New algorithms for batch decision-making with high-dimensional user data.
problem Maximizing treatment efficacy in groups of users.
method Teamwork LASSO Bandit algorithm, switching between teamwork and selfish stages.
result Upper bound on expected cumulative regret for the proposed algorithm.
New approach for open ad hoc teamwork using graph-based policy learning.
problem Designing autonomous agents to collaborate with changing teams without prior coordination.
method Graph-based policy learning to adapt to dynamic team compositions.
result Successfully models the effects of other agents, leading to robust adaptation and superior performance.
A new algorithm reduces communication costs for collaborative decision-making across clients.
problem Collaborative decision-making with sparse rewards and heterogeneous contexts.
method Federated Lasso algorithm for sparse linear contextual bandits.
result Achieves near-optimal regret with logarithmic communication costs.
DiCE uses diverse agents to explore and learn, avoiding local minima.
problem Local minima in RL due to limited exploration and correlated behavior.
method DiCE employs a group of heterogeneous agents to explore simultaneously and share experiences, with a diversity regularization mechanism.
result DiCE achieves substantial improvement over baselines in MuJoCo locomotion tasks.
CGAs estimate team performance from data, simplifying SV computation.
problem Predicting and rewarding team performance using game theory.
method Cooperative game abstractions (CGAs) for estimating characteristic functions from data.
result CGAs enable linear-time computation of Shapley Value for team contributions.
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.
To integrate strategic, tactical and operational decisions, the two-stage optimization has been widely used to guide dynamic decision making. In this paper, we study the two-stage stochastic programming for complex systems with unknown response estimated by simulation. We introduce the global-local metamodel assisted t…
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.
Blended courses that mix in-person instruction with online platforms are increasingly popular in secondary education. These tools record a rich amount of data on students' study habits and social interactions. Prior research has shown that these metrics are correlated with students' performance in face to face classes.…
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…
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.
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…
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.
The rise of algorithmic decision making led to active researches on how to define and guarantee fairness, mostly focusing on one-shot decision making. In several important applications such as hiring, however, decisions are made in multiple stage with additional information at each stage. In such cases, fairness issues…
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…
Subspace clustering (SC) refers to the problem of clustering high-dimensional data into a union of low-dimensional subspaces. Based on spectral clustering, state-of-the-art approaches solve SC problem within a two-stage framework. In the first stage, data representation techniques are applied to draw an affinity matrix…
Deep reinforcement learning for high dimensional, hierarchical control tasks usually requires the use of complex neural networks as functional approximators, which can lead to inefficiency, instability and even divergence in the training process. Here, we introduce stacked deep Q learning (SDQL), a flexible modularized…
Generative adversarial networks (GAN) have recently been shown to be efficient for speech enhancement. However, most, if not all, existing speech enhancement GANs (SEGAN) make use of a single generator to perform one-stage enhancement mapping. In this work, we propose to use multiple generators that are chained to perf…
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.
Deep learning has demonstrated success in health risk prediction especially for patients with chronic and progressing conditions. Most existing works focus on learning disease Network (StageNet) model to extract disease stage information from patient data and integrate it into risk prediction. StageNet is enabled by (1…
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.
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.
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.
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.
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.
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.
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…
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.
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.
We address the issue of the factors driving startup success in raising funds. Using the popular and public startup database Crunchbase, we explicitly take into account two extrinsic characteristics of startups: the competition that the companies face, using similarity measures derived from the Word2Vec algorithm, as we…
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.