arXiv subjects

Zijun Gao

Publications and source records attributed to Zijun Gao.

At least 55 records · Page 3Linked to original sources

arXiv2024

Trustworthy assessment of heterogeneous treatment effect estimator

Accurate heterogeneous treatment effect (HTE) estimation is essential for personalized recommendations, making it important to evaluate and compare HTE estimators. Traditional assessment methods are inapplicable due to missing counterfactuals. Current HTE evaluation methods rely on additional estimation or matching on test data, often ignoring the uncertainty introduced and potentially leading to incorrect conclusions. We propose incorporating uncertainty quantification into HTE estimator comparisons. In addition, we suggest shifting the focus to the estimation and inference of the relative error between methods rather than their absolute errors. Methodology-wise, we develop a relative error estimator based on the efficient influence function and establish its asymptotic distribution for inference. Compared to absolute error-based methods, the relative error estimator (1) is less sensitive to the error of nuisance function estimators, satisfying a "global double robustness" property, and (2) its confidence intervals are often narrower, making it more powerful for determining the more accurate HTE estimator. Through extensive empirical study of the ACIC challenge benchmark datasets, we show that the relative error-based method more effectively identifies the better HTE estimator with statistical confidence, even with a moderately large test dataset or inaccurate nuisance estimators.

stat.ME↗

arXiv2024

A constructive approach to selective risk control

Many modern applications require using data to select the statistical tasks and make valid inference after selection. In this article, we provide a unifying approach to control for a class of selective risks. Our method is motivated by a reformulation of the celebrated Benjamini-Hochberg (BH) procedure for multiple hypothesis testing as the fixed point iteration of the Benjamini-Yekutieli (BY) procedure for constructing post-selection confidence intervals. Building on this observation, we propose a constructive approach to control extra-selection risk (where selection is made after decision) by iterating decision strategies that control the post-selection risk (where decision is made after selection). We show that many previous methods and results are special cases of this general framework, and we further extend this approach to problems with multiple selective risks. Our development leads to two surprising results about the BH procedure: (1) in the context of one-sided location testing, the BH procedure not only controls the false discovery rate at the null but also at other locations for free; (2) in the context of permutation tests, the BH procedure with exact permutation p-values can be well approximated by a procedure which only requires a total number of permutations that is almost linear in the total number of hypotheses.

stat.ME↗

arXiv2024

Time Delay Anomalies of Fuzzy Gravitational Lenses

Fuzzy dark matter is a promising alternative to the standard cold dark matter. It has quite recently been noticed, that they can not only successfully explain the large-scale structure in the Universe, but can also solve problems of position and flux anomalies in galaxy strong lensing systems. In this paper we focus on the perturbation of time delays in strong lensing systems caused by fuzzy dark matter, thus making an important extension of previous works. We select a specific system HS 0810+2554 for the study of time delay anomalies. Then, based on the nature of the fuzzy dark matter fluctuations, we obtain theoretical relationship between the magnitude of the perturbation caused by fuzzy dark matter, its content in the galaxy, and its de Broglie wavelength $λ_{\mathrm{dB}}$. It turns out that, the perturbation of strong lensing time delays due to fuzzy dark matter quantified as standard deviation is $σ_{Δt} \propto λ_{\mathrm{dB}}^{1.5}$. We also verify our results through simulations. Relatively strong fuzzy dark matter fluctuations in a lensing galaxy make it possible to to destroy the topological structure of the lens system and change the arrival order between the saddle point and the minimum point in the time delays surface. Finally, we stress the unique opportunity for studying properties of fuzzy dark matter created by possible precise time delay measurements from strongly lensed transients like fast radio bursts, supernovae or gravitational wave signals.

astro-ph.GA↗

arXiv2024

Bridging multiple worlds: multi-marginal optimal transport for causal partial-identification problem

