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Found 36 papers, 14 papers with code
An Analysis of Switchback Designs in Reinforcement Learning
Our aim is to thoroughly evaluate the effects of these designs on the accuracy of their resulting average treatment effect (ATE) estimators.
Neural Radiance Fields in Medical Imaging: Challenges and Next Steps
Neural Radiance Fields (NeRF), as a pioneering technique in computer vision, offer great potential to revolutionize medical imaging by synthesizing three-dimensional representations from the projected two-dimensional image data.
Disentangled Latent Energy-Based Style Translation: An Image-Level Structural MRI Harmonization Framework
The SST utilizes an energy-based model to comprehend the global latent distribution of a target domain and translate source latent codes toward the target domain, while SMS enables MRI synthesis with a target-specific style.
Multimodal Clinical Trial Outcome Prediction with Large Language Models
To address these issues, we propose a multimodal mixture-of-experts (LIFTED) approach for clinical trial outcome prediction.
Artificial Intelligence in Image-based Cardiovascular Disease Analysis: A Comprehensive Survey and Future Outlook
Our review encompasses these modalities, giving a broad perspective on the diverse imaging techniques integrated with AI for CVD analysis.
Evaluating the Potential of Leading Large Language Models in Reasoning Biology Questions
Recent advances in Large Language Models (LLMs) have presented new opportunities for integrating Artificial General Intelligence (AGI) into biological research and education.
Controlling Neural Style Transfer with Deep Reinforcement Learning
Controlling the degree of stylization in the Neural Style Transfer (NST) is a little tricky since it usually needs hand-engineering on hyper-parameters.
Evaluating Large Language Models for Radiology Natural Language Processing
The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP).
Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI
Resting-state functional magnetic resonance imaging (rs-fMRI) can reflect spontaneous neural activities in brain and is widely used for brain disorder analysis. Previous studies propose to extract fMRI representations through diverse machine/deep learning methods for subsequent analysis.
Evaluating Dynamic Conditional Quantile Treatment Effects with Applications in Ridesharing
However, assessing dynamic quantile treatment effects (QTE) remains a challenge, particularly when dealing with data from ride-sourcing platforms that involve sequential decision-making across time and space.
Nearest-Neighbor Sampling Based Conditional Independence Testing
The CRT assumes that the conditional distribution of X given Z is known under the null hypothesis and then it is compared to the distribution of the observed samples of the original data.
Source-Free Unsupervised Domain Adaptation: A Survey
Existing UDA approaches highly depend on the accessibility of source domain data, which is usually limited in practical scenarios due to privacy protection, data storage and transmission cost, and computation burden.
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace
We show the effectiveness of GCF by deriving the asymptotic property of the estimator and comparing it to popular uplift modeling methods on both synthetic and real-world datasets.
Statistically Efficient Advantage Learning for Offline Reinforcement Learning in Infinite Horizons
We consider reinforcement learning (RL) methods in offline domains without additional online data collection, such as mobile health applications.
Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process
In this paper, we show that with some auxiliary variables that mediate the effect of actions on the system dynamics, the target policy's value is identifiable in a confounded Markov decision process.
Policy Evaluation for Temporal and/or Spatial Dependent Experiments
The aim of this paper is to establish a causal link between the policies implemented by technology companies and the outcomes they yield within intricate temporal and/or spatial dependent experiments.
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided Markets
In this paper we consider large-scale fleet management in ride-sharing companies that involve multiple units in different areas receiving sequences of products (or treatments) over time.
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation Using Nonparametric Methods
Heterogeneous treatment effect (HTE) estimation with continuous treatment is essential in multiple disciplines, such as the online marketplace and pharmaceutical industry.
A Deep Value-network Based Approach for Multi-Driver Order Dispatching
In this work, we propose a deep reinforcement learning based solution for order dispatching and we conduct large scale online A/B tests on DiDi's ride-dispatching platform to show that the proposed method achieves significant improvement on both total driver income and user experience related metrics.
Value Function is All You Need: A Unified Learning Framework for Ride Hailing Platforms
In this paper we propose a unified value-based dynamic learning framework (V1D3) for tackling both tasks.
Reinforcement Learning for Ridesharing: An Extended Survey
In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement learning approaches to decision optimization problems in a typical ridesharing system.
Real-world Ride-hailing Vehicle Repositioning using Deep Reinforcement Learning
We present a new practical framework based on deep reinforcement learning and decision-time planning for real-world vehicle repositioning on ride-hailing (a type of mobility-on-demand, MoD) platforms.
Graph-Based Equilibrium Metrics for Dynamic Supply-Demand Systems with Applications to Ride-sourcing Platforms
How to dynamically measure the local-to-global spatio-temporal coherence between demand and supply networks is a fundamental task for ride-sourcing platforms, such as DiDi.
Optimization and Control Applications
A REINFORCEMENT LEARNING FRAMEWORK FOR TIME DEPENDENT CAUSAL EFFECTS EVALUATION IN A/B TESTING
A/B testing, or online experiment is a standard business strategy to compare a new product with an old one in pharmaceutical, technological, and traditional industries.
Causal Probabilistic Spatio-temporal Fusion Transformers in Two-sided Ride-Hailing Markets
We conduct a wide range of experiments to demonstrate the interpretability of causal attention, the effectiveness of various model components, and the time efficiency of our CausalTrans.
mFI-PSO: A Flexible and Effective Method in Adversarial Image Generation for Deep Neural Networks
Deep neural networks (DNNs) have achieved great success in image classification, but can be very vulnerable to adversarial attacks with small perturbations to images.
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease.
Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework
A/B testing, or online experiment is a standard business strategy to compare a new product with an old one in pharmaceutical, technological, and traditional industries.
D-GCCA: Decomposition-based Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data
We propose a novel decomposition method for this model, called decomposition-based generalized canonical correlation analysis (D-GCCA).
Graph-Based Semi-Supervised Learning with Non-ignorable Non-response
Graph-based semi-supervised learning is a very powerful tool in classification tasks, while in most existing literature the labelled nodes are assumed to be randomly sampled.
CGT: Clustered Graph Transformer for Urban Spatio-temporal Prediction
Deep learning based approaches have been widely used in various urban spatio-temporal forecasting problems, but most of them fail to account for the unsmoothness issue of urban data in their architecture design, which significantly deteriorates their prediction performance.
LSTOD: Latent Spatial-Temporal Origin-Destination prediction model and its applications in ride-sharing platforms
Origin-Destination (OD) flow data is an important instrument in transportation studies.
Optimal Passenger-Seeking Policies on E-hailing Platforms Using Markov Decision Process and Imitation Learning
Vacant taxi drivers' passenger seeking process in a road network generates additional vehicle miles traveled, adding congestion and pollution into the road network and the environment.
Sensitivity Analysis of Deep Neural Networks
Deep neural networks (DNNs) have achieved superior performance in various prediction tasks, but can be very vulnerable to adversarial examples or perturbations.
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.
Local Region Sparse Learning for Image-on-Scalar Regression
Identification of regions of interest (ROI) associated with certain disease has a great impact on public health.
