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Chang-Tien Lu

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AAAI Conference 2026 Conference Paper

Collaborative LLM Numerical Reasoning with Local Data Protection

  • Min Zhang
  • Yuzhe Lu
  • Yun Zhou
  • Panpan Xu
  • Lin Lee Cheong
  • Chang-Tien Lu
  • Haozhu Wang

Numerical reasoning over documents, which demands both contextual understanding and logical inference, is challenging for low-capacity local models deployed on computation-constrained devices. Although such complex reasoning queries could be routed to powerful remote models like GPT-4, exposing local data raises significant data leakage concerns. Existing mitigation methods generate problem descriptions or examples for remote assistance. However, the inherent complexity of numerical reasoning hinders the local model from generating logically equivalent queries and accurately inferring answers with remote guidance. In this paper, we present a model collaboration framework with two key innovations: (1) a context-aware synthesis strategy that shifts the query topics while preserving reasoning patterns; and (2) a tool-based answer reconstruction approach that reuses the remote-generated plug-and-play solution with code snippets. Experimental results demonstrate that our method achieves better reasoning accuracy than solely using local models while providing stronger data protection than fully relying on remote models. Furthermore, our method improves accuracy by 16.2% - 43.6% while reducing data leakage by 2.3% - 44.6% compared to existing data protection approaches.

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NeurIPS Conference 2024 Conference Paper

DC-Gaussian: Improving 3D Gaussian Splatting for Reflective Dash Cam Videos

  • Linhan Wang
  • Kai Cheng
  • Shuo Lei
  • Shengkun Wang
  • Wei Yin
  • Chenyang Lei
  • Xiaoxiao Long
  • Chang-Tien Lu

We present DC-Gaussian, a new method for generating novel views from in-vehicle dash cam videos. While neural rendering techniques have made significant strides in driving scenarios, existing methods are primarily designed for videos collected by autonomous vehicles. However, these videos are limited in both quantity and diversity compared to dash cam videos, which are more widely used across various types of vehicles and capture a broader range of scenarios. Dash cam videos often suffer from severe obstructions such as reflections and occlusions on the windshields, which significantly impede the application of neural rendering techniques. To address this challenge, we develop DC-Gaussian based on the recent real-time neural rendering technique 3D Gaussian Splatting (3DGS). Our approach includes an adaptive image decomposition module to model reflections and occlusions in a unified manner. Additionally, we introduce illumination-aware obstruction modeling to manage reflections and occlusions under varying lighting conditions. Lastly, we employ a geometry-guided Gaussian enhancement strategy to improve rendering details by incorporating additional geometry priors. Experiments on self-captured and public dash cam videos show that our method not only achieves state-of-the-art performance in novel view synthesis, but also accurately reconstructing captured scenes getting rid of obstructions.

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ICRA Conference 2024 Conference Paper

Learning Decentralized Flocking Controllers with Spatio-Temporal Graph Neural Network

  • Siji Chen
  • Yanshen Sun
  • Peihan Li
  • Lifeng Zhou 0001
  • Chang-Tien Lu

Recently a line of research has delved into the use of graph neural networks (GNNs) for decentralized control in swarm robotics. However, it has been observed that relying solely on the states of immediate neighbors is insufficient to imitate a centralized control policy. To address this limitation, prior studies proposed incorporating L-hop delayed states into the computation. While this approach shows promise, it can lead to a lack of consensus among distant flock members and the formation of small clusters, consequently failing cohesive flocking behaviors. Instead, our approach leverages spatiotemporal GNN, named STGNN that encompasses both spatial and temporal expansions. The spatial expansion collects delayed states from distant neighbors, while the temporal expansion incorporates previous states from immediate neighbors. The broader information gathered from both expansions results in more effective and accurate predictions. We develop an expert algorithm for controlling a swarm of robots and employ imitation learning to train our decentralized STGNN model based on the expert algorithm. We simulate the proposed STGNN approach in various settings, demonstrating its decentralized capacity to emulate the global expert algorithm. Further, we implemented our approach to achieve cohesive flocking, leader following, and obstacle avoidance by a group of Crazyflie drones. The performance of STGNN underscores its potential as an effective and reliable approach for achieving cohesive flocking, leader following, and obstacle avoidance tasks.