Under the prevalent potential outcome model in causal inference, each unit is associated with multiple potential outcomes but at most one of which is observed, leading to many causal quantities being only partially identified. The inherent missing data issue echoes the multi-marginal optimal transport (MOT) problem, where marginal distributions are known, but how the marginals couple to form the joint distribution is unavailable. In this paper, we cast the causal partial identification problem in the framework of MOT with $K$ margins and $d$-dimensional outcomes and obtain the exact partial identified set. In order to estimate the partial identified set via MOT, statistically, we establish a convergence rate of the plug-in MOT estimator for the $\ell_2$ cost function stemming from the variance minimization problem and prove it is minimax optimal for arbitrary $K$ and $d \le 4$. We also extend the convergence result to general quadratic objective functions. Numerically, we demonstrate the efficacy of our method over synthetic datasets and several real-world datasets where our proposal consistently outperforms the baseline by a significant margin (over 70%). In addition, we provide efficient off-the-shelf implementations of MOT with general objective functions.

stat.ME↗

arXiv2024

Enhancing Convolutional Neural Networks with Higher-Order Numerical Difference Methods

With the rise of deep learning technology in practical applications, Convolutional Neural Networks (CNNs) have been able to assist humans in solving many real-world problems. To enhance the performance of CNNs, numerous network architectures have been explored. Some of these architectures are designed based on the accumulated experience of researchers over time, while others are designed through neural architecture search methods. The improvements made to CNNs by the aforementioned methods are quite significant, but most of the improvement methods are limited in reality by model size and environmental constraints, making it difficult to fully realize the improved performance. In recent years, research has found that many CNN structures can be explained by the discretization of ordinary differential equations. This implies that we can design theoretically supported deep network structures using higher-order numerical difference methods. It should be noted that most of the previous CNN model structures are based on low-order numerical methods. Therefore, considering that the accuracy of linear multi-step numerical difference methods is higher than that of the forward Euler method, this paper proposes a stacking scheme based on the linear multi-step method. This scheme enhances the performance of ResNet without increasing the model size and compares it with the Runge-Kutta scheme. The experimental results show that the performance of the stacking scheme proposed in this paper is superior to existing stacking schemes (ResNet and HO-ResNet), and it has the capability to be extended to other types of neural networks.

cs.LG↗

arXiv2024

Comprehensive Review and Empirical Evaluation of Causal Discovery Algorithms for Numerical Data

Causal analysis has become an essential component in understanding the underlying causes of phenomena across various fields. Despite its significance, existing literature on causal discovery algorithms is fragmented, with inconsistent methodologies, i.e., there is no universal classification standard for existing methods, and a lack of comprehensive evaluations, i.e., data characteristics are often ignored to be jointly analyzed when benchmarking algorithms. This study addresses these gaps by conducting an exhaustive review and empirical evaluation for causal discovery methods on numerical data, aiming to provide a clearer and more structured understanding of the field. Our research begins with a comprehensive literature review spanning over two decades, analyzing over 200 academic articles and identifying more than 40 representative algorithms. This extensive analysis leads to the development of a structured taxonomy tailored to the complexities of causal discovery, categorizing methods into six main types. To address the lack of comprehensive evaluations, our study conducts an extensive empirical assessment of 29 causal discovery algorithms on multiple synthetic and real-world datasets. We categorize synthetic datasets based on size, linearity, and noise distribution, employing five evaluation metrics, and summarize the top-3 algorithm recommendations, providing guidelines for users in various data scenarios. Our results highlight a significant impact of dataset characteristics on algorithm performance. Moreover, a metadata extraction strategy with an accuracy exceeding 80% is developed to assist users in algorithm selection on unknown datasets. Based on these insights, we offer professional and practical guidelines to help users choose the most suitable causal discovery methods for their specific dataset.

cs.AI↗

arXiv2024

Algorithm Research of ELMo Word Embedding and Deep Learning Multimodal Transformer in Image Description

