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Showing 1–25 of 126 results

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  1. arXiv:2403.11013  [pdf, other

    cs.LG math.ST

    Improved Algorithm and Bounds for Successive Projection

    Authors: Jiashun Jin, Zheng Tracy Ke, Gabriel Moryoussef, Jiajun Tang, Jingming Wang

    Abstract: Given a $K$-vertex simplex in a $d$-dimensional space, suppose we measure $n$ points on the simplex with noise (hence, some of the observed points fall outside the simplex). Vertex hunting is the problem of estimating the $K$ vertices of the simplex. A popular vertex hunting algorithm is successive projection algorithm (SPA). However, SPA is observed to perform unsatisfactorily under strong noise… ▽ More

    Submitted 16 March, 2024; originally announced March 2024.

    Comments: 32 pages, 5 figures

  2. arXiv:2403.10946  [pdf, other

    stat.ML cs.LG

    The Fallacy of Minimizing Local Regret in the Sequential Task Setting

    Authors: Ziping Xu, Kelly W. Zhang, Susan A. Murphy

    Abstract: In the realm of Reinforcement Learning (RL), online RL is often conceptualized as an optimization problem, where an algorithm interacts with an unknown environment to minimize cumulative regret. In a stationary setting, strong theoretical guarantees, like a sublinear ($\sqrt{T}$) regret bound, can be obtained, which typically implies the convergence to an optimal policy and the cessation of explor… ▽ More

    Submitted 16 March, 2024; originally announced March 2024.

  3. arXiv:2403.10799  [pdf, other

    cs.CL cs.AI cs.LG

    Efficient Pruning of Large Language Model with Adaptive Estimation Fusion

    Authors: Jun Liu, Chao Wu, Changdi Yang, Hao Tang, Haoye Dong, Zhenglun Kong, Geng Yuan, Wei Niu, Dong Huang, Yanzhi Wang

    Abstract: Large language models (LLMs) have become crucial for many generative downstream tasks, leading to an inevitable trend and significant challenge to deploy them efficiently on resource-constrained devices. Structured pruning is a widely used method to address this challenge. However, when dealing with the complex structure of the multiple decoder layers, general methods often employ common estimatio… ▽ More

    Submitted 16 March, 2024; originally announced March 2024.

  4. arXiv:2403.10107  [pdf, other

    cs.CV cs.AI cs.MM

    Enhancing Human-Centered Dynamic Scene Understanding via Multiple LLMs Collaborated Reasoning

    Authors: Hang Zhang, Wenxiao Zhang, Haoxuan Qu, Jun Liu

    Abstract: Human-centered dynamic scene understanding plays a pivotal role in enhancing the capability of robotic and autonomous systems, in which Video-based Human-Object Interaction (V-HOI) detection is a crucial task in semantic scene understanding, aimed at comprehensively understanding HOI relationships within a video to benefit the behavioral decisions of mobile robots and autonomous driving systems. A… ▽ More

    Submitted 15 March, 2024; originally announced March 2024.

    Comments: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

  5. arXiv:2403.10013  [pdf, other

    eess.SY cs.LG math.OC

    LyZNet: A Lightweight Python Tool for Learning and Verifying Neural Lyapunov Functions and Regions of Attraction

    Authors: Jun Liu, Yiming Meng, Maxwell Fitzsimmons, Ruikun Zhou

    Abstract: In this paper, we describe a lightweight Python framework that provides integrated learning and verification of neural Lyapunov functions for stability analysis. The proposed tool, named LyZNet, learns neural Lyapunov functions using physics-informed neural networks (PINNs) to solve Zubov's equation and verifies them using satisfiability modulo theories (SMT) solvers. What distinguishes this tool… ▽ More

    Submitted 15 March, 2024; originally announced March 2024.

    Comments: To appear in the 27th ACM International Conference on Hybrid Systems: Computation and Control (HSCC 2024). arXiv admin note: text overlap with arXiv:2312.09131

  6. arXiv:2403.09721  [pdf, other

    cs.CL cs.AI

    A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction

    Authors: Jian Zhang, Changlin Yang, Haiping Zhu, Qika Lin, Fangzhi Xu, Jun Liu

    Abstract: Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from an unstructured document. The advanced approaches on DEAE utilize prompt-based methods to guide pre-trained language models (PLMs) in extracting arguments from input documents. They mainly concentrate on establishing relations between triggers and entity mentions within documents, leaving two u… ▽ More

    Submitted 12 March, 2024; originally announced March 2024.

