no code implementations • EMNLP (WNUT) 2020 • Linh Doan Bao, Viet Anh Nguyen, Quang Pham Huu

This paper proposes an improved custom model for WNUT task 2: Identification of Informative COVID-19 English Tweet.

no code implementations • 25 Jun 2024 • Nghia D. Nguyen, Hieu Trung Nguyen, Ang Li, Hoang Pham, Viet Anh Nguyen, Khoa D. Doan

Intrinsic capability to continuously learn a changing data stream is a desideratum of deep neural networks (DNNs).

1 code implementation • 4 Jun 2024 • Bao Nguyen, Binh Nguyen, Hieu Trung Nguyen, Viet Anh Nguyen

Learning conditional distributions is challenging because the desired outcome is not a single distribution but multiple distributions that correspond to multiple instances of the covariates.

no code implementations • 3 Jun 2024 • Hieu Trung Nguyen, Duy Nguyen, Khoa Doan, Viet Anh Nguyen

Existing elicitation methods employ a fixed set of items to learn the user's preference and then infer the users' preferences on the remaining items.

no code implementations • 30 May 2024 • Man-Chung Yue, Yves Rychener, Daniel Kuhn, Viet Anh Nguyen

That is, we study distributionally robust covariance estimation problems that minimize the worst-case Frobenius error with respect to all data distributions close to a nominal distribution, where the proximity of distributions is measured via a divergence on the space of covariance matrices.

1 code implementation • 23 May 2024 • Ngoc Bui, Hieu Trung Nguyen, Viet Anh Nguyen, Rex Ying

The Shapley value is a prominent tool for interpreting black-box machine learning models thanks to its strong theoretical foundation.

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.

1 code implementation • 27 Dec 2023 • Bao Nguyen, Binh Nguyen, Viet Anh Nguyen

This paper introduces Bellman Optimal Stepsize Straightening (BOSS) technique for distilling flow-matching generative models: it aims specifically for a few-step efficient image sampling while adhering to a computational budget constraint.

Ranked #1 on Image Generation on CelebA-HQ 256x256 (clean-FID metric)

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.

1 code implementation • 1 Jun 2023 • Bao Nguyen, Viet Anh Nguyen

The recommendation algorithm is conceptualized as a stochastic sampler that, in each round, queries the annotators a subset of samples for their true labels and obtains the feedback information on whether the samples are misclassified.

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.

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.

no code implementations • 31 Jan 2023 • Xinru Hua, Truyen Nguyen, Tam Le, Jose Blanchet, Viet Anh Nguyen

The scarcity of labeled data is a long-standing challenge for many machine learning tasks.

no code implementations • 4 Oct 2022 • Jiajin Li, Sirui Lin, Jose Blanchet, Viet Anh Nguyen

Distributionally robust optimization has been shown to offer a principled way to regularize learning models.

no code implementations • 16 Aug 2022 • Ngoc Bui, Phi Le Nguyen, Viet Anh Nguyen, Phan Thuan Do

We then use a deep neural network to parametrize this charging policy, which will be trained by reinforcement learning techniques.

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.

1 code implementation • 22 Feb 2022 • Tam Le, Truyen Nguyen, Dinh Phung, Viet Anh Nguyen

In this work, we consider probability measures supported on a graph metric space and propose a novel Sobolev transport metric.

no code implementations • ICLR 2022 • Hieu Vu, Toan Tran, Man-Chung Yue, Viet Anh Nguyen

Principal component analysis is a simple yet useful dimensionality reduction technique in modern machine learning pipelines.

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 • 18 Dec 2021 • Viet Anh Nguyen, Soroosh Shafiee, Damir Filipović, Daniel Kuhn

We introduce a universal framework for mean-covariance robust risk measurement and portfolio optimization.

no code implementations • NeurIPS 2021 • Tam Le, Truyen Nguyen, Makoto Yamada, Jose Blanchet, Viet Anh Nguyen

In this paper, we propose a novel and coherent scheme for kernel-reweighted regression by reparametrizing the sample weights using a doubly non-negative matrix.

no code implementations • 29 Sep 2021 • Truyen Nguyen, Xinru Hua, Tam Le, Jose Blanchet, Viet Anh Nguyen

The scarcity of labeled data is a long-standing challenge for cross-domain machine learning tasks.

no code implementations • 4 Aug 2021 • Jose Blanchet, Karthyek Murthy, Viet Anh Nguyen

We consider statistical methods which invoke a min-max distributionally robust formulation to extract good out-of-sample performance in data-driven optimization and learning problems.

no code implementations • SEMEVAL 2021 • Viet Anh Nguyen, Tam Minh Nguyen, Huy Quang Dao, Quang Huu Pham

The SemEval 2021 task 5: Toxic Spans Detection is a task of identifying considered-toxic spans in text, which provides a valuable, automatic tool for moderating online contents.

no code implementations • 2 Jun 2021 • Nian Si, Karthyek Murthy, Jose Blanchet, Viet Anh Nguyen

We present a statistical testing framework to detect if a given machine learning classifier fails to satisfy a wide range of group fairness notions.

1 code implementation • 1 Jun 2021 • Bahar Taskesen, Man-Chung Yue, Jose Blanchet, Daniel Kuhn, Viet Anh Nguyen

Given available data, we investigate novel strategies to synthesize a family of least squares estimator experts that are robust with regard to moment conditions.