Zero sample learning is an effective method for data deficiency. The existing embedded zero sample learning methods only use the known classes to construct the embedded space, so there is an overfitting of the known classes in the testing process. This project uses category semantic similarity measures to classify multiple tags. This enables it to incorporate unknown classes that have the same meaning as currently known classes into the vector space when it is built. At the same time, most of the existing zero sample learning algorithms directly use the depth features of medical images as input, and the feature extraction process does not consider semantic information. This project intends to take ELMo-MCT as the main task and obtain multiple visual features related to the original image through self-attention mechanism. In this paper, a large number of experiments are carried out on three zero-shot learning reference datasets, and the best harmonic average accuracy is obtained compared with the most advanced algorithms.

cs.CV↗

arXiv2024

An Uncertainty-guided Tiered Self-training Framework for Active Source-free Domain Adaptation in Prostate Segmentation

Deep learning models have exhibited remarkable efficacy in accurately delineating the prostate for diagnosis and treatment of prostate diseases, but challenges persist in achieving robust generalization across different medical centers. Source-free Domain Adaptation (SFDA) is a promising technique to adapt deep segmentation models to address privacy and security concerns while reducing domain shifts between source and target domains. However, recent literature indicates that the performance of SFDA remains far from satisfactory due to unpredictable domain gaps. Annotating a few target domain samples is acceptable, as it can lead to significant performance improvement with a low annotation cost. Nevertheless, due to extremely limited annotation budgets, careful consideration is needed in selecting samples for annotation. Inspired by this, our goal is to develop Active Source-free Domain Adaptation (ASFDA) for medical image segmentation. Specifically, we propose a novel Uncertainty-guided Tiered Self-training (UGTST) framework, consisting of efficient active sample selection via entropy-based primary local peak filtering to aggregate global uncertainty and diversity-aware redundancy filter, coupled with a tiered self-learning strategy, achieves stable domain adaptation. Experimental results on cross-center prostate MRI segmentation datasets revealed that our method yielded marked advancements, with a mere 5% annotation, exhibiting an average Dice score enhancement of 9.78% and 7.58% in two target domains compared with state-of-the-art methods, on par with fully supervised learning. Code is available at:https://github.com/HiLab-git/UGTST

cs.CV↗

arXiv2024

Gradient-based Parameter Selection for Efficient Fine-Tuning

With the growing size of pre-trained models, full fine-tuning and storing all the parameters for various downstream tasks is costly and infeasible. In this paper, we propose a new parameter-efficient fine-tuning method, Gradient-based Parameter Selection (GPS), demonstrating that only tuning a few selected parameters from the pre-trained model while keeping the remainder of the model frozen can generate similar or better performance compared with the full model fine-tuning method. Different from the existing popular and state-of-the-art parameter-efficient fine-tuning approaches, our method does not introduce any additional parameters and computational costs during both the training and inference stages. Another advantage is the model-agnostic and non-destructive property, which eliminates the need for any other design specific to a particular model. Compared with the full fine-tuning, GPS achieves 3.33% (91.78% vs. 88.45%, FGVC) and 9.61% (73.1% vs. 65.57%, VTAB) improvement of the accuracy with tuning only 0.36% parameters of the pre-trained model on average over 24 image classification tasks; it also demonstrates a significant improvement of 17% and 16.8% in mDice and mIoU, respectively, on medical image segmentation task. Moreover, GPS achieves state-of-the-art performance compared with existing PEFT methods.

cs.CV↗

arXiv2024

Deep Learning Powered Estimate of The Extrinsic Parameters on Unmanned Surface Vehicles

Unmanned Surface Vehicles (USVs) are pivotal in marine exploration, but their sensors' accuracy is compromised by the dynamic marine environment. Traditional calibration methods fall short in these conditions. This paper introduces a deep learning architecture that predicts changes in the USV's dynamic metacenter and refines sensors' extrinsic parameters in real time using a Time-Sequence General Regression Neural Network (GRNN) with Euler angles as input. Simulation data from Unity3D ensures robust training and testing. Experimental results show that the Time-Sequence GRNN achieves the lowest mean squared error (MSE) loss, outperforming traditional neural networks. This method significantly enhances sensor calibration for USVs, promising improved data accuracy in challenging maritime conditions. Future work will refine the network and validate results with real-world data.

