loss fails to account for target variable structure.
method Proposes structured entropy, a generalization of entropy using random partitions.
result Structured cross-entropy loss yields better results on classification problems with known structure.
Sparse neural encoding can store more memories as targets become sparser.
problem Storing sparse input-target associations in neural networks.
method Mathematical proofs using properties of random polytopes and sub-gaussian random vector variables.
result The capacity of neural maps increases with sparsity in target layers.
ReTaSA tackles continuous target shift in regression problems.
problem Continuous target shift in regression settings.
method Nonparametric regularized approach to estimate importance weight function.
result The method provides theoretical justification for the estimated importance weight function.
Paper addresses fairness issues in error-prone outcomes.
problem Fairness in error-prone outcomes.
method Combining fair ML methods and measurement models.
result Using a latent variable model removes detected unfairness.
New method learns differential equations from data with hidden variables.
problem Learning differential equations from data with hidden variables.
method Sparse linear regression optimization problem with higher order time derivatives and dictionary of functions.
result High quality short-term forecasts with orders of magnitude faster than competing methods.
PWSHAP provides targeted explanations for complex models.
problem Inability of black-box models to explain targeted effects in sensitive domains.
method Augments model with DAG, uses Shapley values for causal pathway identification.
result Establishes error bounds and demonstrates resolution, interpretability, and locality.
A new method combines predictors and their lags using supervised PCA for dynamic forecasting.
problem Dynamic forecasting with many predictors.
method Supervised PCA with re-scaling and penalized methods.
result The method outperforms traditional PCA and diffusion-index approaches in prediction.
Proposes a transfer learning method for accurate latent variable estimation.
problem Accurate estimation of latent variables in networks with large parameter spaces.
method Leverages information from similar networks to improve estimation accuracy.
result The proposed methods improve estimation accuracy and are validated on real datasets.
Predictive models can fail to generalize from training to deployment environments because of dataset shift, posing a threat to model reliability and the safety of downstream decisions made in practice. Instead of using samples from the target distribution to reactively correct dataset shift, we use graphical knowledge …
A single pre-trained agent guides feature selection using knockoffs.
problem Feature selection challenges in AI-readiness of data.
method Generates knockoff features and uses reinforcement learning.
result Optimal feature subset identified with reduced dependency on target variable.
Framework improves policy generalizability under biased training data.
problem Learning policies that generalize to a target population from biased training data.
method Characterizes sample selection bias using a selection variable, optimizes minimax value over uncertainty set, derives efficient algorithm.
result Policies generalize to target population, outperform standard methods.
SIG model identifies invariant variables for MSDA with fewer domain constraints.
problem Challenges in enforcing minimal changes across domains for MSDA.
method Subspace identification theory and variational inference.
result SIG model outperforms existing techniques on various benchmark datasets.
A new method improves target selection for manipulating complex systems like the brain.
problem Improper incorporation of low-variance outcomes into latent space of predictive models.
method Developed a novel objective based on supervised variational autoencoders (SVAEs) for PPCA (Probabilistic Principal Component Analysis).
result gPCR (Generative Principal Component Regression) dramatically improves target selection in manipulation compared to standard PCR and SVAEs.
fCBO optimizes interventions in causal graphs using Gaussian processes.
problem Optimizing interventions in known causal graphs.
method Functional causal Bayesian optimization (fCBO) using Gaussian processes and expected improvement acquisition.
result Functional interventions can lead to better target effects and optimal conditional effects.
The paper explores how to extrapolate from limited data points using causal mechanisms.
problem Handling distribution shifts with limited target samples.
method Formulates the extrapolation problem with a latent-variable model embodying the minimal change principle in causal mechanisms, and identifies conditions for identification.
result Theoretical understanding and practical methods for extrapolation without requiring an on-support target distribution.
A method estimates causal parameters using a latent variable recovery.
problem Estimating causal parameters in contexts with multiple causes and unobserved confounding.
method Substitute adjustment via recovery of latent variables.
result Substitute adjustment estimates adjusted regression parameters under certain conditions.
New estimators for causal effects in DAGs with hidden variables, addressing computational and statistical challenges.
problem Estimating causal effects in DAGs with hidden variables beyond traditional criteria.
method Introduces novel one-step corrected plug-in and targeted minimum loss-based estimators for causal effects in DAGs with hidden variables.
result Root-n consistent causal effect estimates with desirable statistical properties.
Traditional medicine typically applies one-size-fits-all treatment for the entire patient population whereas precision medicine develops tailored treatment schemes for different patient subgroups. The fact that some factors may be more significant for a specific patient subgroup motivates clinicians and medical researc…
New method models complex dynamics using a base variable.
problem Modeling complex high-frequency dynamics from time series.
method Constructing a joint model with a base variable and a target variable.
result Successfully models chaotic behavior and reconstructs statistical properties.