selected publications [full list]

1. PREPRINT Non-Cooperative Backdoor Attacks in Federated Learning: A New Threat Landscape

   Tuan M Nguyen, Dung T Nguyen, Khoa D Doan, and Kok-Seng Wong

   2024

   Abs Bib PDF Code

   Despite the promise of Federated Learning (FL) for privacy-preserving model training on distributed data, it remains susceptible to backdoor attacks. These attacks manipulate models by embedding triggers (specific input patterns) in the training data, forcing misclassification as predefined classes during deployment. Traditional single-trigger attacks and recent work on cooperative multiple-trigger attacks, where clients collaborate, highlight limitations in attack realism due to coordination requirements. We investigate a more alarming scenario: non-cooperative multiple-trigger attacks. Here, independent adversaries introduce distinct triggers targeting unique classes. These parallel attacks exploit FL's decentralized nature, making detection difficult. Our experiments demonstrate the alarming vulnerability of FL to such attacks, where individual backdoors can be successfully learned without impacting the main task. This research emphasizes the critical need for robust defenses against diverse backdoor attacks in the evolving FL landscape. While our focus is on empirical analysis, we believe it can guide backdoor research toward more realistic settings, highlighting the crucial role of FL in building robust defenses against diverse backdoor threats. The code is available at https://anonymous.4open.science/r/nba-980F/.

   @article{nguyen2024noncooperativeBAFL,
     title = {Non-Cooperative Backdoor Attacks in Federated Learning: A New Threat Landscape},
     author = {Nguyen, Tuan M and Nguyen, Dung T and Doan, Khoa D and Wong, Kok-Seng},
     year = {2024},
     bibtex_show = {true},
     abbr = {PREPRINT},
     pdf = {https://arxiv.org/abs/2407.07917},
     code = {https://anonymous.4open.science/r/nba-980F/},
     selected = {true}
   }

2. PREPRINT Less is More: Sparse Watermarking in LLMs with Enhanced Text Quality

   Cao-Duy Hoang, Hung T. Q. Le, Rui Chu, Ping Li, Weijie Zhao, Yingjie Lao, and Khoa D Doan

   2024

   Abs Bib Code

   With the widespread adoption of Large Language Models (LLMs), concerns about potential misuse have emerged. To this end, watermarking has been adapted to LLM, enabling a simple and effective way to detect and monitor generated text. However, while the existing methods can differentiate between watermarked and unwatermarked text with high accuracy, they often face a trade-off between the quality of the generated text and the effectiveness of the watermarking process. In this work, we present a novel type of LLM watermark, \textit{Sparse Watermark}, which aims to mitigate this trade-off by applying watermarks to a small subset of generated tokens distributed across the text. The key strategy involves anchoring watermarked tokens to words that have specific Part-of-Speech (POS) tags. Our experimental results demonstrate that the proposed watermarking scheme achieves high detectability while generating text that outperforms previous LLM watermarking methods in quality across various tasks.

   @article{cao2024sparsellmwatermark,
     title = {Less is More: Sparse Watermarking in LLMs with Enhanced Text Quality},
     author = {Hoang, Cao-Duy and Le, Hung T. Q. and Chu, Rui and Li, Ping and Zhao, Weijie and Lao, Yingjie and Doan, Khoa D},
     year = {2024},
     bibtex_show = {true},
     abbr = {PREPRINT},
     code = {https://github.com/mail-research/sparse-llm-watermarking},
     selected = {true}
   }

3. PREPRINT MetaLLM: A High-performant and Cost-efficient Dynamic Framework for Wrapping LLMs

   Quang H Nguyen, Cao-Duy Hoang, Juliette Decugis, Saurav Manchanda, Nitesh V Chawla, and Khoa D Doan

   2024

   Abs Bib PDF Code

   The rapid progress in machine learning (ML) has brought forth many large language models (LLMs) that excel in various tasks and areas. These LLMs come with different abilities and costs in terms of computation or pricing. Since the demand for each query can vary, e.g., because of the queried domain or its complexity, defaulting to one LLM in an application is not usually the best choice, whether it is the biggest, priciest, or even the one with the best average test performance. Consequently, picking the right LLM that is both accurate and cost-effective for an application remains a challenge. In this paper, we introduce MetaLLM, a framework that dynamically and intelligently routes each query to the optimal LLM (among several available LLMs) for classification tasks, achieving significantly improved accuracy and cost-effectiveness. By framing the selection problem as a multi-armed bandit, MetaLLM balances prediction accuracy and cost efficiency under uncertainty. Our experiments, conducted on popular LLM platforms such as OpenAI's GPT models, Amazon's Titan, Anthropic's Claude, and Meta's LLaMa, showcase MetaLLM's efficacy in real-world scenarios, laying the groundwork for future extensions beyond classification tasks.

