no code implementations • NeurIPS 2023 • Rachael Hwee Ling Sim, Yehong Zhang, Trong Nghia Hoang, Xinyi Xu, Bryan Kian Hsiang Low, Patrick Jaillet
Finally, the mediator rewards each party with different posterior samples of the model parameters.
no code implementations • 11 Oct 2023 • Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen, Tsui-Wei Weng
The second approach introduces a post-processing method EsbRS which greatly improves the robustness certificate based on building model ensembles.
1 code implementation • 26 Aug 2023 • Alireza Ghods, Trong Nghia Hoang, Diane Cook
Data summarization is the process of generating interpretable and representative subsets from a dataset.
no code implementations • 5 Jun 2023 • Ziwei Fan, Hao Ding, Anoop Deoras, Trong Nghia Hoang
To mitigate this data bottleneck, we postulate that recommendation patterns learned from existing mature market segments (with private data) could be adapted to build effective warm-start models for emerging ones.
1 code implementation • 2 Jun 2023 • Tengfei Ma, Trong Nghia Hoang, Jie Chen
Second, we need to learn a consensus graph that captures the high-order interactions between local feature spaces and how to combine them to achieve a better prediction.
1 code implementation • 19 Jul 2022 • Linbo Liu, Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Jun Huan
This work studies the threats of adversarial attack on multivariate probabilistic forecasting models and viable defense mechanisms.
no code implementations • 18 Mar 2022 • Trong Nghia Hoang, Anoop Deoras, Tong Zhao, Jin Li, George Karypis
We develop and investigate a personalizable deep metric model that captures both the internal contents of items and how they were interacted with by users.
no code implementations • 29 Sep 2021 • Tengfei Ma, Trong Nghia Hoang, Jie Chen
On the top is a federation of the local data representations, performing global inference that incorporates all distributed parts collectively.
no code implementations • 31 May 2021 • Thanh Vinh Vo, Pengfei Wei, Trong Nghia Hoang, Tze-Yun Leong
The proposed method can infer causal effects in the target population without prior knowledge of data discrepancy between the additional data sources and the target.
1 code implementation • 31 May 2021 • Thanh Vinh Vo, Trong Nghia Hoang, Young Lee, Tze-Yun Leong
Many modern applications collect data that comes in federated spirit, with data kept locally and undisclosed.
1 code implementation • NeurIPS 2020 • Quang Minh Hoang, Trong Nghia Hoang, Hai Pham, David P. Woodruff
We introduce a new scalable approximation for Gaussian processes with provable guarantees which hold simultaneously over its entire parameter space.
no code implementations • 21 May 2020 • Cao Xiao, Trong Nghia Hoang, Shenda Hong, Tengfei Ma, Jimeng Sun
There is a growing interest in applying deep learning (DL) to healthcare, driven by the availability of data with multiple feature channels in rich-data environments (e. g., intensive care units).
2 code implementations • 15 Nov 2019 • Kexin Huang, Cao Xiao, Trong Nghia Hoang, Lucas M. Glass, Jimeng Sun
Adverse drug-drug interactions (DDIs) remain a leading cause of morbidity and mortality.
1 code implementation • NeurIPS 2019 • Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Trong Nghia Hoang
We consider the problem of aggregating models learned from sequestered, possibly heterogeneous datasets.
1 code implementation • 28 May 2019 • Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Trong Nghia Hoang, Yasaman Khazaeni
In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited.
1 code implementation • 6 Sep 2018 • Shenda Hong, Cao Xiao, Trong Nghia Hoang, Tengfei Ma, Hongyan Li, Jimeng Sun
In many situations, we need to build and deploy separate models in related environments with different data qualities.
no code implementations • 23 May 2018 • Trong Nghia Hoang, Quang Minh Hoang, Kian Hsiang Low, Jonathan How
Distributed machine learning (ML) is a modern computation paradigm that divides its workload into independent tasks that can be simultaneously achieved by multiple machines (i. e., agents) for better scalability.
no code implementations • 19 Nov 2017 • Trong Nghia Hoang, Quang Minh Hoang, Ruofei Ouyang, Kian Hsiang Low
This paper presents a novel decentralized high-dimensional Bayesian optimization (DEC-HBO) algorithm that, in contrast to existing HBO algorithms, can exploit the interdependent effects of various input components on the output of the unknown objective function f for boosting the BO performance and still preserve scalability in the number of input dimensions without requiring prior knowledge or the existence of a low (effective) dimension of the input space.
no code implementations • 1 Nov 2017 • Haibin Yu, Trong Nghia Hoang, Kian Hsiang Low, Patrick Jaillet
This paper presents a novel variational inference framework for deriving a family of Bayesian sparse Gaussian process regression (SGPR) models whose approximations are variationally optimal with respect to the full-rank GPR model enriched with various corresponding correlation structures of the observation noises.
no code implementations • 17 Oct 2017 • Trong Nghia Hoang, Yuchen Xiao, Kavinayan Sivakumar, Christopher Amato, Jonathan How
The practicality of existing works addressing this challenge is limited to only small-scale synchronous decision-making scenarios or a single agent planning its best response against a single adversary with fixed, procedurally characterized strategies.
no code implementations • 18 Nov 2016 • Quang Minh Hoang, Trong Nghia Hoang, Kian Hsiang Low
While much research effort has been dedicated to scaling up sparse Gaussian process (GP) models based on inducing variables for big data, little attention is afforded to the other less explored class of low-rank GP approximations that exploit the sparse spectral representation of a GP kernel.
1 code implementation • 21 Nov 2015 • Yehong Zhang, Trong Nghia Hoang, Kian Hsiang Low, Mohan Kankanhalli
This paper addresses the problem of active learning of a multi-output Gaussian process (MOGP) model representing multiple types of coexisting correlated environmental phenomena.
1 code implementation • 18 Apr 2013 • Trong Nghia Hoang, Kian Hsiang Low
A key challenge in non-cooperative multi-agent systems is that of developing efficient planning algorithms for intelligent agents to interact and perform effectively among boundedly rational, self-interested agents (e. g., humans).
no code implementations • 7 Apr 2013 • Trong Nghia Hoang, Kian Hsiang Low
Recent advances in Bayesian reinforcement learning (BRL) have shown that Bayes-optimality is theoretically achievable by modeling the environment's latent dynamics using Flat-Dirichlet-Multinomial (FDM) prior.
Multi-agent Reinforcement Learning reinforcement-learning +1