New algorithm optimizes clinical trials by strategically allocating patients.
problem Optimizing patient allocation in sequential recruitment studies.
method Formulated as a Markov Decision Process, an algorithm (RCT-KG) is proposed to minimize errors.
result Significant reduction in errors and fewer patients needed for a given trial size.
Ant colonies and boosting algorithms both reduce bias and variance through adaptive mechanisms.
problem Understanding the mathematical principles behind ensemble learning and ant colony behavior.
method Developed a formal mapping between AdaBoost's adaptive reweighting and ant recruitment dynamics.
result Proved that the fundamental theorem of weak learnability has a direct analog in colony decision-making.
Machine learning predicts patient recruitment for clinical trials.
problem Improving patient recruitment prediction for clinical trials.
method Machine learning methods applied to historical clinical trial data.
result Reduced prediction error compared to current industry standards.
Deep learning models improve talent search at LinkedIn.
problem Match candidates to hiring needs using complex feature interactions.
method Deep and representation learning models, including neural network models and learning to rank approaches.
result Improved offline and online evaluation results for talent search systems.
New private algorithm for sequential hypothesis testing with privacy and error rate guarantees.
problem Privacy protection in sequential hypothesis testing for sensitive data.
method Renyi differential privacy, Wald's Sequential Probability Ratio Test (SPRT).
result Private algorithm with strong privacy guarantees and theoretical performance analysis.
Doctor2Vec learns doctor representations from EHRs for better clinical trial recruitment.
problem Identifying the right doctors for clinical trials based on EHR data and trial descriptions.
method Dynamic Memory Network with attention mechanism to learn doctor and trial representations.
result Improved performance by up to 8.7% in PR-AUC on real-world trials and EHR data.
Turtle Score analyzes developer similarity to match high-performing candidates.
problem Finding suitable candidates for IT companies based on cultural fit.
method Examines employee performance data, applies machine learning for similarity analysis.
result Develops a model for recruiters to identify high-performing candidates.
A firm learns to efficiently screen candidates for interviews.
problem Minimizing interviews while ensuring optimal matching of candidates to departments.
method Online assignment problem with two variants: independent draws and access to a training set.
result Exponential reduction in the number of retained items with access to a training set.
C3T-Budget optimizes drug efficacy in dose-finding trials with budget and safety constraints.
problem Heterogeneous patient populations and budget constraints make dose-finding clinical trials challenging.
method Contextual constrained clinical trial algorithm that maximizes drug efficacy while learning subgroup responses.
result Demonstrates efficient budget usage and balanced learning-treatment trade-off in simulated trials.
Paper proposes player roles from match data to help clubs.
problem Difficulty in determining a player's fit for a team's style.
method Identifies 21 player roles from match event data.
result Automatic identification of player roles from match data.
Proposes a framework for fairness in two-sided marketplaces.
problem Achieving fairness in two-sided marketplaces.
method Developed an end-to-end framework for fairness constraints from both sides of the marketplace, including dynamic aspects.
result Efficacy of the proposed framework demonstrated through simulations.
We present safe active incremental feature selection~(SAIF) to scale up the computation of LASSO solutions. SAIF does not require a solution from a heavier penalty parameter as in sequential screening or updating the full model for each iteration as in dynamic screening. Different from these existing screening methods,…
ADRL improves participant selection in MCS systems.
problem Designing a participant selection algorithm for different MCS systems with multiple goals.
method Auxiliary-task based deep reinforcement learning (ADRL) using transformers and pointer networks.
result ADRL outperforms other baselines in various MCS settings.
A new framework for adaptive behavior using reusable value profiles.
problem Adaptive behavior in changing environments requires switching among value-control regimes, but maintaining separate parameters for each situation is impractical.
method Introduces value profiles: reusable bundles of parameters assigned to hidden states, allowing for state-conditional strategy recruitment without independent parameters for each context.
result Profile-based models outperform simpler alternatives in probabilistic reversal learning, suggesting belief-dependent control of adaptive behavior.
