Bryan Kian Hsiang Low | DeepAI

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Featured Co-authors

Roger Wattenhofer
110 publications
Patrick Jaillet
50 publications
Beng Chin Ooi
44 publications
Harold Soh
31 publications
Cheston Tan
21 publications
Zhongxiang Dai
17 publications
Arun Verma
15 publications
Wei Jing
14 publications
Yizhou Chen
14 publications
Quoc Phong Nguyen
10 publications
Shaofeng Cai
10 publications

∙ 05/23/2023

Goat: Fine-tuned LLaMA Outperforms GPT-4 on Arithmetic Tasks

We introduce Goat, a fine-tuned LLaMA model that significantly outperfor...

0 Tiedong Liu, et al. ∙

∙ 01/26/2023

FedHQL: Federated Heterogeneous Q-Learning

Federated Reinforcement Learning (FedRL) encourages distributed agents t...

0 Flint Xiaofeng Fan, et al. ∙

∙ 10/13/2022

Sample-Then-Optimize Batch Neural Thompson Sampling

Bayesian optimization (BO), which uses a Gaussian process (GP) as a surr...

0 Zhongxiang Dai, et al. ∙

∙ 06/19/2022

Bayesian Optimization under Stochastic Delayed Feedback

Bayesian optimization (BO) is a widely-used sequential method for zeroth...

0 Arun Verma, et al. ∙

∙ 06/14/2022

On Provably Robust Meta-Bayesian Optimization

Bayesian optimization (BO) has become popular for sequential optimizatio...

0 Zhongxiang Dai, et al. ∙

∙ 05/28/2022

Federated Neural Bandit

Recent works on neural contextual bandit have achieved compelling perfor...

0 Zhongxiang Dai, et al. ∙

∙ 05/10/2022

Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization

The expected improvement (EI) is one of the most popular acquisition fun...

0 Shouri Hu, et al. ∙

∙ 02/28/2022

Rectified Max-Value Entropy Search for Bayesian Optimization

Although the existing max-value entropy search (MES) is based on the wid...

10 Quoc Phong Nguyen, et al. ∙

∙ 02/28/2022

Markov Chain Monte Carlo-Based Machine Unlearning: Unlearning What Needs to be Forgotten

As the use of machine learning (ML) models is becoming increasingly popu...

0 Quoc Phong Nguyen, et al. ∙

∙ 01/24/2022

Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search

Neural architecture search (NAS) has gained immense popularity owing to ...

0 Yao Shu, et al. ∙

∙ 10/27/2021

Differentially Private Federated Bayesian Optimization with Distributed Exploration

Bayesian optimization (BO) has recently been extended to the federated l...

14 Zhongxiang Dai, et al. ∙

∙ 10/26/2021

Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee

The growing literature of Federated Learning (FL) has recently inspired ...

0 Flint Xiaofeng Fan, et al. ∙

∙ 09/06/2021

Going Beyond Neural Architecture Search with Sampling-based Neural Ensemble Search

Recently, Neural Architecture Search (NAS) has been widely applied to au...

0 Yao Shu, et al. ∙

∙ 09/02/2021

NASI: Label- and Data-agnostic Neural Architecture Search at Initialization

Recent years have witnessed a surging interest in Neural Architecture Se...

0 Yao Shu, et al. ∙

∙ 07/30/2021

Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization

Information-based Bayesian optimization (BO) algorithms have achieved st...

9 Quoc Phong Nguyen, et al. ∙

∙ 05/13/2021

Value-at-Risk Optimization with Gaussian Processes

Value-at-risk (VaR) is an established measure to assess risks in critica...

29 Quoc Phong Nguyen, et al. ∙

∙ 04/17/2021

Convolutional Normalizing Flows for Deep Gaussian Processes

Deep Gaussian processes (DGPs), a hierarchical composition of GP models,...

0 Haibin Yu, et al. ∙

∙ 12/19/2020

An Information-Theoretic Framework for Unifying Active Learning Problems

This paper presents an information-theoretic framework for unifying acti...

11 Quoc Phong Nguyen, et al. ∙

∙ 12/19/2020

Top-k Ranking Bayesian Optimization

This paper presents a novel approach to top-k ranking Bayesian optimizat...

19 Quoc Phong Nguyen, et al. ∙

∙ 11/17/2020

Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization

The problem of inverse reinforcement learning (IRL) is relevant to a var...

7 Sreejith Balakrishnan, et al. ∙

∙ 10/24/2020

Variational Bayesian Unlearning

This paper studies the problem of approximately unlearning a Bayesian mo...

3 Quoc Phong Nguyen, et al. ∙

∙ 10/24/2020

Private Outsourced Bayesian Optimization

This paper presents the private-outsourced-Gaussian process-upper confid...

0 Dmitrii Kharkovskii, et al. ∙

∙ 10/24/2020

Collaborative Machine Learning with Incentive-Aware Model Rewards

Collaborative machine learning (ML) is an appealing paradigm to build hi...

0 Rachael Hwee Ling Sim, et al. ∙

∙ 10/23/2019

A Unifying Framework of Bilinear LSTMs

This paper presents a novel unifying framework of bilinear LSTMs that ca...

0 Mohit Rajpal, et al. ∙