   @article{nguyen2024metallm,
     title = {MetaLLM: A High-performant and Cost-efficient Dynamic Framework for Wrapping LLMs},
     author = {Nguyen, Quang H and Hoang, Cao-Duy and Decugis, Juliette and Manchanda, Saurav and Chawla, Nitesh V and Doan, Khoa D},
     year = {2024},
     bibtex_show = {true},
     abbr = {PREPRINT},
     pdf = {https://arxiv.org/abs/2407.10834},
     code = {https://github.com/mail-research/MetaLLM-wrapper/},
     selected = {true}
   }

4. PREPRINT Forget but Recall: Incremental Latent Rectification in Continual Learning

   Nghia D Nguyen, Hieu T Nguyen, Ang Li, Hoang V Pham, Viet Anh Nguyen, and Khoa D Doan

   2024

   Bib PDF Code

   @article{nguyen2024irl,
     title = {Forget but Recall: Incremental Latent Rectification in Continual Learning},
     author = {Nguyen, Nghia D and Nguyen, Hieu T and Li, Ang and Pham, Hoang V and Nguyen, Viet Anh and Doan, Khoa D},
     year = {2024},
     bibtex_show = {true},
     abbr = {PREPRINT},
     pdf = {https://arxiv.org/abs/2312.03419},
     code = {https://github.com/mail-research/IRL-forget-but-recall},
     selected = {true}
   }

5. UAI Cold-start Recommendation by Personalized Embedding Region Elicitation

   Hieu Trung Nguyen, Duy Nguyen, Khoa D Doan, and Viet Anh Nguyen

   In The Conference on Uncertainty in Artificial Intelligence 2023

   Bib PDF Submission History

   @inproceedings{nguyen2024cold,
     title = {Cold-start Recommendation by Personalized Embedding Region Elicitation},
     author = {Nguyen, Hieu Trung and Nguyen, Duy and Doan, Khoa D and Nguyen, Viet Anh},
     booktitle = {The Conference on Uncertainty in Artificial Intelligence},
     year = {2023},
     bibtex_show = {true},
     abbr = {UAI},
     pdf = {https://arxiv.org/pdf/2406.00973},
     submissions = {RecSys'23 -- CIKM'23 -- AAAI'24 -- UAI'24},
     selected = {true}
   }

   RecSys'23 -- CIKM'23 -- AAAI'24 -- UAI'24

6. PREPRINT Synthesizing Physical Backdoor Datasets: An Automated Framework Leveraging Deep Generative Models

   Sze Jue Yang, Chinh D La, Quang H Nguyen, Eugene Bagdasaryan, Kok-Seng Wong, Anh T Tran, Chee Seng Chan, and Khoa D Doan

   2024

   Bib PDF Code

   @article{yang2023synthesizing,
     title = {Synthesizing Physical Backdoor Datasets: An Automated Framework Leveraging Deep Generative Models},
     author = {Yang, Sze Jue and La, Chinh D and Nguyen, Quang H and Bagdasaryan, Eugene and Wong, Kok-Seng and Tran, Anh T and Chan, Chee Seng and Doan, Khoa D},
     year = {2024},
     bibtex_show = {true},
     selected = {true},
     abbr = {PREPRINT},
     code = {https://github.com/mail-research/synthetic-physical-backdoor-datasets},
     pdf = {https://arxiv.org/abs/2312.03419}
   }

7. ACL-Findings Fooling the Textual Fooler via Randomizing Latent Representations

   Cao-Duy Hoang, Quang H Nguyen, Saurav Manchanda, Minlong Peng, Kok-Seng Wong, and Khoa D Doan

