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Found 90 papers, 37 papers with code
On Validation and Planning of An Optimal Decision Rule with Application in Healthcare Studies
Estimation of optimal decision rules (ODR) has been extensively investigated recently, however, at present, no testing procedure is proposed to verify whether these ODRs are significantly better than the naive decision rule that always assigning individuals to a fixed treatment option.
单项形容词定语分布考察及“的”字隐现研究(Study on Distribution of Single Item Adjective Attributives and Appearance and Disappearance of “de”)
“本文以2019-2021年《人民日报》文章中单项形容词定语77845个词例为研究对象, 从实用性的角度考察了粘合式与组合式定语词例的分布特征、音节组配模式及“的”字的隐现倾向性。通过研究我们发现, 粘合式定语的词例数量明显少于组合式定语词例数量, 但使用频数却高出组合式定语的4-5倍。两种定语结构中, 形容词和名词重复使用的比例很高, 但其共现组合的比例偏少, 同时, 真实文本中“的”字的隐现具有“两极分化”的特征, 绝大部分词例在使用过程中带“的”或不带“的”都具有很强的倾向性,“的”字出现具有区分词义和突显信息的作用,“的”字隐藏能促使语义更加凝练, 进一步固化句式结构, 使得某些句式形成了特指或隐喻的表达方式。本文为形容词定语结构的词汇语义研究提供依据和参考。”
TUMTraf V2X Cooperative Perception Dataset
We propose CoopDet3D, a cooperative multi-modal fusion model, and TUMTraf-V2X, a perception dataset, for the cooperative 3D object detection and tracking task.
Collaborative Semantic Occupancy Prediction with Hybrid Feature Fusion in Connected Automated Vehicles
Additionally, due to the lack of a collaborative perception dataset designed for semantic occupancy prediction, we augment a current collaborative perception dataset to include 3D collaborative semantic occupancy labels for a more robust evaluation.
Is Knowledge All Large Language Models Needed for Causal Reasoning?
This paper explores the causal reasoning of large language models (LLMs) to enhance their interpretability and reliability in advancing artificial intelligence.
Large Language Model for Causal Decision Making
Large Language Models (LLMs) have shown their success in language understanding and reasoning on general topics.
TACIT: A Target-Agnostic Feature Disentanglement Framework for Cross-Domain Text Classification
However, these methods rely on unlabeled samples provided by the target domains, which renders the model ineffective when the target domain is agnostic.
Zero-Inflated Bandits
Many real applications of bandits have sparse non-zero rewards, leading to slow learning rates.
Effect Size Estimation for Duration Recommendation in Online Experiments: Leveraging Hierarchical Models and Objective Utility Approaches
The selection of the assumed effect size (AES) critically determines the duration of an experiment, and hence its accuracy and efficiency.
A Survey on Fairness in Large Language Models
Large Language Models (LLMs) have shown powerful performance and development prospects and are widely deployed in the real world.
Pseudo Flow Consistency for Self-Supervised 6D Object Pose Estimation
Most self-supervised 6D object pose estimation methods can only work with additional depth information or rely on the accurate annotation of 2D segmentation masks, limiting their application range.
Automatic Counterfactual Augmentation for Robust Text Classification Based on Word-Group Search
Despite large-scale pre-trained language models have achieved striking results for text classificaion, recent work has raised concerns about the challenge of shortcut learning.
Shape-Constraint Recurrent Flow for 6D Object Pose Estimation
In this work, we propose a shape-constraint recurrent matching framework for 6D object pose estimation.
V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection
Machine learning (ML) has revolutionized transportation systems, enabling autonomous driving and smart traffic services.
FedBEVT: Federated Learning Bird's Eye View Perception Transformer in Road Traffic Systems
Our findings suggest that FedBEVT outperforms the baseline approaches in all four use cases, demonstrating the potential of our approach for improving BEV perception in autonomous driving.
Experimentation Platforms Meet Reinforcement Learning: Bayesian Sequential Decision-Making for Continuous Monitoring
With the growing needs of online A/B testing to support the innovation in industry, the opportunity cost of running an experiment becomes non-negligible.
Rigidity-Aware Detection for 6D Object Pose Estimation
To address this, we propose a rigidity-aware detection method exploiting the fact that, in 6D pose estimation, the target objects are rigid.
V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception
To facilitate the development of cooperative perception, we present V2V4Real, the first large-scale real-world multi-modal dataset for V2V perception.
