367 citations · 1.3k across the 62 of their papers we have counts for
8 papers · 1 filter
Scalable Transfer Evolutionary Optimization: Coping with Big Task Instances
Mojtaba Shakeri, Erfan Miahi, Abhishek Gupta +1
In today's digital world, we are faced with an explosion of data and models produced and manipulated by numerous large-scale cloud-based applications. Under such settings, existing…
Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder
Alvin Chan, Yi Tay, Yew-Soon Ong +1
This paper demonstrates a fatal vulnerability in natural language inference (NLI) and text classification systems. More concretely, we present a 'backdoor poisoning' attack on NLP…
Modulating Scalable Gaussian Processes for Expressive Statistical Learning
Haitao Liu, Yew-Soon Ong, Xiaomo Jiang +1
For a learning task, Gaussian process (GP) is interested in learning the statistical relationship between inputs and outputs, since it offers not only the prediction mean but also…
Adversary Agnostic Robust Deep Reinforcement Learning
Xinghua Qu, Yew-Soon Ong, Abhishek Gupta +1
Deep reinforcement learning (DRL) policies have been shown to be deceived by perturbations (e.g., random noise or intensional adversarial attacks) on state observations that appear…
CoCon: A Self-Supervised Approach for Controlled Text Generation
Alvin Chan, Yew-Soon Ong, Bill Pung +2
Pretrained Transformer-based language models (LMs) display remarkable natural language generation capabilities. With their immense potential, controlling text generation of such LM…
Deep Latent-Variable Kernel Learning
Haitao Liu, Yew-Soon Ong, Xiaomo Jiang +1
Deep kernel learning (DKL) leverages the connection between Gaussian process (GP) and neural networks (NN) to build an end-to-end, hybrid model. It combines the capability of NN to…
Heterogeneous Representation Learning: A Review
Joey Tianyi Zhou, Xi Peng, Yew-Soon Ong
The real-world data usually exhibits heterogeneous properties such as modalities, views, or resources, which brings some unique challenges wherein the key is Heterogeneous Represen…
Relational Thematic Clustering with Mutually Preferred Neighbors
Tiantian He, Lu Bai, Yew-Soon Ong
Automatically learning thematic clusters in network data has long been a challenging task in machine learning community. A number of approaches have been proposed to accomplish it,…