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AAAI Conference 2022 Short Paper

Augmentation of Chinese Character Representations with Compositional Graph Learning (Student Abstract)

  • Jason Wang
  • Kaiqun Fu
  • Zhiqian Chen
  • Chang-Tien Lu

Chinese characters have semantic-rich compositional information in radical form. While almost all previous research has applied CNNs to extract this compositional information, our work utilizes deep graph learning on a compact, graph-based representation of Chinese characters. This allows us to exploit temporal information within the strict stroke order used in writing characters. Our results show that our stroke-based model has potential for helping large-scale language models on some Chinese natural language understanding tasks. In particular, we demonstrate that our graph model produces more interpretable embeddings shown through word subtraction analogies and character embedding visualizations.

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AAAI Conference 2022 Short Paper

Blocking Influence at Collective Level with Hard Constraints (Student Abstract)

  • Zonghan Zhang
  • Subhodip Biswas
  • Fanglan Chen
  • Kaiqun Fu
  • Taoran Ji
  • Chang-Tien Lu
  • Naren Ramakrishnan
  • Zhiqian Chen

Influence blocking maximization (IBM) is crucial in many critical real-world problems such as rumors prevention and epidemic containment. The existing work suffers from: (1) concentrating on uniform costs at the individual level, (2) mostly utilizing greedy approaches to approximate optimization, (3) lacking a proper graph representation for influence estimates. To address these issues, this research introduces a neural network model dubbed Neural Influence Blocking (NIB) for improved approximation and enhanced influence blocking effectiveness. The code is available at https: //github. com/oates9895/NIB.

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AAAI Conference 2022 Short Paper

Early Forecast of Traffic Accident Impact Based on a Single-Snapshot Observation (Student Abstract)

  • Guangyu Meng
  • Qisheng Jiang
  • Kaiqun Fu
  • Beiyu Lin
  • Chang-Tien Lu
  • Zhiqian Chen

Predicting and quantifying the impact of traffic accidents is necessary and critical to Intelligent Transport Systems (ITS). As a state-of-the-art technique in graph learning, current graph neural networks heavily rely on graph Fourier transform, assuming homophily among the neighborhood. However, the homophily assumption makes it challenging to characterize abrupt signals such as traffic accidents. Our paper proposes an abrupt graph wavelet network (AGWN) to model traffic accidents and predict their time durations using only one single snapshot.

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AAAI Conference 2022 Short Paper

From “Dynamics on Graphs” to “Dynamics of Graphs”: An Adaptive Echo-State Network Solution (Student Abstract)

  • Lei Zhang
  • Zhiqian Chen
  • Chang-Tien Lu
  • Liang Zhao

Many real-world networks evolve over time, which results in dynamic graphs such as human mobility networks and brain networks. Usually, the “dynamics on graphs” (e. g. , node attribute values evolving) are observable, and may be related to and indicative of the underlying “dynamics of graphs” (e. g. , evolving of the graph topology). Traditional RNN-based methods are not adaptive or scalable for learning the unknown mappings between two types of dynamic graph data. This study presents a AD-ESN, and adaptive echo state network that can automatically learn the best neural network architecture for certain data while keeping the efficiency advantage of echo state networks. We show that AD-ESN can successfully discover the underlying pre-defined mapping function and unknown nonlinear map-ping between time series and graphs.

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AAAI Conference 2021 Conference Paper

Dynamic Multi-Context Attention Networks for Citation Forecasting of Scientific Publications

  • Taoran Ji
  • Nathan Self
  • Kaiqun Fu
  • Zhiqian Chen
  • Naren Ramakrishnan
  • Chang-Tien Lu

Forecasting citations of scientific patents and publications is a crucial task for understanding the evolution and development of technological domains and for foresight into emerging technologies. By construing citations as a time series, the task can be cast into the domain of temporal point processes. Most existing work on forecasting with temporal point processes, both conventional and neural network-based, only performs single-step forecasting. In citation forecasting, however, the more salient goal is n-step forecasting: predicting the arrival time and the technology class of the next n citations. In this paper, we propose Dynamic Multi-Context Attention Networks (DMA-Nets), a novel deep learning sequence-tosequence (Seq2Seq) model with a novel hierarchical dynamic attention mechanism for long-term citation forecasting. Extensive experiments on two real-world datasets demonstrate that the proposed model learns better representations of conditional dependencies over historical sequences compared to state-of-the-art counterparts and thus achieves significant performance for citation predictions.