Multiplier Bootstrap-based Exploration
Despite the great interest in the bandit problem, designing efficient algorithms for complex models remains challenging, as there is typically no analytical way to quantify uncertainty.
A Reinforcement Learning Framework for Dynamic Mediation Analysis
However, there are a number of applications (e. g., mobile health) where the treatments are sequentially assigned over time and the dynamic mediation effects are of primary interest.
On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs
To characterize this heterogeneity, we first conceptualize heterogeneous causal graphs (HCGs) by generalizing the causal graphical model with confounder-based interactions and multiple mediators.
Deep Spectral Q-learning with Application to Mobile Health
Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates.
FCC: Feature Clusters Compression for Long-Tailed Visual Recognition
Through representation learning, DNNs can map BFs into dense clusters in feature space, while the features of minority classes often show sparse clusters.
Efficient Hierarchical Entropy Model for Learned Point Cloud Compression
Learning an accurate entropy model is a fundamental way to remove the redundancy in point cloud compression.
Heterogeneous Synthetic Learner for Panel Data
In the new era of personalization, learning the heterogeneous treatment effect (HTE) becomes an inevitable trend with numerous applications.
Quantile Off-Policy Evaluation via Deep Conditional Generative Learning
Off-Policy evaluation (OPE) is concerned with evaluating a new target policy using offline data generated by a potentially different behavior policy.
An Instrumental Variable Approach to Confounded Off-Policy Evaluation
Off-policy evaluation (OPE) is a method for estimating the return of a target policy using some pre-collected observational data generated by a potentially different behavior policy.
Mining the Factor Zoo: Estimation of Latent Factor Models with Sufficient Proxies
Latent factor model estimation typically relies on either using domain knowledge to manually pick several observed covariates as factor proxies, or purely conducting multivariate analysis such as principal component analysis.
ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals
To address this bottleneck, we introduce a residual-based federated learning framework (ResFed), where residuals rather than model parameters are transmitted in communication networks for training.
Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments
In federated learning, all networked clients contribute to the model training cooperatively.
Few-shot Learning with Class-Covariance Metric for Hyperspectral Image Classification
Recently, embedding and metric-based few-shot learning (FSL) has been introduced into hyperspectral image classification (HSIC) and achieved impressive progress.
Edge-Aided Sensor Data Sharing in Vehicular Communication Networks
Specifically, when the measurement error from the sensors (also referred as measurement noise) is unknown and time varying, the performance of the data fusion process is restricted, which represents a major challenge in the calibration of sensors.
Locally Aggregated Feature Attribution on Natural Language Model Understanding
Some feature attribution methods have shown promising results in computer vision, especially the gradient-based methods where effectively smoothing the gradients with reference data is key to a robust and faithful result.
Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS
Considering the individual heterogeneity of data distribution, computational and communication capabilities across traffic agents and roadside units, we employ a novel method that addresses the heterogeneity of different aggregation layers of the framework architecture, i. e., aggregation in layers of roadside units and cloud.
Semisupervised Cross-scale Graph Prototypical Network for Hyperspectral Image Classification
In practice, the acquirement of labeled samples for hyperspectral image (HSI) is time-consuming and labor-intensive.
Adaptive Semi-Supervised Inference for Optimal Treatment Decisions with Electronic Medical Record Data
Simulation studies are conducted to assess the empirical performance of the proposed method and to compare with a fully supervised method using only the labeled data.
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.
Safe Exploration for Efficient Policy Evaluation and Comparison
High-quality data plays a central role in ensuring the accuracy of policy evaluation.
Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework
In this paper, we propose a unified meta-learning framework for a general class of structured bandit problems where the parameter space can be factorized to item-level.
Exploratory Hidden Markov Factor Models for Longitudinal Mobile Health Data: Application to Adverse Posttraumatic Neuropsychiatric Sequelae
Adverse posttraumatic neuropsychiatric sequelae (APNS) are common among veterans and millions of Americans after traumatic exposures, resulting in substantial burdens for trauma survivors and society.
On Learning and Testing of Counterfactual Fairness through Data Preprocessing
Machine learning has become more important in real-life decision-making but people are concerned about the ethical problems it may bring when used improperly.
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.
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.
Rule Mining over Knowledge Graphs via Reinforcement Learning
Knowledge graphs (KGs) are an important source repository for a wide range of applications and rule mining from KGs recently attracts wide research interest in the KG-related research community.
OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression
In point cloud compression, sufficient contexts are significant for modeling the point cloud distribution.
Reinforcement Learning with Heterogeneous Data: Estimation and Inference
Reinforcement Learning (RL) has the promise of providing data-driven support for decision-making in a wide range of problems in healthcare, education, business, and other domains.
Statistical Learning for Individualized Asset Allocation
We show that our proposed Discretization and Regression with generalized fOlded concaVe penalty on Effect discontinuity (DROVE) approach enjoys desirable theoretical properties and allows for statistical inference of the optimal value associated with optimal decision-making.
HPRN: Holistic Prior-embedded Relation Network for Spectral Super-Resolution
Spectral super-resolution (SSR) refers to the hyperspectral image (HSI) recovery from an RGB counterpart.
SGML: A Symmetric Graph Metric Learning Framework for Efficient Hyperspectral Image Classification
To conquer these issues, we propose an efficient symmetric graph metric learning (SGML) framework by incorporating metric learning into the SSGCN paradigm.
A Review on Graph Neural Network Methods in Financial Applications
With multiple components and relations, financial data are often presented as graph data, since it could represent both the individual features and the complicated relations.
Jump Interval-Learning for Individualized Decision Making
To derive an optimal I2DR, our jump interval-learning method estimates the conditional mean of the outcome given the treatment and the covariates via jump penalized regression, and derives the corresponding optimal I2DR based on the estimated outcome regression function.
A Probit Tensor Factorization Model For Relational Learning
A large number of models and algorithms have been proposed to perform link prediction, among which tensor factorization method has proven to achieve state-of-the-art performance in terms of computation efficiency and prediction accuracy.
Doubly Robust Interval Estimation for Optimal Policy Evaluation in Online Learning
We use this probability to conduct valid inference on the online conditional mean estimator under each action and develop the doubly robust interval estimation (DREAM) method to infer the value under the estimated optimal policy in online learning.
Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models
How to explore efficiently is a central problem in multi-armed bandits.
Label prompt for multi-label text classification
In this paper, we propose a Label Mask multi-label text classification model (LM-MTC), which is inspired by the idea of cloze questions of language model.
Periodic-GP: Learning Periodic World with Gaussian Process Bandits
We consider the sequential decision optimization on the periodic environment, that occurs in a wide variety of real-world applications when the data involves seasonality, such as the daily demand of drivers in ride-sharing and dynamic traffic patterns in transportation.
Pattern Transfer Learning for Reinforcement Learning in Order Dispatching
Order dispatch is one of the central problems to ride-sharing platforms.
Deeply-Debiased Off-Policy Interval Estimation
Off-policy evaluation learns a target policy's value with a historical dataset generated by a different behavior policy.
GEAR: On Optimal Decision Making with Auxiliary Data
We propose an auGmented inverse propensity weighted Experimental and Auxiliary sample-based decision Rule (GEAR) by maximizing the augmented inverse propensity weighted value estimator over a class of decision rules using the experimental sample, with the primary outcome being imputed based on the auxiliary sample.
Calibrated Optimal Decision Making with Multiple Data Sources and Limited Outcome
We consider the optimal decision-making problem in a primary sample of interest with multiple auxiliary sources available.
ASFM-Net: Asymmetrical Siamese Feature Matching Network for Point Completion
We tackle the problem of object completion from point clouds and propose a novel point cloud completion network employing an Asymmetrical Siamese Feature Matching strategy, termed as ASFM-Net.
Topological Regularization for Graph Neural Networks Augmentation
The complexity and non-Euclidean structure of graph data hinder the development of data augmentation methods similar to those in computer vision.
Giant Inverse Rashba-Edelstein Effect: Application to Monolayer OsBi$_2$
We propose that the hybridization between two sets of Rashba bands can lead to an unconventional topology where the two Fermi circles from different bands own in-plane helical spin textures with the same chiralities, and possess group velocities with the same directions.
Materials Science Mesoscale and Nanoscale Physics
ANOCE: Analysis of Causal Effects with Multiple Mediators via Constrained Structural Learning
Under a general causal graph, the exposure may have a direct effect on the outcome and also an indirect effect regulated by a set of mediators.
Counterfactual Fairness through Data Preprocessing
Machine learning has become more important in real-life decision-making but people are concerned about the ethical problems it may bring when used improperly.
GraphCGAN: Convolutional Graph Neural Network with Generative Adversarial Networks
In a number of experiments on benchmark datasets, we show that the proposed GraphCGAN outperforms the baseline methods by a significant margin.
