27 citations · 113 across the 27 of their papers we have counts for
27 papers
AdsWorldEngine: A Self-Evolving Conversational Advertising Agent through Orchestrator and Tool Coevolution
Simiao Zuo, Chenhui Xu, Yimeng Jia +3
Conversational advertising aims to deliver useful ads within multi-turn assistant interactions. Unlike conventional query-based advertising, where the user's intent is often expres…
m3BERT: A Modern, Multi-lingual, Matryoshka Bidirectional Encoder
Yaoxiang Wang, Simiao Zuo, Qingguo Hu +4
Embedding models are pivotal in industrial information retrieval systems like search and advertising. However, existing pretrained models often exhibit fixed architectures and embe…
Task Oriented In-Domain Data Augmentation
Xiao Liang, Xinyu Hu, Simiao Zuo +5
Large Language Models (LLMs) have shown superior performance in various applications and fields. To achieve better performance on specialized domains such as law and advertisement,…
Towards Consistent Natural-Language Explanations via Explanation-Consistency Finetuning
Yanda Chen, Chandan Singh, Xiaodong Liu +4
Large language models (LLMs) often generate convincing, fluent explanations. However, different from humans, they often generate inconsistent explanations on different inputs. For…
SMURF-THP: Score Matching-based UnceRtainty quantiFication for Transformer Hawkes Process
Zichong Li, Yanbo Xu, Simiao Zuo +4
Transformer Hawkes process models have shown to be successful in modeling event sequence data. However, most of the existing training methods rely on maximizing the likelihood of e…
Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing
Xinyu Hu, Pengfei Tang, Simiao Zuo +5
Large language models (LLMs) have made impressive progress in natural language processing. These models rely on proper human instructions (or prompts) to generate suitable response…
Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms
Alexander Bukharin, Yan Li, Yue Yu +6
Multi-Agent Reinforcement Learning (MARL) has shown promising results across several domains. Despite this promise, MARL policies often lack robustness and are therefore sensitive…
DeepTagger: Knowledge Enhanced Named Entity Recognition for Web-Based Ads Queries
Simiao Zuo, Pengfei Tang, Xinyu Hu +3
Named entity recognition (NER) is a crucial task for online advertisement. State-of-the-art solutions leverage pre-trained language models for this task. However, three major chall…
Machine Learning Force Fields with Data Cost Aware Training
Alexander Bukharin, Tianyi Liu, Shengjie Wang +4
Machine learning force fields (MLFF) have been proposed to accelerate molecular dynamics (MD) simulation, which finds widespread applications in chemistry and biomedical research.…
Efficient Long Sequence Modeling via State Space Augmented Transformer
Simiao Zuo, Xiaodong Liu, Jian Jiao +4
Transformer models have achieved superior performance in various natural language processing tasks. However, the quadratic computational cost of the attention mechanism limits its…
Less is More: Task-aware Layer-wise Distillation for Language Model Compression
Chen Liang, Simiao Zuo, Qingru Zhang +3
Layer-wise distillation is a powerful tool to compress large models (i.e. teacher models) into small ones (i.e., student models). The student distills knowledge from the teacher by…
Context-Aware Query Rewriting for Improving Users' Search Experience on E-commerce Websites
Simiao Zuo, Qingyu Yin, Haoming Jiang +4
E-commerce queries are often short and ambiguous. Consequently, query understanding often uses query rewriting to disambiguate user-input queries. While using e-commerce search too…
DiP-GNN: Discriminative Pre-Training of Graph Neural Networks
Simiao Zuo, Haoming Jiang, Qingyu Yin +3
Graph neural network (GNN) pre-training methods have been proposed to enhance the power of GNNs. Specifically, a GNN is first pre-trained on a large-scale unlabeled graph and then…
Differentially Private Estimation of Hawkes Process
Simiao Zuo, Tianyi Liu, Tuo Zhao +1
Point process models are of great importance in real world applications. In certain critical applications, estimation of point process models involves large amounts of sensitive pe…
PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance
Qingru Zhang, Simiao Zuo, Chen Liang +4
Large Transformer-based models have exhibited superior performance in various natural language processing and computer vision tasks. However, these models contain enormous amounts…
MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation
Simiao Zuo, Qingru Zhang, Chen Liang +3
Pre-trained language models have demonstrated superior performance in various natural language processing tasks. However, these models usually contain hundreds of millions of param…
No Parameters Left Behind: Sensitivity Guided Adaptive Learning Rate for Training Large Transformer Models
Chen Liang, Haoming Jiang, Simiao Zuo +5
Recent research has shown the existence of significant redundancy in large Transformer models. One can prune the redundant parameters without significantly sacrificing the generali…
Taming Sparsely Activated Transformer with Stochastic Experts
Simiao Zuo, Xiaodong Liu, Jian Jiao +5
Sparsely activated models (SAMs), such as Mixture-of-Experts (MoE), can easily scale to have outrageously large amounts of parameters without significant increase in computational…
Adversarially Regularized Policy Learning Guided by Trajectory Optimization
Zhigen Zhao, Simiao Zuo, Tuo Zhao +1
Recent advancement in combining trajectory optimization with function approximation (especially neural networks) shows promise in learning complex control policies for diverse task…
Self-Training with Differentiable Teacher
Simiao Zuo, Yue Yu, Chen Liang +5
Self-training achieves enormous success in various semi-supervised and weakly-supervised learning tasks. The method can be interpreted as a teacher-student framework, where the tea…
ARCH: Efficient Adversarial Regularized Training with Caching
Simiao Zuo, Chen Liang, Haoming Jiang +5
Adversarial regularization can improve model generalization in many natural language processing tasks. However, conventional approaches are computationally expensive since they nee…
Super Tickets in Pre-Trained Language Models: From Model Compression to Improving Generalization
Chen Liang, Simiao Zuo, Minshuo Chen +5
The Lottery Ticket Hypothesis suggests that an over-parametrized network consists of ``lottery tickets'', and training a certain collection of them (i.e., a subnetwork) can match t…
Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach
Simiao Zuo, Chen Liang, Haoming Jiang +5
Adversarial regularization has been shown to improve the generalization performance of deep learning models in various natural language processing tasks. Existing works usually for…
A Hypergradient Approach to Robust Regression without Correspondence
Yujia Xie, Yixiu Mao, Simiao Zuo +4
We consider a variant of regression problem, where the correspondence between input and output data is not available. Such shuffled data is commonly observed in many real world pro…