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AAAI Conference 2020 Conference Paper

Self-Paced Robust Learning for Leveraging Clean Labels in Noisy Data

  • Xuchao Zhang
  • Xian Wu
  • Fanglan Chen
  • Liang Zhao
  • Chang-Tien Lu

The success of training accurate models strongly depends on the availability of a sufficient collection of precisely labeled data. However, real-world datasets contain erroneously labeled data samples that substantially hinder the performance of machine learning models. Meanwhile, well-labeled data is usually expensive to obtain and only a limited amount is available for training. In this paper, we consider the problem of training a robust model by using large-scale noisy data in conjunction with a small set of clean data. To leverage the information contained via the clean labels, we propose a novel self-paced robust learning algorithm (SPRL) that trains the model in a process from more reliable (clean) data instances to less reliable (noisy) ones under the supervision of welllabeled data. The self-paced learning process hedges the risk of selecting corrupted data into the training set. Moreover, theoretical analyses on the convergence of the proposed algorithm are provided under mild assumptions. Extensive experiments on synthetic and real-world datasets demonstrate that our proposed approach can achieve a considerable improvement in effectiveness and robustness to existing methods.

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AAAI Conference 2020 Conference Paper

TapNet: Multivariate Time Series Classification with Attentional Prototypical Network

  • Xuchao Zhang
  • Yifeng Gao
  • Jessica Lin
  • Chang-Tien Lu

With the advance of sensor technologies, the Multivariate Time Series classification (MTSC) problem, perhaps one of the most essential problems in the time series data mining domain, has continuously received a significant amount of attention in recent decades. Traditional time series classification approaches based on Bag-of-Patterns or Time Series Shapelet have difficulty dealing with the huge amounts of feature candidates generated in high-dimensional multivariate data but have promising performance even when the training set is small. In contrast, deep learning based methods can learn low-dimensional features efficiently but suffer from a shortage of labelled data. In this paper, we propose a novel MTSC model with an attentional prototype network to take the strengths of both traditional and deep learning based approaches. Specifically, we design a random group permutation method combined with multi-layer convolutional networks to learn the low-dimensional features from multivariate time series data. To handle the issue of limited training labels, we propose a novel attentional prototype network to train the feature representation based on their distance to class prototypes with inadequate data labels. In addition, we extend our model into its semi-supervised setting by utilizing the unlabeled data. Extensive experiments on 18 datasets in a public UEA Multivariate time series archive with eight state-of-theart baseline methods exhibit the effectiveness of the proposed model.

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IJCAI Conference 2019 Conference Paper

Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks

  • Taoran Ji
  • Zhiqian Chen
  • Nathan Self
  • Kaiqun Fu
  • Chang-Tien Lu
  • Naren Ramakrishnan

Modeling and forecasting forward citations to a patent is a central task for the discovery of emerging technologies and for measuring the pulse of inventive progress. Conventional methods for forecasting these forward citations cast the problem as analysis of temporal point processes which rely on the conditional intensity of previously received citations. Recent approaches model the conditional intensity as a chain of recurrent neural networks to capture memory dependency in hopes of reducing the restrictions of the parametric form of the intensity function. For the problem of patent citations, we observe that forecasting a patent's chain of citations benefits from not only the patent's history itself but also from the historical citations of assignees and inventors associated with that patent. In this paper, we propose a sequence-to-sequence model which employs an attention-of-attention mechanism to capture the dependencies of these multiple time sequences. Furthermore, the proposed model is able to forecast both the timestamp and the category of a patent's next citation. Extensive experiments on a large patent citation dataset collected from USPTO demonstrate that the proposed model outperforms state-of-the-art models at forward citation forecasting.

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IJCAI Conference 2018 Conference Paper

Distributed Self-Paced Learning in Alternating Direction Method of Multipliers

  • Xuchao Zhang
  • Liang Zhao
  • Zhiqian Chen
  • Chang-Tien Lu

Self-paced learning (SPL) mimics the cognitive process of humans, who generally learn from easy samples to hard ones. One key issue in SPL is the training process required for each instance weight depends on the other samples and thus cannot easily be run in a distributed manner in a large-scale dataset. In this paper, we reformulate the self-paced learning problem into a distributed setting and propose a novel Distributed Self-Paced Learning method (DSPL) to handle large scale datasets. Specifically, both the model and instance weights can be optimized in parallel for each batch based on a consensus alternating direction method of multipliers. We also prove the convergence of our algorithm under mild conditions. Extensive experiments on both synthetic and real datasets demonstrate that our approach is superior to those of existing methods.