cs.RO↗

arXiv2024

An Enhanced Encoder-Decoder Network Architecture for Reducing Information Loss in Image Semantic Segmentation

The traditional SegNet architecture commonly encounters significant information loss during the sampling process, which detrimentally affects its accuracy in image semantic segmentation tasks. To counter this challenge, we introduce an innovative encoder-decoder network structure enhanced with residual connections. Our approach employs a multi-residual connection strategy designed to preserve the intricate details across various image scales more effectively, thus minimizing the information loss inherent to down-sampling procedures. Additionally, to enhance the convergence rate of network training and mitigate sample imbalance issues, we have devised a modified cross-entropy loss function incorporating a balancing factor. This modification optimizes the distribution between positive and negative samples, thus improving the efficiency of model training. Experimental evaluations of our model demonstrate a substantial reduction in information loss and improved accuracy in semantic segmentation. Notably, our proposed network architecture demonstrates a substantial improvement in the finely annotated mean Intersection over Union (mIoU) on the dataset compared to the conventional SegNet. The proposed network structure not only reduces operational costs by decreasing manual inspection needs but also scales up the deployment of AI-driven image analysis across different sectors.

eess.IV↗

arXiv2024

TD3 Based Collision Free Motion Planning for Robot Navigation

This paper addresses the challenge of collision-free motion planning in automated navigation within complex environments. Utilizing advancements in Deep Reinforcement Learning (DRL) and sensor technologies like LiDAR, we propose the TD3-DWA algorithm, an innovative fusion of the traditional Dynamic Window Approach (DWA) with the Twin Delayed Deep Deterministic Policy Gradient (TD3). This hybrid algorithm enhances the efficiency of robotic path planning by optimizing the sampling interval parameters of DWA to effectively navigate around both static and dynamic obstacles. The performance of the TD3-DWA algorithm is validated through various simulation experiments, demonstrating its potential to significantly improve the reliability and safety of autonomous navigation systems.

cs.RO↗

arXiv2024

Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks

The internal structure and operation mechanism of large-scale language models are analyzed theoretically, especially how Transformer and its derivative architectures can restrict computing efficiency while capturing long-term dependencies. Further, we dig deep into the efficiency bottleneck of the training phase, and evaluate in detail the contribution of adaptive optimization algorithms (such as AdamW), massively parallel computing techniques, and mixed precision training strategies to accelerate convergence and reduce memory footprint. By analyzing the mathematical principles and implementation details of these algorithms, we reveal how they effectively improve training efficiency in practice. In terms of model deployment and inference optimization, this paper systematically reviews the latest advances in model compression techniques, focusing on strategies such as quantification, pruning, and knowledge distillation. By comparing the theoretical frameworks of these techniques and their effects in different application scenarios, we demonstrate their ability to significantly reduce model size and inference delay while maintaining model prediction accuracy. In addition, this paper critically examines the limitations of current efficiency optimization methods, such as the increased risk of overfitting, the control of performance loss after compression, and the problem of algorithm generality, and proposes some prospects for future research. In conclusion, this study provides a comprehensive theoretical framework for understanding the efficiency optimization of large-scale language models.

cs.LG↗

arXiv2024

Reinforcement Learning Approach for Integrating Compressed Contexts into Knowledge Graphs

The widespread use of knowledge graphs in various fields has brought about a challenge in effectively integrating and updating information within them. When it comes to incorporating contexts, conventional methods often rely on rules or basic machine learning models, which may not fully grasp the complexity and fluidity of context information. This research suggests an approach based on reinforcement learning (RL), specifically utilizing Deep Q Networks (DQN) to enhance the process of integrating contexts into knowledge graphs. By considering the state of the knowledge graph as environment states defining actions as operations for integrating contexts and using a reward function to gauge the improvement in knowledge graph quality post-integration, this method aims to automatically develop strategies for optimal context integration. Our DQN model utilizes networks as function approximators, continually updating Q values to estimate the action value function, thus enabling effective integration of intricate and dynamic context information. Initial experimental findings show that our RL method outperforms techniques in achieving precise context integration across various standard knowledge graph datasets, highlighting the potential and effectiveness of reinforcement learning in enhancing and managing knowledge graphs.