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.
Multi-direction Networks with Attentional Spectral Prior for Hyperspectral Image Classification
Convolutional neural networks (CNNs) have achieved prominent progress in recent years and demonstrated remarkable properties in spectral-spatial hyperspectral image (HSI) classification.
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings
To handle continuous treatments, we develop a novel estimation method for OPE using deep jump learning.
Knowledge-guided Open Attribute Value Extraction with Reinforcement Learning
Effectively filtering out noisy articles as well as bad answers is the key to improving extraction accuracy.
Statistical Inference for Online Decision-Making: In a Contextual Bandit Setting
Based on the properties of the parameter estimators, we further show that the in-sample inverse propensity weighted value estimator is asymptotically normal.
Statistical Inference for Online Decision Making via Stochastic Gradient Descent
Focusing on the statistical inference of online decision making, we establish the asymptotic normality of the parameter estimator produced by our algorithm and the online inverse probability weighted value estimator we used to estimate the optimal value.
Deep Jump Q-Evaluation for Offline Policy Evaluation in Continuous Action Space
To handle continuous action space, we develop a brand-new deep jump Q-evaluation method for OPE.
Multiscale Context-Aware Ensemble Deep KELM for Efficient Hyperspectral Image Classification
Recently, multiscale spatial features have been widely utilized to improve the hyperspectral image (HSI) classification performance.
Multi-Objective Model-based Reinforcement Learning for Infectious Disease Control
Severe infectious diseases such as the novel coronavirus (COVID-19) pose a huge threat to public health.
Kernel Assisted Learning for Personalized Dose Finding
In this article, we propose a kernel assisted learning method for estimating the optimal individualized dose rule.
Deep Prototypical Networks with Hybrid Residual Attention for Hyperspectral Image Classification
Recently, convolutional neural networks (CNNs) have attracted enormous attention in pattern recognition and demonstrated excellent performance in hyperspectral image (HSI) classification.
AdaptiveWeighted Attention Network with Camera Spectral Sensitivity Prior for Spectral Reconstruction from RGB Images
Recent promising effort for spectral reconstruction (SR) focuses on learning a complicated mapping through using a deeper and wider convolutional neural networks (CNNs).
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.
Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making
The Markov assumption (MA) is fundamental to the empirical validity of reinforcement learning.
The USTC-NEL Speech Translation system at IWSLT 2018
This paper describes the USTC-NEL system to the speech translation task of the IWSLT Evaluation 2018.
Neural network-based arithmetic coding of intra prediction modes in HEVC
In this paper, we propose an arithmetic coding strategy by training neural networks, and make preliminary studies on coding of the intra prediction modes in HEVC.
Multimedia
Robust Interpolation of Correspondences for Large Displacement Optical Flow
In this paper, we present a Robust Interpolation method of Correspondences (called RicFlow) to overcome the weakness.
Efficient Coarse-To-Fine PatchMatch for Large Displacement Optical Flow
Inspired by the nearest neighbor field (NNF) algorithms, our approach, called CPM (Coarse-to-fine PatchMatch), blends an efficient random search strategy with the coarse-to-fine scheme for optical flow problem.
A ParaBoost Stereoscopic Image Quality Assessment (PBSIQA) System
The problem of stereoscopic image quality assessment, which finds applications in 3D visual content delivery such as 3DTV, is investigated in this work.
Robust Learning for Optimal Treatment Decision with NP-Dimensionality
In this paper, we propose a two-step estimation procedure for deriving the optimal treatment regime under NP dimensionality.
Sure Screening for Gaussian Graphical Models
We propose {graphical sure screening}, or GRASS, a very simple and computationally-efficient screening procedure for recovering the structure of a Gaussian graphical model in the high-dimensional setting.
Sequential Advantage Selection for Optimal Treatment Regimes
Gunter et al. (2011) proposed S-score which characterizes the magnitude of qualitative interaction of each variable with treatment individually.
MCL-3D: a database for stereoscopic image quality assessment using 2D-image-plus-depth source
A new stereoscopic image quality assessment database rendered using the 2D-image-plus-depth source, called MCL-3D, is described and the performance benchmarking of several known 2D and 3D image quality metrics using the MCL-3D database is presented in this work.
Structured Estimation in Nonparameteric Cox Model
To better understand the interplay of censoring and sparsity we develop finite sample properties of nonparametric Cox proportional hazard's model.
