Study on NNs for forecasting time series with novel control variable combinations.
problem Forecast future time series with novel combinations of control variables.
method Modular NN architecture with inductive bias for independence of control variables.
result Modular NN architecture improves forecasting of dependent variables up to large horizons.
A new method reduces variance in training discrete latent variable models.
problem High variance in stochastic gradient estimators for discrete latent variable models.
method Double control variates for score function estimators using Taylor expansions.
result Our method can have lower variance compared to other estimators.
New method infers causal effects without knowing control variables.
problem Inference errors when control variables are unknown.
method Proposes a method for inferring causal effects when control variables are unknown.
result Proves method yields asymptotically valid confidence intervals for average causal effects.
Private variable selection method controls FDR with simulations showing reasonable power.
problem Performing variable selection with privacy constraints.
method Private knockoff filter using Gaussian and Laplace mechanisms.
result Achieves controlled false discovery rate (FDR) in variable selection.
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.
SurvNet selects important variables in DNNs with false discovery rate control.
problem Variable selection in deep neural networks (DNNs) for interpretability.
method Backward elimination procedure based on a new variable importance measure.
result SurvNet estimates and controls false discovery rate of selected variables.
Proposes a deep reinforcement learning model for efficient variable speed limits control.
problem Improving traffic flow, safety, and emissions on freeways with varying speed limits.
method Uses a novel actor-critic architecture for deep reinforcement learning to manage dynamic speed limits.
result The proposed method enhances efficiency, safety, and emissions compared to traditional control methods.
Kernel methods identify treatment effects with unobserved confounding using negative controls.
problem Learning causal relationships with unmeasured confounding.
method Kernel ridge regression algorithms for nonparametric treatment effects.
result Uniform consistency and finite sample rates of convergence proved.
Novel framework controls FDR in high-dimensional, dependent data.
problem FDR control failure in high-dimensional, dependent data.
method Dependency-aware T-Rex selector integrating hierarchical graphical models and martingale theory.
result First to control FDR in high-dimensional, dependent data.
A/B testing improves marketing decisions by selecting effective stratification variables.
problem Improving the sensitivity of A/B testing through stratified sampling.
method Designing an algorithm to select a subset of stratification variables for variance reduction.
result The subset selection method outperforms other variance reduction techniques in A/B testing.
T-Rex selector selects variables fast and controls FDR in high-dimensional data.
problem Variable selection in high-dimensional data with FDR control.
method Fused solutions of early terminated random experiments.
result FDR control at target level with high variable selection power.
A game theory study examines gradual concessions in variable contribution games under uncertainty.
problem Gradualism in contribution games due to free rider effect.
method Stochastic game analysis of variable contribution games, extending Nerlove-Arrow model.
result Equilibrium characterized by regular control strategies leading to gradual concession.
Big T-Rex solves FDR-controlled sparse regression on laptops with millions of variables.
problem Scalable FDR-controlled variable selection for high-dimensional data.
method Early terminated random experiments with memory-mapping and permutation-based dummy generation.
result Solves FDR-controlled Lasso problems with 5 million variables on a laptop in 30 minutes.
Simpson's paradox can invalidate the results of medical trials if an unobserved variable is considered.
problem The validity of medical trial results can be invalidated by an unobserved confounding variable.
method Simulated controlled trials to demonstrate the paradox.
result The results of a trial can be reversed by including an unobserved confounding variable.
New method controls FDR for sparse GLMs, identifying positive and negative relationships.
problem Sparse GLMs with high-dimensional data and varying sample size.
method Debiased-Lasso estimator and CLIME method for precision matrix estimation.
result Asymptotically controls directional FDR and FDV for sparse GLMs.
Enhances FDR control in variable selection using neural networks.
problem Balancing rigorous error control with statistical power in high-dimensional variable selection.
method Learning-augmented T-Rex Selector framework with a neural network trained on synthetic datasets.
result Achieves superior detection of true variables compared to existing approaches.
Method interprets GAN latent space via latent variable correlation analysis.
problem Understanding the inner workings of GANs.
method Analyzing correlation between latent variables and semantic contents in generated images.
result A method for controllable semantic content generation in GANs.