Over the past two decades, the notion of implicit bias has come to serve as an important component in our understanding of discrimination in activities such as hiring, promotion, and school admissions. Research on implicit bias posits that when people evaluate others -- for example, in a hiring context -- their unconsc…
The personalization of treatment via bio-markers and other risk categories has drawn increasing interest among clinical scientists. Personalized treatment strategies can be learned using data from clinical trials, but such trials are very costly to run. This paper explores the use of active learning techniques to desig…
We present a novel factor analysis method that can be applied to the discovery of common factors shared among trajectories in multivariate time series data. These factors satisfy a precedence-ordering property: certain factors are recruited only after some other factors are activated. Precedence-ordering arise in appli…
Early prognosis of Alzheimer's dementia is hard. Mild cognitive impairment (MCI) typically precedes Alzheimer's dementia, yet only a fraction of MCI individuals will progress to dementia, even when screened using biomarkers. We propose here to identify a subset of individuals who share a common brain signature highly p…
Syntax designs adaptive trials for subpopulations with potential benefits.
problem Identifying subpopulations with positive treatment effects in diverse patient populations.
method Adaptive patient recruitment and synthetic control estimation.
result Syntax outperforms conventional trial designs in identifying beneficial subpopulations.
Assessing the impact of the individual actions performed by soccer players during games is a crucial aspect of the player recruitment process. Unfortunately, most traditional metrics fall short in addressing this task as they either focus on rare actions like shots and goals alone or fail to account for the context in …
Study shows fiduciary duty reduces municipal bond yields by 9% after SEC rule.
problem Effect of fiduciary duty on municipal bond yields and fees.
method Difference-in-differences analysis using hand-collected data.
result Bond yields decrease by 9% after SEC rule, but smaller issuers see increased borrowing costs.
Machine learning conferences face ethical issues in review process.
problem Ethical issues in the review process of machine learning conferences.
method Study of recruitment issues, double-blind process infringements, fraudulent behaviors, biases, and appendix phenomenon.
result Highlighting the need for awareness in the machine learning community.
Methods for prediction and tolerance intervals in non-normal models.
problem Constructing prediction and tolerance intervals for non-normal data.
method Two approaches: pivotal quantity approximation and confidence interval for mean.
result Intuitive, simple, efficient methods with proper operating characteristics.
Study automates detection of visitation disruptions in ICU patients.
problem Difficulty in detecting frequent visitation disruptions in ICU patients.
method Used DensePose R-CNN model to count people in video frames, analyzed disruptions and patient outcomes.
result Automated method detects visitation disruptions, impacts on pain and length of stay examined.
Generative model creates diverse neural network weights efficiently.
problem Creating high-performance and diverse weights for neural networks.
method Trains a hypernetwork mapping latent vectors to high-performance weights, balancing accuracy and diversity.
result Generated weights form a diverse manifold, improving classification accuracy.
Majorizing measures control sequential complexities for online learning.
problem Extending classical empirical processes theory to sequential cases.
method Generic chaining, majorizing measures, fractional covering numbers.
result Sharp control of worst-case sequential Rademacher complexity.
CrowdLLM uses LLMs and generative models to create diverse digital populations.
problem Lack of diversity and accuracy in digital populations created by LLMs.
method Integrates pretrained LLMs and generative models to enhance diversity and fidelity.
result CrowdLLM achieves promising performance in accuracy and distributional fidelity.
Experiment shows author rankings can improve peer review scores.
problem Improving accuracy in machine learning conference peer review.
method Used Isotonic Mechanism to calibrate review scores using author rankings.
result Calibrated scores outperform raw scores in estimating ground truth review scores.
Mathematical approach assesses human resource competences accurately.
problem Accurate assessment and representation of human resource competences.
method Detailed quantification scheme and mathematical approach.
result Flexible tools for optimal job assignment and recruitment.
Paper studies pseudo-projective tensors on warped products.
problem Characterizing pseudo-projectively flat warped products.
method Analyzes sequential warped products and pseudo-projective tensors.
result Necessary and sufficient conditions for pseudo-projectively flat sequential warped products.
We develop a probabilistic framework for sequential random projection.
problem Challenges of sequential decision-making under uncertainty.
method Novel construction of a stopped process and method of mixtures.
result Achieved a non-asymptotic probability bound for random projection.
New method designs fairer transport plans with uncertainty.
problem Designing fair and balanced mass transport plans.
method Hierarchical fully probabilistic design (HFPD) for transport plans.
result Optimal hyperprior for transport plans with uncertain marginals.
Single neurons can perform as well as dense networks in binary and multi-class recognition tasks.
problem Designing efficient neural networks for recognition tasks.
method Investigated the use of single or multiple neurons in neural networks for binary and multi-class recognition tasks.
result Sparse networks can be as efficient as dense networks in both binary and multi-class tasks.