   In Findings of the Association for Computational Linguistics 2024

   Bib PDF Code Submission History

   @inproceedings{hoang2023advfooler,
     title = {Fooling the Textual Fooler via Randomizing Latent Representations},
     author = {Hoang, Cao-Duy and Nguyen, Quang H and Manchanda, Saurav and Peng, Minlong and Wong, Kok-Seng and Doan, Khoa D},
     year = {2024},
     abbr = {ACL-Findings},
     bibtex_show = {true},
     booktitle = {Findings of the Association for Computational Linguistics},
     pdf = {https://arxiv.org/abs/2310.01452},
     code = {https://github.com/mail-research/AdvFooler-text-defender},
     submissions = {EMNLP'24 -- ICLR'24 -- ACL'24},
     selected = {true}
   }

   EMNLP'24 -- ICLR'24 -- ACL'24

8. ICLR Understanding the Robustness of Randomized Feature Defense Against Query-Based Adversarial Attacks

   Quang H Nguyen, Yingjie Lao, Tung Pham, Kok-Seng Wong, and Khoa D Doan

   In The Twelfth International Conference on Learning Representations 2024

   Abs Bib PDF Code Submission History

   Recent works have shown that deep neural networks are vulnerable to adversarial examples that find samples close to the original image but can make the model misclassify. Even with access only to the model's output, an attacker can employ black-box attacks to generate such adversarial examples. In this work, we propose a simple and lightweight defense against black-box attacks by adding random noise to hidden features at intermediate layers of the model at inference time. Our theoretical analysis confirms that this method effectively enhances the model's resilience against both score-based and decision-based black-box attacks. Importantly, our defense does not necessitate adversarial training and has minimal impact on accuracy, rendering it applicable to any pre-trained model. Our analysis also reveals the significance of selectively adding noise to different parts of the model based on the gradient of the adversarial objective function, which can be varied during the attack. We demonstrate the robustness of our defense against multiple black-box attacks through extensive empirical experiments involving diverse models with various architectures.

   @inproceedings{nguyen2023randomized,
     title = {Understanding the Robustness of Randomized Feature Defense Against Query-Based Adversarial Attacks},
     author = {Nguyen, Quang H and Lao, Yingjie and Pham, Tung and Wong, Kok-Seng and Doan, Khoa D},
     booktitle = {The Twelfth International Conference on Learning Representations},
     year = {2024},
     bibtex_show = {true},
     abbr = {ICLR},
     pdf = {https://openreview.net/forum?id=vZ6r9GMT1n},
     selected = {true},
     code = {https://github.com/mail-research/randomized_defenses},
     submissions = {NeurIPS'23 -- ICLR'24}
   }

   NeurIPS'23 -- ICLR'24

9. NeurIPS Iba: Towards irreversible backdoor attacks in federated learning

   Thuy Dung Nguyen, Tuan M Nguyen, Anh T Tran, Khoa D Doan, and Kok-Seng Wong

   Advances in Neural Information Processing Systems 2024

   Bib PDF Code Submission History

   @article{nguyen2024iba,
     title = {Iba: Towards irreversible backdoor attacks in federated learning},
     author = {Nguyen, Thuy Dung and Nguyen, Tuan M and Tran, Anh T and Doan, Khoa D and Wong, Kok-Seng},
     journal = {Advances in Neural Information Processing Systems},
     volume = {36},
     year = {2024},
     submissions = {NeurIPS'23},
     bibtex_show = {true},
     abbr = {NeurIPS},
     pdf = {https://proceedings.neurips.cc/paper_files/paper/2023/hash/d0c6bc641a56bebee9d985b937307367-Abstract-Conference.html},
     code = {https://github.com/sail-research/iba},
     selected = {true}
   }

   NeurIPS'23

10. EAAI Backdoor attacks and defenses in federated learning: Survey, challenges and future research directions

    Thuy Dung Nguyen, Tuan M Nguyen, Phi Le Nguyen, Hieu H Pham, Khoa D Doan, and Kok-Seng Wong

    Engineering Applications of Artificial Intelligence 2024

    Bib PDF Submission History

    @article{nguyen2024backdoor,
      title = {Backdoor attacks and defenses in federated learning: Survey, challenges and future research directions},
      author = {Nguyen, Thuy Dung and Nguyen, Tuan M and Le Nguyen, Phi and Pham, Hieu H and Doan, Khoa D and Wong, Kok-Seng},
      journal = {Engineering Applications of Artificial Intelligence},
      volume = {127},
      pages = {107166},
      year = {2024},
      publisher = {Elsevier},
      submissions = {EAAI'24},
      bibtex_show = {true},
      abbr = {EAAI},
      pdf = {https://www.sciencedirect.com/science/article/pii/S0952197623013507},
      selected = {true}
    }