New method reduces memory usage for high-dimensional variable selection.
problem Scalability issues in high-dimensional variable selection, especially in genomics.
method Adaptive sampling of null features to eliminate dummy matrix materialization.
result Reduces memory and runtime by several orders of magnitude while preserving FDR control.
Paper uses VAEs to control IVS features for financial modeling.
problem Generating realistic IVSs with desired characteristics.
method Variational autoencoder architecture with controllable latent variables.
result Controlled generation of IVSs with specified features.
Paper introduces multitask neural networks for efficient stochastic control problems.
problem Infeasibility of simulating state variables in some stochastic control problems.
method Multitask neural networks with dynamic task balancing.
result Multitask neural networks outperform state-of-the-art approaches in derivatives pricing problems.
Paper proposes knockoff-based methods to simplify deep neural networks by controlling false discovery rates.
problem High-dimensional deep neural networks with many irrelevant parameters and inputs.
method Knockoff methods combined with regularized neural networks for variable screening.
result Proposed algorithms show satisfactory performance in controlling false discovery rates.
Proposes a two-stage method for testing variable interactions with FDR control.
problem Testing pairwise interactions in high-dimensional data with dependence.
method Two-stage testing procedure with FDR control using Cramér type moderate deviation technique.
result The proposed method controls FDR and has comparable or improved statistical power.
Proposes a new estimator for weak instrumental variables in panel data models.
problem Weak instrumental variables due to ignored nonlinearities in panel data.
method Triangular simultaneous equation model with a nonlinear reduced form equation and a control function approach using Super Learner.
result The proposed SLCF estimator is consistent and asymptotically normal, achieving a parametric rate of convergence.
Improved FDR control for sparse financial index tracking.
problem Maintaining FDR control in high-dimensional financial data with strong variable dependencies.
method Expanding T-Rex framework to handle overlapping groups of correlated variables with nearest neighbors penalization.
result Accurately tracks the S&P 500 index using only a small number of stocks.
Hierarchical-CPI improves variable importance measurement for medical data.
problem Limited interpretability of complex medical models.
method Hierarchical-CPI measures conditional variable importance with statistical control, handling correlated data.
result Hierarchical-CPI outperforms existing methods in medical datasets.
Sparse PCA selects variables with FDR control for improved performance.
problem Sparse PCA selects irrelevant variables when maximizing explained variance.
method Proposes FDR-controlled selection using T-Rex selector.
result Significant performance improvement over traditional sparse PCA.
A heuristic method for determining input ranges for complex processes.
problem Determining input variable ranges for non-numeric, high-dimensional processes.
method Create synthetic training data and use a decision tree classifier.
result Validated on a real use case in a lamination factory.
This study proposes a method to generate biosignals with controlled characteristics using GANs and latent variable analysis.
problem Unclear relationship between input and generated data from GANs, inability to control generated data characteristics.
method Recurrent GANs with latent variable analysis using CCA to control generated biosignals.
result Effective control of biosignal characteristics using proposed GAN method and latent variable analysis.
New method controls false edge detections in Gaussian graphical models.
problem High false edge detections in well-established estimators.
method Nodewise variable selection approach to control false discovery rate.
result Significant gain in performance compared to competing methods.
Stability Selection improves structured variable selection but requires careful tuning.
problem Finding a right-sized model or controlling false positives in structured selection problems.
method Stability Selection applied to group lasso and structured input-output lasso.
result Stability Selection often increases power but reduces error control reliability in structured settings.
Assigning significance in high-dimensional regression is challenging. Most computationally efficient selection algorithms cannot guard against inclusion of noise variables. Asymptotically valid p-values are not available. An exception is a recent proposal by Wasserman and Roeder (2008) which splits the data into two pa…
Optimizes tree ensemble models for controllable variables.
problem Maximizing predicted value by setting controllable independent variables in tree ensemble models.
method Formulated as mixed-integer optimization problem, developed solution methods based on Benders decomposition and iteratively generating tree split constraints.
result Efficiently solves large-scale instances to near or full optimality, outperforming heuristic approaches.
