no code implementations • 26 Feb 2024 • Huy N. Chau, Duy Nguyen, Thai Nguyen
This paper investigates short-term behaviors of implied volatility of derivatives written on indexes in equity markets when the index processes are constructed by using a ranking procedure.
no code implementations • 23 Feb 2024 • Duy Nguyen, Bao Nguyen, Viet Anh Nguyen
Algorithmic recourse recommends a cost-efficient action to a subject to reverse an unfavorable machine learning classification decision.
no code implementations • 19 Nov 2023 • Ngoc Bui, Duy Nguyen, Man-Chung Yue, Viet Anh Nguyen
Algorithmic recourse emerges as a prominent technique to promote the explainability, transparency and hence ethics of machine learning models.
no code implementations • 20 Sep 2023 • Son The Nguyen, Theja Tulabandhula, Duy Nguyen
We introduce Dynamic Tiling, a model-agnostic, adaptive, and scalable approach for small object detection, anchored in our inference-data-centric philosophy.
1 code implementation • 22 Feb 2023 • Duy Nguyen, Ngoc Bui, Viet Anh Nguyen
The experimental results show that our method produces a set of recourses that are close to the data manifold while delivering a better cost-diversity trade-off than existing approaches.
1 code implementation • 22 Feb 2023 • Duy Nguyen, Ngoc Bui, Viet Anh Nguyen
To redress this shortcoming, we propose the Distributionally Robust Recourse Action (DiRRAc) framework, which generates a recourse action that has a high probability of being valid under a mixture of model shifts.
no code implementations • 22 Jun 2022 • Tuan-Duy H. Nguyen, Ngoc Bui, Duy Nguyen, Man-Chung Yue, Viet Anh Nguyen
Algorithmic recourse aims to recommend an informative feedback to overturn an unfavorable machine learning decision.
no code implementations • 28 Mar 2022 • Hari Shankar, Adithya Narayan, Shefali Jain, Divya Singh, Pooja Vyas, Nivedita Hegde, Purbayan Kar, Abhi Lad, Jens Thang, Jagruthi Atada, Duy Nguyen, PS Roopa, Akhila Vasudeva, Prathima Radhakrishnan, Sripad Krishna Devalla
Multiple studies have demonstrated that obtaining standardized fetal brain biometry from mid-trimester ultrasonography (USG) examination is key for the reliable assessment of fetal neurodevelopment and the screening of central nervous system (CNS) anomalies.
1 code implementation • ICLR 2022 • Ngoc Bui, Duy Nguyen, Viet Anh Nguyen
Counterfactual explanations are attracting significant attention due to the flourishing applications of machine learning models in consequential domains.
no code implementations • 19 Nov 2021 • Lam Si Tung Ho, Binh T. Nguyen, Vu Dinh, Duy Nguyen
We prove that under the multi-scale Bernstein's condition, the generalized posterior distribution concentrates around the set of optimal hypotheses and the generalized Bayes estimator can achieve fast learning rate.
1 code implementation • 7 Sep 2021 • Huy Q. Vo, Tuong Do, Vi C. Pham, Duy Nguyen, An T. Duong, Quang D. Tran
This paper contributes a new high-quality dataset for hand gesture recognition in hand hygiene systems, named "MFH".
no code implementations • 10 Mar 2020 • Thong Nguyen, Duy Nguyen, Pramod Rao
For several purposes in Natural Language Processing (NLP), such as Information Extraction, Sentiment Analysis or Chatbot, Named Entity Recognition (NER) holds an important role as it helps to determine and categorize entities in text into predefined groups such as the names of persons, locations, quantities, organizations or percentages, etc.
1 code implementation • 8 Aug 2019 • Minh-Ngoc Tran, Dang H. Nguyen, Duy Nguyen
Nonetheless, the development of the existing VB algorithms is so far generally restricted to the case where the variational parameter space is Euclidean, which hinders the potential broad application of VB methods.
no code implementations • NeurIPS 2016 • Vu Dinh, Lam Si Tung Ho, Duy Nguyen, Binh T. Nguyen
We study fast learning rates when the losses are not necessarily bounded and may have a distribution with heavy tails.
no code implementations • 12 Aug 2014 • Vu Dinh, Lam Si Tung Ho, Nguyen Viet Cuong, Duy Nguyen, Binh T. Nguyen
We prove new fast learning rates for the one-vs-all multiclass plug-in classifiers trained either from exponentially strongly mixing data or from data generated by a converging drifting distribution.