    EAAI'24

11. SIGIR Asymmetric Hashing for Fast Ranking via Neural Network Measures

    Khoa D Doan, Shulong Tan, Weijie Zhao, and Ping Li

    In 46th International ACM SIGIR Conference on Research and Development in Information Retrieval 2023

    Bib PDF Submission History

    @inproceedings{doan2023flora,
      bibtex_show = {true},
      abbr = {SIGIR},
      title = {Asymmetric Hashing for Fast Ranking via Neural Network Measures},
      author = {Doan, Khoa D and Tan, Shulong and Zhao, Weijie and Li, Ping},
      booktitle = {46th International ACM SIGIR Conference on Research and Development in Information Retrieval},
      year = {2023},
      submissions = {SIGIR'21 -- RecSys'21 -- WSDM'22 -- WWW'22 -- SIGIR'22 -- VLDB'23 -- CIKM'22 -- WWW'23 -- SIGIR'23},
      pdf = {https://dl.acm.org/doi/abs/10.1145/3539618.3591640},
      selected = {true}
    }

    SIGIR'21 -- RecSys'21 -- WSDM'22 -- WWW'22 -- SIGIR'22 -- VLDB'23 -- CIKM'22 -- WWW'23 -- SIGIR'23

12. AAAI Defending backdoor attacks on vision transformer via patch processing

    Khoa D Doan, Yingjie Lao, and Ping Li

    In AAAI Conference on Artificial Intelligence 2023

    Bib PDF Submission History

    @inproceedings{doan2023bdvitt,
      abbr = {AAAI},
      title = {Defending backdoor attacks on vision transformer via patch processing},
      author = {Doan, Khoa D and Lao, Yingjie and Li, Ping},
      booktitle = {AAAI Conference on Artificial Intelligence},
      year = {2023},
      submissions = {CVPR'22 -- ICCV'22 -- AAAI'23},
      bibtex_show = {true},
      selected = {true},
      teaser = {doan2023bdvits.png},
      pdf = {https://ojs.aaai.org/index.php/AAAI/article/view/25125}
    }

    CVPR'22 -- ICCV'22 -- AAAI'23

13. NeurIPS Marksman Backdoor: Backdoor Attacks with Arbitrary Target Class

    Khoa D Doan, Yingjie Lao, and Ping Li

    In Thirty-Sixth Conference on Neural Information Processing Systems 2022

    Bib PDF Code Slides Submission History

    @inproceedings{doan2022marksman,
      bibtex_show = {true},
      abbr = {NeurIPS},
      title = {Marksman Backdoor: Backdoor Attacks with Arbitrary Target Class},
      author = {Doan, Khoa D and Lao, Yingjie and Li, Ping},
      booktitle = {Thirty-Sixth Conference on Neural Information Processing Systems},
      year = {2022},
      submissions = {NeurIPS'22},
      teaser = {doan2022marksman.png},
      pdf = {https://openreview.net/forum?id=i-k6J4VkCDq},
      code = {https://github.com/khoadoan106/backdoor_attacks},
      slides = {https://nips.cc/media/neurips-2022/Slides/52924.pdf},
      selected = {true}
    }

    NeurIPS'22

14. CVPR One Loss for Quantization: Deep Hashing with Discrete Wasserstein Distributional Matching

    Khoa D Doan, Peng Yang, and Ping Li

    In Conference on Computer Vision and Pattern Recognition 2022

    Bib PDF Code Slides Submission History

    @inproceedings{doan2022hswd,
      bibtex_show = {true},
      abbr = {CVPR},
      title = {One Loss for Quantization: Deep Hashing with Discrete Wasserstein Distributional Matching},
      author = {Doan, Khoa D and Yang, Peng and Li, Ping},
      booktitle = {Conference on Computer Vision and Pattern Recognition},
      year = {2022},
      submissions = {CVPR'22},
      slides = {doan2022hswd-slides.pdf},
      code = {https://github.com/khoadoan106/single_loss_quantization},
      pdf = {https://openreview.net/pdf?id=uaqweIZ-9_k},
      teaser = {doan2022hswd.png},
      selected = {true}
    }