    Comments: Accepted By Coling 2024

  7. arXiv:2403.05911  [pdf, other

    cs.HC cs.AI

    Towards Optimizing Human-Centric Objectives in AI-Assisted Decision-Making With Offline Reinforcement Learning

    Authors: Zana Buçinca, Siddharth Swaroop, Amanda E. Paluch, Susan A. Murphy, Krzysztof Z. Gajos

    Abstract: As AI assistance is increasingly infused into decision-making processes, we may seek to optimize human-centric objectives beyond decision accuracy, such as skill improvement or task enjoyment of individuals interacting with these systems. With this aspiration in mind, we propose offline reinforcement learning (RL) as a general approach for modeling human-AI decision-making to optimize such human-c… ▽ More

    Submitted 9 March, 2024; originally announced March 2024.

  8. arXiv:2403.05854  [pdf, other

    cs.CV

    LTGC: Long-tail Recognition via Leveraging LLMs-driven Generated Content

    Authors: Qihao Zhao, Yalun Dai, Hao Li, Wei Hu, Fan Zhang, Jun Liu

    Abstract: Long-tail recognition is challenging because it requires the model to learn good representations from tail categories and address imbalances across all categories. In this paper, we propose a novel generative and fine-tuning framework, LTGC, to handle long-tail recognition via leveraging generated content. Firstly, inspired by the rich implicit knowledge in large-scale models (e.g., large language… ▽ More

    Submitted 13 March, 2024; v1 submitted 9 March, 2024; originally announced March 2024.

    Comments: CVPR 2024

  9. arXiv:2403.05018  [pdf, other

    cs.CV

    InstructGIE: Towards Generalizable Image Editing

    Authors: Zichong Meng, Changdi Yang, Jun Liu, Hao Tang, Pu Zhao, Yanzhi Wang

    Abstract: Recent advances in image editing have been driven by the development of denoising diffusion models, marking a significant leap forward in this field. Despite these advances, the generalization capabilities of recent image editing approaches remain constrained. In response to this challenge, our study introduces a novel image editing framework with enhanced generalization robustness by boosting in-… ▽ More

    Submitted 7 March, 2024; originally announced March 2024.

    Comments: Preprint

  10. arXiv:2402.18476  [pdf, other

    cs.CV

    IBD: Alleviating Hallucinations in Large Vision-Language Models via Image-Biased Decoding

    Authors: Lanyun Zhu, Deyi Ji, Tianrun Chen, Peng Xu, Jieping Ye, Jun Liu

    Abstract: Despite achieving rapid developments and with widespread applications, Large Vision-Language Models (LVLMs) confront a serious challenge of being prone to generating hallucinations. An over-reliance on linguistic priors has been identified as a key factor leading to these hallucinations. In this paper, we propose to alleviate this problem by introducing a novel image-biased decoding (IBD) techniqu… ▽ More

    Submitted 28 February, 2024; originally announced February 2024.

  11. arXiv:2402.17739  [pdf, other

    cs.AI cs.LG

    reBandit: Random Effects based Online RL algorithm for Reducing Cannabis Use

    Authors: Susobhan Ghosh, Yongyi Guo, Pei-Yao Hung, Lara Coughlin, Erin Bonar, Inbal Nahum-Shani, Maureen Walton, Susan Murphy

    Abstract: The escalating prevalence of cannabis use, and associated cannabis-use disorder (CUD), poses a significant public health challenge globally. With a notably wide treatment gap, especially among emerging adults (EAs; ages 18-25), addressing cannabis use and CUD remains a pivotal objective within the 2030 United Nations Agenda for Sustainable Development Goals (SDG). In this work, we develop an onlin… ▽ More

    Submitted 27 February, 2024; originally announced February 2024.

  12. arXiv:2402.17003  [pdf, other

    cs.LG cs.AI cs.CY

    Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials

    Authors: Anna L. Trella, Kelly W. Zhang, Inbal Nahum-Shani, Vivek Shetty, Iris Yan, Finale Doshi-Velez, Susan A. Murphy

    Abstract: Online reinforcement learning (RL) algorithms offer great potential for personalizing treatment for participants in clinical trials. However, deploying an online, autonomous algorithm in the high-stakes healthcare setting makes quality control and data quality especially difficult to achieve. This paper proposes algorithm fidelity as a critical requirement for deploying online RL algorithms in cli… ▽ More

    Submitted 26 February, 2024; originally announced February 2024.