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IJCAI Conference 2018 Conference Paper

Social Media based Simulation Models for Understanding Disease Dynamics

  • Ting Hua
  • Chandan K Reddy
  • Lei Zhang
  • Lijing Wang
  • Liang Zhao
  • Chang-Tien Lu
  • Naren Ramakrishnan

In this modern era, infectious diseases, such as H1N1, SARS, and Ebola, are spreading much faster than any time in history. Efficient approaches are therefore desired to monitor and track the diffusion of these deadly epidemics. Traditional computational epidemiology models are able to capture the disease spreading trends through contact network, however, one unable to provide timely updates via real-world data. In contrast, techniques focusing on emerging social media platforms can collect and monitor real-time disease data, but do not provide an understanding of the underlying dynamics of ailment propagation. To achieve efficient and accurate real-time disease prediction, the framework proposed in this paper combines the strength of social media mining and computational epidemiology. Specifically, individual health status is first learned from user's online posts through Bayesian inference, disease parameters are then extracted for the computational models at population-level, and the outputs of computational epidemiology model are inversely fed into social media data based models for further performance improvement. In various experiments, our proposed model outperforms current disease forecasting approaches with better accuracy and more stability.

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TIST Journal 2018 Journal Article

Virtual Metering

  • Bingsheng Wang
  • Zhiqian Chen
  • Arnold P. Boedihardjo
  • Chang-Tien Lu

The scarcity of potable water is a critical challenge in many regions around the world. Previous studies have shown that knowledge of device-level water usage can lead to significant conservation. Although there is considerable interest in determining discriminative features via sparse coding for water disaggregation to separate whole-house consumption into its component appliances, existing methods lack a mechanism for fitting coefficient distributions and are thus unable to accurately discriminate parallel devices’ consumption. This article proposes a Bayesian discriminative sparse coding model, referred to as Virtual Metering (VM), for this disaggregation task. Mixture-of-Gammas is employed for the prior distribution of coefficients, contributing two benefits: (i) guaranteeing the coefficients’ sparseness and non-negativity, and (ii) capturing the distribution of active coefficients. The resulting method effectively adapts the bases to aggregated consumption to facilitate discriminative learning in the proposed model, and devices’ shape features are formalized and incorporated into Bayesian sparse coding to direct the learning of basis functions. Compact Gibbs Sampling (CGS) is developed to accelerate the inference process by utilizing the sparse structure of coefficients. The empirical results obtained from applying the new model to large-scale real and synthetic datasets revealed that VM significantly outperformed the benchmark methods.

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IJCAI Conference 2017 Conference Paper

Multimodal Storytelling via Generative Adversarial Imitation Learning

  • Zhiqian Chen
  • Xuchao Zhang
  • Arnold P. Boedihardjo
  • Jing Dai
  • Chang-Tien Lu

Deriving event storylines is an effective summarization method to succinctly organize extensive information, which can significantly alleviate the pain of information overload. The critical challenge is the lack of widely recognized definition of storyline metric. Prior studies have developed various approaches based on different assumptions about users' interests. These works can extract interesting patterns, but their assumptions do not guarantee that the derived patterns will match users' preference. On the other hand, their exclusiveness of single modality source misses cross-modality information. This paper proposes a method, multimodal imitation learning via Generative Adversarial Networks(MIL-GAN), to directly model users' interests as reflected by various data. In particular, the proposed model addresses the critical challenge by imitating users' demonstrated storylines. Our proposed model is designed to learn the reward patterns given user-provided storylines and then applies the learned policy to unseen data. The proposed approach is demonstrated to be capable of acquiring the user's implicit intent and outperforming competing methods by a substantial margin with a user study.