    CVPR'22

15. NeurIPS Backdoor Attack with Imperceptible Input and Latent Modification

    Khoa D Doan, Yingjie Lao, and Ping Li

    In Thirty-Fifth Conference on Neural Information Processing Systems 2021

    Bib PDF VIDEO Code Slides Submission History

    @inproceedings{doan2021wb,
      bibtex_show = {true},
      abbr = {NeurIPS},
      title = {Backdoor Attack with Imperceptible Input and Latent Modification},
      author = {Doan, Khoa D and Lao, Yingjie and Li, Ping},
      booktitle = {Thirty-Fifth Conference on Neural Information Processing Systems},
      year = {2021},
      submissions = {NeurIPS'21},
      pdf = {https://proceedings.neurips.cc/paper/2021/file/9d99197e2ebf03fc388d09f1e94af89b-Paper.pdf},
      code = {https://github.com/khoadoan106/backdoor_attacks},
      video = {https://recorder-v3.slideslive.com/?share=51522&s=8af881c0-56e8-451e-865f-adb1e90e5471},
      slides = {doan2021wb-slides.pdf},
      teaser = {doan2021wb.png},
      selected = {true}
    }

    NeurIPS'21

16. ICCV LIRA: Learnable, Imperceptible and Robust Backdoor Attacks

    Khoa D Doan, Yingjie Lao, Weijie Zhao, and Ping Li

    In International Conference on Computer Vision 2021

    Abs Bib PDF Code Poster Slides Submission History

    Recently, machine learning models have demonstrated to be vulnerable to backdoor attacks, primarily due to the lack of transparency in black-box models such as deep neural networks. A third-party model can be poisoned such that it works adequately in normal conditions but behaves maliciously on samples with specific trigger patterns. However, the trigger injection function is manually defined in most existing backdoor attack methods, e.g., placing a small patch of pixels on an image or slightly deforming the image before poisoning the model. This results in a two-stage approach with a sub-optimal attack success rate and a lack of complete stealthiness under human inspection. In this paper, we propose a novel and stealthy backdoor attack framework, LIRA, which jointly learns the optimal, stealthy trigger injection function and poisons the model. We formulate such an objective as a non-convex, constrained optimization problem. Under this optimization framework, the trigger generator function will learn to manipulate the input with imperceptible noise to preserve the model performance on the clean data and maximize the attack success rate on the poisoned data. Then, we solve this challenging optimization problem with an efficient, two-stage stochastic optimization procedure. Finally, the proposed attack framework achieves 100% success rates in several benchmark datasets, including MNIST, CIFAR10, GTSRB, and T-ImageNet, while simultaneously bypassing existing backdoor defense methods and human inspection.

    @inproceedings{doan2021lira,
      bibtex_show = {true},
      abbr = {ICCV},
      title = {LIRA: Learnable, Imperceptible and Robust Backdoor Attacks},
      author = {Doan, Khoa D and Lao, Yingjie and Zhao, Weijie and Li, Ping},
      booktitle = {International Conference on Computer Vision},
      submissions = {ICCV'21},
      code = {https://github.com/khoadoan106/backdoor_attacks},
      slides = {https://github.com/sunbelbd/invisible_backdoor_attacks/raw/master/resources/ICCV2021-LIRA-Slides.pdf},
      poster = {https://github.com/sunbelbd/invisible_backdoor_attacks/raw/master/resources/ICCV2021-LIRA-Poster.pdf},
      pdf = {https://openaccess.thecvf.com/content/ICCV2021/papers/Doan_LIRA_Learnable_Imperceptible_and_Robust_Backdoor_Attacks_ICCV_2021_paper.pdf},
      year = {2021},
      teaser = {doan2021lira.png},
      selected = {true}
    }

    ICCV'21

17. SIGIR Interpretable Graph Similarity Computation via Differentiable Optimal Alignment of Node Embeddings

    Khoa D Doan, Saurav Manchanda, Suchismit Mahapatra, and Chandan K Reddy

    In 44th International ACM SIGIR Conference on Research and Development in Information Retrieval 2021