1 code implementation • 25 May 2021 • Robbie Vreugdenhil, Viet Anh Nguyen, Armin Eftekhari, Peyman Mohajerin Esfahani

We propose a novel approximation hierarchy for cardinality-constrained, convex quadratic programs that exploits the rank-dominating eigenvectors of the quadratic matrix.

1 code implementation • 30 Mar 2021 • Viet Anh Nguyen, Fan Zhang, Shanshan Wang, Jose Blanchet, Erick Delage, Yinyu Ye

Despite the non-linearity of the objective function in the probability measure, we show that the distributionally robust portfolio allocation with side information problem can be reformulated as a finite-dimensional optimization problem.

no code implementations • 11 Mar 2021 • Yijie Wang, Viet Anh Nguyen, Grani A. Hanasusanto

We propose a distributionally robust classification model with a fairness constraint that encourages the classifier to be fair in view of the equality of opportunity criterion.

no code implementations • 25 Feb 2021 • Jose Blanchet, Fernando Hernandez, Viet Anh Nguyen, Markus Pelger, Xuhui Zhang

Imputation methods in time-series data often are applied to the full panel data with the purpose of training a model for a downstream out-of-sample task.

no code implementations • 24 Feb 2021 • Quang Huu Pham, Viet Anh Nguyen, Linh Bao Doan, Ngoc N. Tran, Ta Minh Thanh

In this paper, we propose a pipeline to adapt the general-purpose RoBERTa language model to a specific text classification task: Vietnamese Hate Speech Detection.

no code implementations • 9 Dec 2020 • Bahar Taskesen, Jose Blanchet, Daniel Kuhn, Viet Anh Nguyen

Leveraging the geometry of the feature space, the test statistic quantifies the distance of the empirical distribution supported on the test samples to the manifold of distributions that render a pre-trained classifier fair.

no code implementations • NeurIPS 2020 • Viet Anh Nguyen, Fan Zhang, Jose Blanchet, Erick Delage, Yinyu Ye

Conditional estimation given specific covariate values (i. e., local conditional estimation or functional estimation) is ubiquitously useful with applications in engineering, social and natural sciences.

1 code implementation • NeurIPS 2020 • Viet Anh Nguyen, Xuhui Zhang, Jose Blanchet, Angelos Georghiou

We consider the parameter estimation problem of a probabilistic generative model prescribed using a natural exponential family of distributions.

no code implementations • 13 Sep 2020 • Jose Blanchet, Yang Kang, Jose Luis Montiel Olea, Viet Anh Nguyen, Xuhui Zhang

This paper shows that dropout training in Generalized Linear Models is the minimax solution of a two-player, zero-sum game where an adversarial nature corrupts a statistician's covariates using a multiplicative nonparametric errors-in-variables model.

no code implementations • 18 Jul 2020 • Bahar Taskesen, Viet Anh Nguyen, Daniel Kuhn, Jose Blanchet

We propose a distributionally robust logistic regression model with an unfairness penalty that prevents discrimination with respect to sensitive attributes such as gender or ethnicity.

1 code implementation • ICML 2020 • Viet Anh Nguyen, Nian Si, Jose Blanchet

The optimistic score searches for the distribution that is most plausible to explain the observed outcomes in the testing sample among all distributions belonging to the contextual ambiguity set which is prescribed using a limited structural constraint on the mean vector and the covariance matrix of the underlying contextual distribution.

1 code implementation • 8 Nov 2019 • Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Daniel Kuhn, Peyman Mohajerin Esfahani

The proposed model can be viewed as a zero-sum game between a statistician choosing an estimator -- that is, a measurable function of the observation -- and a fictitious adversary choosing a prior -- that is, a pair of signal and noise distributions ranging over independent Wasserstein balls -- with the goal to minimize and maximize the expected squared estimation error, respectively.

1 code implementation • NeurIPS 2019 • Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Man-Chung Yue, Daniel Kuhn, Wolfram Wiesemann

The likelihood function is a fundamental component in Bayesian statistics.

1 code implementation • NeurIPS 2019 • Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Man-Chung Yue, Daniel Kuhn, Wolfram Wiesemann

A fundamental problem arising in many areas of machine learning is the evaluation of the likelihood of a given observation under different nominal distributions.

no code implementations • 23 Aug 2019 • Daniel Kuhn, Peyman Mohajerin Esfahani, Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh

The goal of data-driven decision-making is to learn a decision from finitely many training samples that will perform well on unseen test samples.

1 code implementation • NeurIPS 2018 • Soroosh Shafieezadeh-Abadeh, Viet Anh Nguyen, Daniel Kuhn, Peyman Mohajerin Esfahani

Despite the non-convex nature of the ambiguity set, we prove that the estimation problem is equivalent to a tractable convex program.

no code implementations • 18 May 2018 • Viet Anh Nguyen, Daniel Kuhn, Peyman Mohajerin Esfahani

We introduce a distributionally robust maximum likelihood estimation model with a Wasserstein ambiguity set to infer the inverse covariance matrix of a $p$-dimensional Gaussian random vector from $n$ independent samples.

no code implementations • 28 Aug 2016 • Trinh Van Chien, Khanh Quoc Dinh, Viet Anh Nguyen, Byeungwoo Jeon

Total variation has proved its effectiveness in solving inverse problems for compressive sensing.

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