Machine learning predicts Sunn Pest migration and nymphal stages for better pesticide application timing.
problem Improving pest control timing through accurate forecasting of Sunn Pest life-cycle phases.
method Correlated climate data with Sunn Pest life-cycle phases using decision trees and random forest models.
result Highly accurate predictions of migration and nymphal stages for better pesticide application timing.
Improves classifier performance in multi-stage selection processes.
problem Difficulty in training classifiers in multi-stage selection processes due to varying sample sizes and information.
method Multi-Stage Transfer Learning (MSGTL) approach that uses knowledge from simpler classifiers trained in early stages to improve later stages.
result MSGTL outperforms other transfer learning methods in real-world selection process data.
A new conformal prediction framework for two-stage models identifies stage-wise uncertainty.
problem Limited coverage guarantees and lack of modular structure understanding in existing conformal prediction methods.
method Decomposes prediction residuals into stage-specific components, calibrates parameters using FWER control, and adapts to non-stationary settings.
result Improves coverage and identifies stage-wise error contributions compared to standard conformal methods.
New framework optimizes complex systems decisions via simulation.
problem Optimizing strategic, tactical, and operational decisions in complex systems.
method Global-local metamodel assisted two-stage optimization via simulation.
result Framework efficiently searches for optimal decisions with unknown objective.
Dual-stage sEMG classification improves gesture recognition accuracy.
problem Improving accuracy in hand gesture recognition from sEMG signals.
method Dual-stage classification approach: first stage groups similar activities, second stage classifies within groups.
result Dual-stage classification yields significantly higher accuracy than single-stage approach.
Improves classifier performance in multi-stage processes with adversarial autoencoders and multi-task learning.
problem Challenges in training classifiers due to varying sample sizes and information content across stages.
method Combines adversarial autoencoders, multi-task learning, and semi-supervised learning to address underfitting and overfitting.
result Our approach outperforms state-of-the-art methods across different domains.
New algorithm efficiently learns sparse staged trees.
problem Scalability issue in existing structural learning algorithms for staged trees.
method Searches over a space of models with limited dependencies.
result First scalable structural learning algorithm for staged trees.
This paper explores the following question: what kind of statistical guarantees can be given when doing variable selection in high-dimensional models? In particular, we look at the error rates and power of some multi-stage regression methods. In the first stage we fit a set of candidate models. In the second stage we s…
This work improves speech enhancement using multiple GAN generators in stages.
problem Efficient speech enhancement using GANs with multiple stages.
method Multiple GAN generators in a chained multi-stage approach, with two scenarios: shared or independent parameters.
result Multi-stage SEGAN outperforms one-stage SEGAN, with independent generators yielding better results.
The paper studies fairness in multi-stage selection problems and introduces a method to compute fair selections.
problem Fairness in multi-stage selection problems with additional features at each stage.
method Introducing fairness notions, proposing a linear program for fair selections, and defining the price of local fairness.
result It is possible to have a selection that has a small price of local fairness and is close to locally fair.
In this paper, we consider multi-stage stochastic optimization problems with convex objectives and conic constraints at each stage. We present a new stochastic first-order method, namely the dynamic stochastic approximation (DSA) algorithm, for solving these types of stochastic optimization problems. We show that DSA c…
New algorithms learn staged trees from incomplete data.
problem Handling missing data in staged tree learning.
method Characterized likelihood and pseudo-likelihoods for missing data, implemented structural expectation-maximization algorithm.
result It is feasible to account for different missingness patterns when learning staged trees.
StageNet improves health risk prediction by integrating disease stage information.
problem Improving health risk prediction for patients with chronic conditions.
method StageNet uses a stage-aware LSTM and stage-adaptive convolutional modules to extract and integrate disease stage information.
result StageNet achieves up to 12% higher AUPRC for risk prediction and over 58% higher Calinski-Harabasz score for patient subtyping compared to state-of-the-art models.
In many classification systems, sensing modalities have different acquisition costs. It is often {\it unnecessary} to use every modality to classify a majority of examples. We study a multi-stage system in a prediction time cost reduction setting, where the full data is available for training, but for a test example, m…
SDQL uses modular deep Q networks to efficiently learn multi-stage optimal control tasks.
problem Training complex deep reinforcement learning models for multi-stage control tasks is inefficient and unstable.
method Stacked Deep Q Learning (SDQL) with modular Q networks and backward training.
result SDQL efficiently learns optimal control policies for multi-stage tasks with high-dimensional state and action spaces.