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IJCAI Conference 2017 Conference Paper

Robust Regression via Heuristic Hard Thresholding

  • Xuchao Zhang
  • Liang Zhao
  • Arnold P. Boedihardjo
  • Chang-Tien Lu

The presence of data noise and corruptions recently invokes increasing attention on Robust Least Squares Regression (RLSR), which addresses the fundamental problem that learns reliable regression coefficients when response variables can be arbitrarily corrupted. Until now, several important challenges still cannot be handled concurrently: 1) exact recovery guarantee of regression coefficients 2) difficulty in estimating the corruption ratio parameter; and 3) scalability to massive dataset. This paper proposes a novel Robust Least squares regression algorithm via Heuristic Hard thresholding (RLHH), that concurrently addresses all the above challenges. Specifically, the algorithm alternately optimizes the regression coefficients and estimates the optimal uncorrupted set via heuristic hard thresholding without corruption ratio parameter until it converges. We also prove that our algorithm benefits from strong guarantees analogous to those of state-of-the-art methods in terms of convergence rates and recovery guarantees. We provide empirical evidence to demonstrate that the effectiveness of our new method is superior to that of existing methods in the recovery of both regression coefficients and uncorrupted sets, with very competitive efficiency.

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AAAI Conference 2016 Conference Paper

Topical Analysis of Interactions Between News and Social Media

  • Ting Hua
  • Yue Ning
  • Feng Chen
  • Chang-Tien Lu
  • Naren Ramakrishnan

The analysis of interactions between social media and traditional news streams is becoming increasingly relevant for a variety of applications, including: understanding the underlying factors that drive the evolution of data sources, tracking the triggers behind events, and discovering emerging trends. Researchers have explored such interactions by examining volume changes or information diffusions, however, most of them ignore the semantical and topical relationships between news and social media data. Our work is the first attempt to study how news influences social media, or inversely, based on topical knowledge. We propose a hierarchical Bayesian model that jointly models the news and social media topics and their interactions. We show that our proposed model can capture distinct topics for individual datasets as well as discover the topic influences among multiple datasets. By applying our model to large sets of news and tweets, we demonstrate its significant improvement over baseline methods and explore its power in the discovery of interesting patterns for real world cases.

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IS Journal 2015 Journal Article

Model-Based Forecasting of Significant Societal Events

  • Naren Ramakrishnan
  • Chang-Tien Lu
  • Madhav Marathe
  • Achla Marathe
  • Anil Vullikanti
  • Stephen Eubank
  • Scotland Leman
  • Michael Roan

The article outlines some salient aspects of Embers-generated forecasts through its design considerations, system architecture, and user interface.

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AAAI Conference 2013 Conference Paper

A Generalized Student-t Based Approach to Mixed-Type Anomaly Detection

  • Yen-Cheng Lu
  • Feng Chen
  • Yang Chen
  • Chang-Tien Lu

Anomaly detection for mixed-type data is an important problem that has not been well addressed in the machine learning field. There are two challenging issues for mixed-type datasets, namely modeling mutual correlations between mixed-type attributes and capturing large variations due to anomalies. This paper presents BuffDetect, a robust error buffering approach for anomaly detection in mixed-type datasets. A new variant of the generalized linear model is proposed to model the dependency between mixed-type attributes. The model incorporates an error buffering component based on Student-t distribution to absorb the variations caused by anomalies. However, because of the non- Gaussian design, the problem becomes analytically intractable. We propose a novel Bayesian inference approach, which integrates Laplace approximation and several computational optimizations, and is able to ef- ficiently approximate the posterior of high dimensional latent variables by iteratively updating the latent variables in groups. Extensive experimental evaluations based on 13 benchmark datasets demonstrate the effectiveness and efficiency of BuffDetect.

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IJCAI Conference 2013 Conference Paper

Deep Sparse Coding Based Recursive Disaggregation Model for Water Conservation

  • Haili Dong
  • Bingsheng Wang
  • Chang-Tien Lu

The increasing demands on drinkable water, along with population growth, water-intensive agriculture and economic development, pose critical challenges to water sustainability. New techniques to long-term water conservation that incorporate principles of sustainability are expected. Recent studies have shown that providing customers with usage information of fixtures could help them save a considerable amount of water. Existing disaggregation techniques focus on learning consumption patterns for individual devices. Little attention has been given to the hierarchical decomposition structure of the aggregated consumption. In this paper, a Deep Sparse Coding based Recursive Disaggregation Model (DSCRDM) is proposed for water conservation. We design a recursive decomposition structure to perform the disaggregation task, and introduce sequential set to capture its characteristics. An efficient and effective algorithm deep sparse coding is developed to automatically learn the disaggregation architecture, along with discriminative and reconstruction dictionaries for each layer. We demonstrated that our proposed approach significantly improved the performance of the benchmark methods on a large scale disaggregation task and illustrated how our model could provide practical feedbacks to customers for water conservation.

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