63 citations · 177 across the 11 of their papers we have counts for
6 papers · 1 filter
Mitigating Reward Hacking in RLHF via Bayesian Non-negative Reward Modeling
Zhibin Duan, Guowei Rong, Zhuo Li +3
Reward models learned from human preferences are central to aligning large language models (LLMs) via reinforcement learning from human feedback, yet they are often vulnerable to r…
Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport
Dandan Guo, Long Tian, He Zhao +2
Few-shot classification aims to learn a classifier to recognize unseen classes during training, where the learned model can easily become over-fitted based on the biased distributi…
Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification
Dandan Guo, Zhuo Li, Meixi Zheng +3
Imbalanced data pose challenges for deep learning based classification models. One of the most widely-used approaches for tackling imbalanced data is re-weighting, where training s…
Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings
Dongsheng Wang, Dandan Guo, He Zhao +4
A topic model is often formulated as a generative model that explains how each word of a document is generated given a set of topics and document-specific topic proportions. It is…
Learning Prototype-oriented Set Representations for Meta-Learning
Dandan Guo, Long Tian, Minghe Zhang +2
Learning from set-structured data is a fundamental problem that has recently attracted increasing attention, where a series of summary networks are introduced to deal with the set…
Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference
Hao Zhang, Bo Chen, Yulai Cong +3
To build a flexible and interpretable model for document analysis, we develop deep autoencoding topic model (DATM) that uses a hierarchy of gamma distributions to construct its mul…