    Abs Bib PDF VIDEO Code Slides Submission History

    Computing graph similarity is an important task in many graph-related applications such as retrieval in graph databases or graph clustering. While numerous measures have been proposed to capture the similarity between a pair of graphs, Graph Edit Distance (GED) and Maximum Common Subgraphs (MCS) are the two widely used measures in practice. GED and MCS are domain-agnostic measures of structural similarity between the graphs and define the similarity as a function of pairwise alignment of different entities (such as nodes, edges, and subgraphs) in the two graphs. The explicit explainability offered by the pairwise alignment provides transparency and justification of the similarity score, thus, GED and MCS have important practical applications. However, their exact computations are known to be NP-hard. While recently proposed neural-network based approximations have been shown to accurately compute these similarity scores, they have limited ability in providing comprehensive explanations compared to classical combinatorial algorithms, e.g., Beam search. This paper aims at efficiently approximating these domain-agnostic similarity measures through a neural network, and simultaneously learning the alignments (i.e., explanations) similar to those of classical intractable methods. Specifically, we formulate the similarity between a pair of graphs as the minimal "transformation" cost from one graph to another in the learnable node-embedding space. We show that, if node embedding is able to capture its neighborhood context closely, our proposed similarity function closely approximates both the alignment and the similarity score of classical methods. Furthermore, we also propose an efficient differentiable computation of our proposed objective for model training. Empirically, we demonstrate that the proposed method achieves up to 50%-100% reduction in the Mean Squared Error for the graph similarity approximation task and up to 20% improvement in the retrieval evaluation metrics for the graph retrieval task. The source code is available at https://github.com/khoadoan/GraphOTSim.

    @inproceedings{doan2021interpretable,
      bibtex_show = {true},
      abbr = {SIGIR},
      url = {https://doi.org/10.1145/3404835.3462960},
      code = {https://github.com/khoadoan/GraphOTSim},
      pdf = {doan2021interpretable.pdf},
      slides = {https://github.com/khoadoan/GraphOTSim/raw/main/resources/SIGIR21-fp0937-slides.pdf},
      video = {https://www.youtube.com/watch?v=IWxxsuFPsgs&t=1s},
      teaser = {doan2021interpretable.png},
      selected = {true},
      submissions = {AAAI'21 -- WWW'21 -- SIGIR'21},
      author = {Doan, Khoa D and Manchanda, Saurav and Mahapatra, Suchismit and Reddy, Chandan K},
      title = {Interpretable Graph Similarity Computation via Differentiable Optimal Alignment of Node Embeddings},
      year = {2021},
      isbn = {9781450380379},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      doi = {10.1145/3404835.3462960},
      booktitle = {44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
      pages = {665–674},
      numpages = {10},
      keywords = {similarity search, model interpretability, graph similarity, GCN},
      location = {Virtual Event, Canada},
      series = {SIGIR '21}
    }

    AAAI'21 -- WWW'21 -- SIGIR'21

18. WWW Efficient Implicit Unsupervised Text Hashing Using Adversarial Autoencoder

    Khoa D Doan, and Chandan K Reddy

    In Proceedings of The Web Conference 2020

    Abs Bib HTML PDF

    Searching for documents with semantically similar content is a fundamental problem in the information retrieval domain with various challenges, primarily, in terms of efficiency and effectiveness. Despite the promise of modeling structured dependencies in documents, several existing text hashing methods lack an efficient mechanism to incorporate such vital information. Additionally, the desired characteristics of an ideal hash function, such as robustness to noise, low quantization error and bit balance/uncorrelation, are not effectively learned with existing methods. This is because of the requirement to either tune additional hyper-parameters or optimize these heuristically and explicitly constructed cost functions. In this paper, we propose a Denoising Adversarial Binary Autoencoder (DABA) model which presents a novel representation learning framework that captures structured representation of text documents in the learned hash function. Also, adversarial training provides an alternative direction to implicitly learn a hash function that captures all the desired characteristics of an ideal hash function. Essentially, DABA adopts a novel single-optimization adversarial training procedure that minimizes the Wasserstein distance in its primal domain to regularize the encoder’s output of either a recurrent neural network or a convolutional autoencoder. We empirically demonstrate the effectiveness of our proposed method in capturing the intrinsic semantic manifold of the related documents. The proposed method outperforms the current state-of-the-art shallow and deep unsupervised hashing methods for the document retrieval task on several prominent document collections.