Machine learning can improve 2SLS first stage predictions, but nonlinear methods often introduce bias.
problem Improving the first stage of 2SLS using machine learning.
method Decomposed bias into three components, investigated through simulation.
result Nonlinear machine learning methods can introduce substantial bias in second-stage estimates.
We propose a two-stage hybrid approach with neural networks as the new feature construction algorithms for bankcard response classifications. The hybrid model uses a very simple neural network structure as the new feature construction tool in the first stage, then the newly created features are used as the additional i…
We consider two stage estimation with a non-parametric first stage and a generalized method of moments second stage, in a simpler setting than (Chernozhukov et al. 2016). We give an alternative proof of the theorem given in (Chernozhukov et al. 2016) that orthogonal second stage moments, sample splitting and n1/4-…
CycleFQI tackles offline reinforcement learning for cyclic MDPs, mitigating state distribution mismatch.
problem Offline reinforcement learning for cyclic MDPs with heterogeneous dynamics and discount factors.
method CycleFQI decomposes the cyclic process into stage-wise sub-problems, using vector of stage-specific Q-functions.
result CycleFQI mitigates the curse of dimensionality and provides finite-sample suboptimality error bounds.
Two-stage framework detects multi-modal outliers.
problem Detecting outliers in multi-modal data structures.
method Combines global kernel PCA and local clustering stages.
result Significantly outperforms existing methods on challenging datasets.
EML model tackles evolving features in online metric learning.
problem Challenges in applying metric learning to evolving features.
method Develops a new Evolving Metric Learning (EML) model for incremental and decremental features.
result EML model handles instance and feature evolutions simultaneously.
Many problems on signal processing reduce to nonparametric function estimation. We propose a new methodology, piecewise convex fitting (PCF), and give a two-stage adaptive estimate. In the first stage, the number and location of the change points is estimated using strong smoothing. In the second stage, a constrained s…
Hippo optimizes deep learning hyper-parameters by reducing redundant trials.
problem Redundant hyper-parameter trials in hyper-parameter optimization.
method Hippo breaks down hyper-parameter sequences into stages and executes them in a tree structure.
result Hippo reduces GPU-hours and training time significantly compared to existing methods.
The aim of this paper is to write explicit expression in terms of a given principal connection of the Lagrange-d'Alembert-Poincarè equations in several stages. This is obtained by using a reduced Lagrange-d'Alembert's Principle in several stages, extending methods introduced for the case of two stages by one of the aut…
Bayesian optimization tackles non-convex, two-stage stochastic problems efficiently.
problem Solving non-convex, two-stage stochastic optimization problems with expensive, black-box evaluations.
method Knowledge-gradient-based acquisition function for joint optimization of first- and second-stage variables.
result Comparable and superior empirical results compared to alternatives.
Adaptive multi-stage density ratio estimation improves learning of latent space EBM.
problem Learning energy-based models in latent space is computationally expensive and challenging.
method Adaptive multi-stage density ratio estimation using NCE to bridge the gap between prior and posterior densities.
result The method enables more expressive prior models and sharpens the latent space EBM.
Two-stage recommender systems struggle with exploration, leading to linear regret.
problem Linear regret in two-stage recommender systems due to exploration issues.
method Proposed a method to synchronize exploration strategies between the ranker and nominators using LinUCB.
result Demonstrated the effectiveness of the proposed algorithm experimentally.
New framework improves adversarial robustness in one-stage L2D.
problem Adversarial robustness in one-stage Learning-to-Defer (L2D).
method Formalizes attacks, proposes cost-sensitive adversarial surrogate losses, establishes theoretical guarantees.
result Improves robustness against untargeted and targeted attacks while preserving clean performance.
New algorithms learn simple staged trees from data, improving model fit.
problem Complex conditional independences in categorical data vectors.
method Structural learning algorithms for simple staged trees, coalescing the underlying tree.
result Data-learned simple staged trees often outperform Bayesian networks in model fit.
3S-SC framework improves SC accuracy by optimizing affinity matrices.
problem Inaccurate clustering results due to flawed affinity matrices.
method 3S-SC framework with GBTO to optimize affinity matrices.
result Improves connectivity and accuracy in SC methods.
Guided Learning improves end-to-end modeling for multi-stage decision-making.
problem Challenges in training unified neural networks for multi-stage decision-making.
method Guided Learning framework with a guide function and utility function.
result Significant improvement in performance over traditional methods.