  13. arXiv:2402.16978  [pdf, other

    math.OC cs.LG

    An inexact Bregman proximal point method and its acceleration version for unbalanced optimal transport

    Authors: Xiang Chen, Faqiang Wang, Jun Liu, Li Cui

    Abstract: The Unbalanced Optimal Transport (UOT) problem plays increasingly important roles in computational biology, computational imaging and deep learning. Scaling algorithm is widely used to solve UOT due to its convenience and good convergence properties. However, this algorithm has lower accuracy for large regularization parameters, and due to stability issues, small regularization parameters can easi… ▽ More

    Submitted 26 February, 2024; originally announced February 2024.

  14. arXiv:2402.11771  [pdf, other

    cs.LG cs.AI stat.ME stat.ML

    Evaluating the Effectiveness of Index-Based Treatment Allocation

    Authors: Niclas Boehmer, Yash Nair, Sanket Shah, Lucas Janson, Aparna Taneja, Milind Tambe

    Abstract: When resources are scarce, an allocation policy is needed to decide who receives a resource. This problem occurs, for instance, when allocating scarce medical resources and is often solved using modern ML methods. This paper introduces methods to evaluate index-based allocation policies -- that allocate a fixed number of resources to those who need them the most -- by using data from a randomized… ▽ More

    Submitted 18 February, 2024; originally announced February 2024.

  15. arXiv:2402.08563  [pdf, other

    cs.LG cs.CV math.AP

    Denoising Diffusion Restoration Tackles Forward and Inverse Problems for the Laplace Operator

    Authors: Amartya Mukherjee, Melissa M. Stadt, Lena Podina, Mohammad Kohandel, Jun Liu

    Abstract: Diffusion models have emerged as a promising class of generative models that map noisy inputs to realistic images. More recently, they have been employed to generate solutions to partial differential equations (PDEs). However, they still struggle with inverse problems in the Laplacian operator, for instance, the Poisson equation, because the eigenvalues that are large in magnitude amplify the meas… ▽ More

    Submitted 14 February, 2024; v1 submitted 13 February, 2024; originally announced February 2024.

    Comments: 29 pages

  16. arXiv:2402.04933  [pdf, other

    cs.LG stat.AP

    A Bayesian Approach to Online Learning for Contextual Restless Bandits with Applications to Public Health

    Authors: Biyonka Liang, Lily Xu, Aparna Taneja, Milind Tambe, Lucas Janson

    Abstract: Restless multi-armed bandits (RMABs) are used to model sequential resource allocation in public health intervention programs. In these settings, the underlying transition dynamics are often unknown a priori, requiring online reinforcement learning (RL). However, existing methods in online RL for RMABs cannot incorporate properties often present in real-world public health applications, such as con… ▽ More

    Submitted 7 February, 2024; originally announced February 2024.

    Comments: 26 pages, 18 figures

  17. arXiv:2402.04599  [pdf, other

    cs.CV cs.AI cs.LG

    Meet JEANIE: a Similarity Measure for 3D Skeleton Sequences via Temporal-Viewpoint Alignment

    Authors: Lei Wang, Jun Liu, Liang Zheng, Tom Gedeon, Piotr Koniusz

    Abstract: Video sequences exhibit significant nuisance variations (undesired effects) of speed of actions, temporal locations, and subjects' poses, leading to temporal-viewpoint misalignment when comparing two sets of frames or evaluating the similarity of two sequences. Thus, we propose Joint tEmporal and cAmera viewpoiNt alIgnmEnt (JEANIE) for sequence pairs. In particular, we focus on 3D skeleton sequenc… ▽ More

    Submitted 7 February, 2024; originally announced February 2024.

    Comments: Under minor revision with IJCV. An extension of our ACCV'22 paper [arXiv:arXiv:2210.16820] which was distinguished by the Sang Uk Lee Best Student Paper Award. arXiv admin note: text overlap with arXiv:2112.12668

  18. arXiv:2402.03738  [pdf, other

    cs.CV

    AoSRNet: All-in-One Scene Recovery Networks via Multi-knowledge Integration

    Authors: Yuxu Lu, Dong Yang, Yuan Gao, Ryan Wen Liu, Jun Liu, Yu Guo

    Abstract: Scattering and attenuation of light in no-homogeneous imaging media or inconsistent light intensity will cause insufficient contrast and color distortion in the collected images, which limits the developments such as vision-driven smart urban, autonomous vehicles, and intelligent robots. In this paper, we propose an all-in-one scene recovery network via multi-knowledge integration (termed AoSRNet)… ▽ More

    Submitted 6 February, 2024; originally announced February 2024.