In this note, we introduce a new type of warped products called as sequential warped products to cover a wider variety of exact solutions to Einstein's equation. First, we study the geometry of sequential warped products and obtain covariant derivatives, curvature tensor, Ricci curvature and scalar curvature formulas. …
Paper proposes AdaBoost-assisted ELM for efficient online sequential classification.
problem Efficient online sequential classification with improved accuracy and stability.
method Utilizes AdaBoost for cost-sensitive learning and forgetting mechanism for stability.
result Achieves 94.41% accuracy on MNIST dataset with reduced standard deviation.
Study finds conditions for certain warped product manifolds to be quasi-Einstein.
problem Conditions for quasi-Einstein sequential warped product manifolds.
method Investigated necessary and sufficient conditions for specific types of manifolds.
result Identified conditions for sequential warped product manifolds to be quasi-Einstein.
A new model for sequential memory using temporal predictive coding.
problem Forming accurate memory of sequential stimuli in the brain.
method Proposes a novel PC-based model called temporal predictive coding (tPC).
result Shows that tPC models can accurately memorize and retrieve sequential inputs.
Optimum in Convex Hulls (OCH) generalizes clinical trial results to broader populations.
problem Clinical trials exclude confounding but limit recruitment; observational data are more inclusive but suffer from confounding.
method OCH uses convex hulls of conditional expectations or densities to approximate the true treatment effect from both observational and trial data.
result OCH estimates the treatment effect with state-of-the-art accuracy in terms of both expectations and densities.
This paper reviews methods for interpreting deep learning models with sequential data.
problem Limited interpretability of deep learning models in sequential data domains.
method Reviews and compares techniques for sequential interpretability.
result Current techniques have limitations and future research is needed.
An adversarial detector identifies anomalous sequences in sequential data.
problem Detecting anomalous sequences in one-class settings with limited data.
method Solves a minimax problem to find an optimal detector against the worst-case sequences from a generator, using marked point process model.
result Demonstrated good performance on simulations and real credit card fraud datasets.
The paper analyzes how employers can efficiently screen candidates using multiple tests, considering both skill estimation and fairness.
problem How to efficiently screen candidates using multiple noisy signals without violating fairness.
method The paper extends traditional screening models to a multi-test setting, analyzing optimal employer policies for both fixed and dynamic test assignments.
result A fundamental impossibility emerges when noise levels vary across groups, making it impossible to administer the same number of tests and maintain the same outcomes.
We present a novel framework for kernel learning with sequential data of any kind, such as time series, sequences of graphs, or strings. Our approach is based on signature features which can be seen as an ordered variant of sample (cross-)moments; it allows to obtain a "sequentialized" version of any static kernel. The…
Reduces change detection to estimation using confidence sequences.
problem Detecting changes in data streams with minimal delay and false alarms.
method Reduction from sequential change detection to sequential estimation using confidence sequences.
result Change detection scheme with minimal structural assumptions and strong guarantees.
A universal framework for constructing confidence sets using sequential likelihood mixing.
problem Constructing reliable confidence sets for realizable likelihood functions.
method Sequential likelihood mixing, integrating Bayesian inference and regret inequalities.
result Establishes fundamental connections and provable coverage guarantees for various inference techniques.
This study shows how social insects and machine learning methods share a common mathematical framework.
problem Understanding how decentralized systems achieve optimal decision-making.
method Developed a rigorous mathematical framework to show isomorphism between ant colonies and ensemble machine learning.
result Demonstrated that ant colony decision-making and random forest learning implement identical variance reduction strategies through decorrelation of identical units.
New distributed EnKF method for non-sequential assimilation of large datasets.
problem Computational intensity and order dependencies in traditional EnKF.
method Distributed computing for full model error covariance matrix.
result Non-sequential assimilation outperforms sequential in performance.
A neural network model mimics body functions for movement tasks.
problem Solving inverse and forward kinematics, dynamics for a redundant manipulator.
method Recurrent neural network with Mean of Multiple Computations principle, dynamic extension.
result Neural network solves inverse tasks and shows prototypical population-coding.
Paper tackles uncertainty prediction for deep sequential regression.
problem Challenges in generating accurate uncertainty estimates for deep recurrent networks.
method Flexible method that generates symmetric and asymmetric uncertainty estimates without stationarity assumptions.
result Outperforms competitive baselines on both drift and non-drift scenarios.