Proposes a method to learn dynamic models for systems with variable number of objects.
problem Efficiently modeling systems with a variable number of objects.
method Uses graph neural networks and block-wise linear transition matrices to learn compositional Koopman operators.
result The method adapts to new environments and produces better control signals.
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.
Paper presents deep LSMC method for efficient variable annuity pricing.
problem Efficiently pricing variable annuities with guarantees using simulation methods.
method Modifies least-squares Monte Carlo (LSMC) algorithm for optimal stochastic control problems.
result Deep LSMC provides more stable and robust pricing performance for higher-dimensional problems.
Paper proposes a privacy-preserving knockoff inference method.
problem Ensuring privacy in model-X knockoff inference.
method Differential privacy framework for knockoff inference.
result Guaranteed FDR control with privacy protection.
New method identifies causal relationships using proxy variables in the presence of unmeasured confounders.
problem Challenges in inferring causal relationships due to unmeasured confounding.
method Develops a general nonparametric approach using a single negative control outcome (NCO) and negative control exposure (NCE).
result Establishes a new identification result and proposes a kernel-based testing procedure.
Proposes a virtual bidding strategy for electricity markets using stochastic control.
problem Optimizing electricity prices in day-ahead and real-time markets.
method Modeling price differences as Brownian motion with meteorological variables, transforming into portfolio management problem.
result Developed a strategy to manage electricity prices efficiently.
New algorithm uses control variates to improve multi-armed bandit performance.
problem Stochastic multi-armed bandits with auxiliary reward information.
method Developed UCB-CV algorithm using control variates for mean estimation.
result UCB-CV algorithm provides tighter confidence bounds and smaller variance.
Bayesian optimization adapted for experiments with changing environmental conditions.
problem Optimizing experiments influenced by uncontrollable environmental factors.
method Extends Bayesian optimization to handle both controllable and uncontrollable parameters, fitting a global surrogate model and optimizing only controllable parameters conditionally on measurements of uncontrollable variables.
result The proposed ENVBO algorithm finds solutions for the full domain of the environmental variable more efficiently and cost-effectively than traditional methods.
Bayesian approach controls FDR in high-dimensional models.
problem High-dimensional variable selection and inference.
method Adapted Mirror Statistic to Bayesian framework for FDR control.
result Effective FDR control without data splitting.
Solves high-dimensional observation learning for control models.
problem Learning dynamics from high-dimensional images is challenging.
method Proposes a Beta DVBF approach to handle latent and observable space discrepancies.
result Demonstrates improved model learning from high-dimensional observations.
New approach to optimal dividend control with mean-variance criterion.
problem Balancing expected dividends and variability in a singular control framework.
method Game-theoretic approach to find time-consistent equilibrium strategies.
result Verification theorem for MV singular dividend control problem.
NICE learns a representation to avoid bad controls in causal inference.
problem Avoiding bad controls in causal inference from observational data.
method Uses invariant risk minimization (IRM) to learn a representation of covariates that avoids bad controls.
result NICE outperforms adjusting for all covariates in cases with unknown collider variables and bad controls.
Novel privatization framework for high-dimensional variable selection with differential privacy.
problem High-dimensional controlled variable selection with rigorous FDR control under differential privacy constraints.
method Gaussian Johnson-Lindenstrauss Transformation for privatizing the knockoff matrix.
result The proposed private variable selection procedure maintains statistical power even under strict privacy budgets.
New algorithm controls false discoveries in time series Granger causality.
problem Learning causal structure from time series data with false discovery control.
method Proposes MMPC-p algorithm for Granger causality, controls false discovery rate.
result Shows improved statistical power and false discovery rate control in experiments.
Develops robust knockoffs for controlling false discoveries in financial data.
problem Challenges in variable selection with highly correlated data in finance and economics.
method Robustified knockoff framework addressing high dependence and time correlation.
result Identifies new important groups of factors on top of known drivers.
Proposes hybrid reinforcement learning for both discrete and continuous control problems.
problem Real-world control problems involving both discrete and continuous decision variables.
method Solves hybrid problems by optimizing for discrete and continuous actions simultaneously.
result Efficiently solves hybrid reinforcement learning problems and improves upon expert heuristics.