New framework estimates staged tree models using hierarchical clustering on the probability simplex.
problem Estimating staged tree models with context-specific dependencies.
method Hierarchical clustering on the probability simplex, using simplex-based divergences and linkage methods.
result Total Variation divergence with Ward.D2 linkage produces staged trees with better model fit, structure recovery, and computational efficiency.
Stage-based hyper-parameter optimization reduces GPU-hours and training time.
problem Efficiently executing hyper-parameter optimization for deep learning models.
method Stage-based execution strategy to remove redundant computations.
result Stage-based execution outperforms trial-based method by up to 6.60 times in GPU-hours and 4.13 times in training time.
This paper improves fraud prevention rule sets in fintech by generating diverse rules and finding Pareto-optimal subsets.
problem Improving the quality and flexibility of fraud prevention rule sets in fintech.
method Introducing SpectralRules for generating diverse rules, and PORS for finding Pareto-optimal subsets.
result SpectralRules generates diverse rules that improve the quality of final rule subsets.
SLEEPER combines deep learning with expert rules for accurate sleep staging.
problem Manual sleep staging is tedious and requires expert time.
method SLEEPER uses convolutional neural networks and expert rules to generate interpretable models.
result SLEEPER achieves comparable accuracy to human experts and deep neural networks.
R package stagedtrees learns staged tree structures from data.
problem Learning the structure of staged trees from data.
method Score-based and clustering-based algorithms implemented.
result Illustrated capabilities using two datasets.
Study shows startup competition and investor network influence fundraising success at different stages.
problem Misunderstanding of fundraising success factors across startup stages.
method Used Word2Vec for competition measures and Graph Neural Networks for investor network analysis.
result Startup competition is crucial for early-stage fundraising, while growth-stage fundraising is influenced by investor network features.
Proposes a two-stage method for selecting correlated predictors in high-dimensional data.
problem Selecting correlated predictors in high-dimensional data with unknown group structures.
method Two-stage approach: variable clustering followed by group selection.
result The two-stage method improves prediction accuracy and active predictor selection.
New deep learning method validated across multiple sleep staging databases.
problem Improving automatic sleep scoring accuracy across different datasets.
method Ensemble of local models using deep learning for automatic sleep staging.
result Good general performance compared to human experts and state-of-the-art methods.
We have developed an automatic sleep stage classification algorithm based on deep residual neural networks and raw polysomnogram signals. Briefly, the raw data is passed through 50 convolutional layers before subsequent classification into one of five sleep stages. Three model configurations were trained on 1850 polyso…
The paper introduces staged event trees for transparent treatment effect estimation.
problem Estimating treatment effects from observational data.
method Staged event trees framework for causal inference.
result Enhanced treatment effect estimation with improved interpretability.
This paper develops a learning framework for optimal strategies in multi-stage decentralized matching markets.
problem Optimal strategies in multi-stage decentralized matching markets with uncertain preferences.
method Nonparametric statistical approach and variational analysis.
result Participants can be better off with multi-stage matching compared to single-stage matching.
Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is an early predictor of Parkinson's disease. This study proposes a fully-automated framework for RBD detection consisting of automated sleep staging followed by RBD identification. Analysis was assessed using a limited polysomnography montage fr…
POTEC tackles off-policy learning in large action spaces, improving effectiveness.
problem Existing OPL methods fail in large discrete action spaces due to bias or variance issues.
method Two-stage algorithm: cluster selection via policy-based approach, action selection via regression-based approach.
result POTEC provides substantial improvements in off-policy learning effectiveness, especially in large and structured action spaces.
A new algorithm converts staged trees into Chain Event Graphs.
problem Creating explicit representations of structural zeros and conditional independences.
method Iterative backward algorithm transforming a staged tree into a CEG.
result No information is lost in the transformation from staged tree to CEG.
TSCI estimates treatment effects using machine learning and data-adaptive methods for invalid instruments.
problem Estimating treatment effects with invalid instruments.
method Two-stage algorithm: first stage uses machine learning for nonlinearities, second stage selects and projects out instrument violations.
result Effective treatment effect estimation even with invalid instruments.
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.
Study uses deep neural networks for causal inference tasks, especially in high-dimensional settings.
problem Challenges in direct estimation for complex causal inference tasks.
method Sequential multi-stage learning with doubly robust deep neural networks.
result Theoretical guarantees for DNNs' effectiveness in high-dimensional causal inference.