198 citations · 1.3k across the 62 of their papers we have counts for
8 papers · 1 filter
When Does Group Invariant Learning Survive Spurious Correlations?
Yimeng Chen, Ruibin Xiong, Zhiming Ma +1
By inferring latent groups in the training data, recent works introduce invariant learning to the case where environment annotations are unavailable. Typically, learning group inva…
Uncertainty Calibration for Ensemble-Based Debiasing Methods
Ruibin Xiong, Yimeng Chen, Liang Pang +2
Ensemble-based debiasing methods have been shown effective in mitigating the reliance of classifiers on specific dataset bias, by exploiting the output of a bias-only model to adju…
On the Relation between Quality-Diversity Evaluation and Distribution-Fitting Goal in Text Generation
Jianing Li, Yanyan Lan, Jiafeng Guo +1
The goal of text generation models is to fit the underlying real probability distribution of text. For performance evaluation, quality and diversity metrics are usually applied. Ho…
Robust Reinforcement Learning with Wasserstein Constraint
Linfang Hou, Liang Pang, Xin Hong +3
Robust Reinforcement Learning aims to find the optimal policy with some extent of robustness to environmental dynamics. Existing learning algorithms usually enable the robustness t…
On Layer Normalization in the Transformer Architecture
Ruibin Xiong, Yunchang Yang, Di He +7
The Transformer is widely used in natural language processing tasks. To train a Transformer however, one usually needs a carefully designed learning rate warm-up stage, which is sh…
MQGrad: Reinforcement Learning of Gradient Quantization in Parameter Server
Guoxin Cui, Jun Xu, Wei Zeng +3
One of the most significant bottleneck in training large scale machine learning models on parameter server (PS) is the communication overhead, because it needs to frequently exchan…
Locally Smoothed Neural Networks
Liang Pang, Yanyan Lan, Jun Xu +2
Convolutional Neural Networks (CNN) and the locally connected layer are limited in capturing the importance and relations of different local receptive fields, which are often cruci…
Stochastic Rank Aggregation
Shuzi Niu, Yanyan Lan, Jiafeng Guo +1
This paper addresses the problem of rank aggregation, which aims to find a consensus ranking among multiple ranking inputs. Traditional rank aggregation methods are deterministic,…