  19. arXiv:2402.03110  [pdf, other

    cs.LG cs.AI

    Non-Stationary Latent Auto-Regressive Bandits

    Authors: Anna L. Trella, Walter Dempsey, Finale Doshi-Velez, Susan A. Murphy

    Abstract: We consider the stochastic multi-armed bandit problem with non-stationary rewards. We present a novel formulation of non-stationarity in the environment where changes in the mean reward of the arms over time are due to some unknown, latent, auto-regressive (AR) state of order $k$. We call this new environment the latent AR bandit. Different forms of the latent AR bandit appear in many real-world s… ▽ More

    Submitted 5 February, 2024; originally announced February 2024.

  20. arXiv:2402.01995  [pdf, other

    cs.LG math.OC

    Online Uniform Risk Times Sampling: First Approximation Algorithms, Learning Augmentation with Full Confidence Interval Integration

    Authors: Xueqing Liu, Kyra Gan, Esmaeil Keyvanshokooh, Susan Murphy

    Abstract: In digital health, the strategy of allocating a limited treatment budget across available risk times is crucial to reduce user fatigue. This strategy, however, encounters a significant obstacle due to the unknown actual number of risk times, a factor not adequately addressed by existing methods lacking theoretical guarantees. This paper introduces, for the first time, the online uniform risk times… ▽ More

    Submitted 7 February, 2024; v1 submitted 2 February, 2024; originally announced February 2024.

  21. arXiv:2401.17916  [pdf, other

    cs.CV

    Source-free Domain Adaptive Object Detection in Remote Sensing Images

    Authors: Weixing Liu, Jun Liu, Xin Su, Han Nie, Bin Luo

    Abstract: Recent studies have used unsupervised domain adaptive object detection (UDAOD) methods to bridge the domain gap in remote sensing (RS) images. However, UDAOD methods typically assume that the source domain data can be accessed during the domain adaptation process. This setting is often impractical in the real world due to RS data privacy and transmission difficulty. To address this challenge, we p… ▽ More

    Submitted 31 January, 2024; originally announced January 2024.

    Comments: 14 pages, 11 figures

  22. arXiv:2401.14923  [pdf, other

    cs.AI cs.LG

    Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks

    Authors: Eura Nofshin, Siddharth Swaroop, Weiwei Pan, Susan Murphy, Finale Doshi-Velez

    Abstract: Many important behavior changes are frictionful; they require individuals to expend effort over a long period with little immediate gratification. Here, an artificial intelligence (AI) agent can provide personalized interventions to help individuals stick to their goals. In these settings, the AI agent must personalize rapidly (before the individual disengages) and interpretably, to help us unders… ▽ More

    Submitted 26 January, 2024; originally announced January 2024.

    Comments: In AAMAS 2024

  23. arXiv:2401.13564  [pdf, ps, other

    cs.IT eess.SP

    RIS Empowered Near-Field Covert Communications

    Authors: Jun Liu, Gang Yang, Yuanwei Liu, Xiangyun Zhou

    Abstract: This paper studies an extremely large-scale reconfigurable intelligent surface (XL-RIS) empowered covert communication system in the near-field region. Alice covertly transmits messages to Bob with the assistance of the XL-RIS, while evading detection by Willie. To enhance the covert communication performance, we maximize the achievable covert rate by jointly optimizing the hybrid analog and digit… ▽ More

    Submitted 24 January, 2024; originally announced January 2024.

    Comments: 15 pages, 8 figures, submitted to IEEE journal

  24. arXiv:2401.07188  [pdf, other

    cs.CV

    Left-right Discrepancy for Adversarial Attack on Stereo Networks

    Authors: Pengfei Wang, Xiaofei Hui, Beijia Lu, Nimrod Lilith, Jun Liu, Sameer Alam

    Abstract: Stereo matching neural networks often involve a Siamese structure to extract intermediate features from left and right images. The similarity between these intermediate left-right features significantly impacts the accuracy of disparity estimation. In this paper, we introduce a novel adversarial attack approach that generates perturbation noise specifically designed to maximize the discrepancy bet… ▽ More

    Submitted 13 January, 2024; originally announced January 2024.

  25. arXiv:2401.06795  [pdf, other

    cs.CL cs.AI cs.LG

    AI and Generative AI for Research Discovery and Summarization

    Authors: Mark Glickman, Yi Zhang

    Abstract: AI and generative AI tools, including chatbots like ChatGPT that rely on large language models (LLMs), have burst onto the scene this year, creating incredible opportunities to increase work productivity and improve our lives. Statisticians and data scientists have begun experiencing the benefits from the availability of these tools in numerous ways, such as the generation of programming code from… ▽ More

    Submitted 8 January, 2024; originally announced January 2024.

    Comments: 29 pages, 9 figures