cs.AI↗

arXiv2024

Deep Reinforcement Learning for Mobile Robot Path Planning

Path planning is an important problem with the the applications in many aspects, such as video games, robotics etc. This paper proposes a novel method to address the problem of Deep Reinforcement Learning (DRL) based path planning for a mobile robot. We design DRL-based algorithms, including reward functions, and parameter optimization, to avoid time-consuming work in a 2D environment. We also designed an Two-way search hybrid A* algorithm to improve the quality of local path planning. We transferred the designed algorithm to a simple embedded environment to test the computational load of the algorithm when running on a mobile robot. Experiments show that when deployed on a robot platform, the DRL-based algorithm in this article can achieve better planning results and consume less computing resources.

cs.RO↗

arXiv2023

Adaptive Storey's null proportion estimator

False discovery rate (FDR) is a commonly used criterion in multiple testing and the Benjamini-Hochberg (BH) procedure is arguably the most popular approach with FDR guarantee. To improve power, the adaptive BH procedure has been proposed by incorporating various null proportion estimators, among which Storey's estimator has gained substantial popularity. The performance of Storey's estimator hinges on a critical hyper-parameter, where a pre-fixed configuration lacks power and existing data-driven hyper-parameters compromise the FDR control. In this work, we propose a novel class of adaptive hyper-parameters and establish the FDR control of the associated BH procedure using a martingale argument. Within this class of data-driven hyper-parameters, we present a specific configuration designed to maximize the number of rejections and characterize the convergence of this proposal to the optimal hyper-parameter under a commonly-used mixture model. We evaluate our adaptive Storey's null proportion estimator and the associated BH procedure on extensive simulated data and a motivating protein dataset. Our proposal exhibits significant power gains when dealing with a considerable proportion of weak non-nulls or a conservative null distribution.

stat.ME↗

arXiv2023

Identifying Strongly Lensed Gravitational Waves with the Third-generation Detectors

The joint detection of GW signals by a network of instruments will increase the detecting ability of faint and far GW signals with higher signal-to-noise ratios (SNRs), which could improve the ability of detecting the lensed GWs as well, especially for the 3rd generation detectors, e.g. Einstein Telescope (ET) and Cosmic Explorer (CE). However, identifying Strongly Lensed Gravitational Waves (SLGWs) is still challenging. We focus on the identification ability of 3G detectors in this article. We predict and analyze the SNR distribution of SLGW signals and prove only 50.6\% of SLGW pairs detected by ET alone can be identified by Lens Bayes factor (LBF), which is a popular method at present to identify SLGWs. For SLGW pairs detected by CE\&ET network, owing to the superior spatial resolution, this number rises to 87.3\%. Moreover, we get an approximate analytical relation between SNR and LBF. We give clear SNR limits to identify SLGWs and estimate the expected yearly detection rates of galaxy-scale lensed GWs that can get identified with 3G detector network.

astro-ph.IM↗

arXiv2023

Simultaneous Hypothesis Testing Using Internal Negative Controls with An Application to Proteomics

Negative control is a common technique in scientific investigations and broadly refers to the situation where a null effect (''negative result'') is expected. Motivated by a real proteomic dataset, we will present three promising and closely connected methods of using negative controls to assist simultaneous hypothesis testing. The first method uses negative controls to construct a permutation p-value for every hypothesis under investigation, and we give several sufficient conditions for such p-values to be valid and positive regression dependent on the set (PRDS) of true nulls. The second method uses negative controls to construct an estimate of the false discovery rate (FDR), and we give a sufficient condition under which the step-up procedure based on this estimate controls the FDR. The third method, derived from an existing ad hoc algorithm for proteomic analysis, uses negative controls to construct a nonparametric estimator of the local false discovery rate. We conclude with some practical suggestions and connections to some closely related methods that are propsed recently.

stat.ME↗