    @inproceedings{doan2020efficient,
      bibtex_show = {true},
      abbr = {WWW},
      url = {https://doi.org/10.1145/3366423.3380150},
      html = {https://dl.acm.org/doi/abs/10.1145/3366423.3380150},
      pdf = {https://people.cs.vt.edu/~reddy/papers/WWW20a.pdf},
      teaser = {doan2020efficient.png},
      selected = {true},
      author = {Doan, Khoa D and Reddy, Chandan K},
      title = {Efficient Implicit Unsupervised Text Hashing Using Adversarial Autoencoder},
      year = {2020},
      isbn = {9781450370233},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      doi = {10.1145/3366423.3380150},
      booktitle = {Proceedings of The Web Conference},
      pages = {684–694},
      numpages = {11},
      keywords = {autoencoder, Hashing, adversarial training, deep learning.},
      location = {Taipei, Taiwan},
      series = {WWW '20}
    }

19. arXiv Gradient boosting neural networks: Grownet

    Sarkhan Badirli, Xuanqing Liu, Zhengming Xing, Avradeep Bhowmik, Khoa D Doan, and Sathiya K Selvaraj

    arXiv preprint arXiv:2002.07971 2020

    Abs Bib PDF Code

    A novel gradient boosting framework is proposed where shallow neural networks are employed as ``weak learners''. General loss functions are considered under this unified framework with specific examples presented for classification, regression, and learning to rank. A fully corrective step is incorporated to remedy the pitfall of greedy function approximation of classic gradient boosting decision tree. The proposed model rendered outperforming results against state-of-the-art boosting methods in all three tasks on multiple datasets. An ablation study is performed to shed light on the effect of each model components and model hyperparameters.

    @article{badirli2020gradient,
      bibtex_show = {true},
      abbr = {arXiv},
      url = {https://arxiv.org/abs/2002.07971},
      code = {https://github.com/sbadirli/GrowNet},
      pdf = {https://arxiv.org/pdf/2002.07971.pdf},
      teaser = {badirli2020gradient.png},
      selected = {true},
      title = {Gradient boosting neural networks: Grownet},
      author = {Badirli, Sarkhan and Liu, Xuanqing and Xing, Zhengming and Bhowmik, Avradeep and Doan, Khoa D and Selvaraj, Sathiya K},
      journal = {arXiv preprint arXiv:2002.07971},
      year = {2020}
    }

20. CIKM Adversarial Factorization Autoencoder for Look-Alike Modeling

    Khoa D Doan, Pranjul Yadav, and Chandan K Reddy

    In Proceedings of the 28th ACM International Conference on Information and Knowledge Management 2019

    Abs Bib PDF

    Digital advertising is performed in multiple ways, for e.g., contextual, display-based and search-based advertising. Across these avenues, the primary goal of the advertiser is to maximize the return on investment. To realize this, the advertiser often aims to target the advertisements towards a targeted set of audience as this set has a high likelihood to respond positively towards the advertisements. One such form of tailored and personalized, targeted advertising is known as look-alike modeling, where the advertiser provides a set of seed users and expects the machine learning model to identify a new set of users such that the newly identified set is similar to the seed-set with respect to the online purchasing activity. Existing look-alike modeling techniques (i.e., similarity-based and regression-based) suffer from serious limitations due to the implicit constraints induced during modeling. In addition, the high-dimensional and sparse nature of the advertising data increases the complexity. To overcome these limitations, in this paper, we propose a novel Adversarial Factorization Autoencoder that can efficiently learn a binary mapping from sparse, high-dimensional data to a binary address space through the use of an adversarial training procedure. We demonstrate the effectiveness of our proposed approach on a dataset obtained from a real-world setting and also systematically compare the performance of our proposed approach with existing look-alike modeling baselines.

    @inproceedings{doan2019adversarial,
      bibtex_show = {true},
      abbr = {CIKM},
      pdf = {https://dmkd.cs.vt.edu/papers/CIKM19.pdf},
      teaser = {doan2019adversarial.png},
      selected = {true},
      author = {Doan, Khoa D and Yadav, Pranjul and Reddy, Chandan K},
      title = {Adversarial Factorization Autoencoder for Look-Alike Modeling},
      year = {2019},
      isbn = {9781450369763},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      url = {https://doi.org/10.1145/3357384.3357807},
      doi = {10.1145/3357384.3357807},
      booktitle = {Proceedings of the 28th ACM International Conference on Information and Knowledge Management},
      pages = {2803–2812},
      numpages = {10},
      keywords = {deep learning, autoencoder, hashing, factorization, adversarial training, look-alike modeling},
      location = {Beijing, China},
      series = {CIKM '19}
    }