172 citations · 341 across the 17 of their papers we have counts for
17 papers
Data Acquisition: A New Frontier in Data-centric AI
Lingjiao Chen, Bilge Acun, Newsha Ardalani +8
As Machine Learning (ML) systems continue to grow, the demand for relevant and comprehensive datasets becomes imperative. There is limited study on the challenges of data acquisiti…
How is ChatGPT's behavior changing over time?
Lingjiao Chen, Matei Zaharia, James Zou
GPT-3.5 and GPT-4 are the two most widely used large language model (LLM) services. However, when and how these models are updated over time is opaque. Here, we evaluate the March…
FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance
Lingjiao Chen, Matei Zaharia, James Zou
There is a rapidly growing number of large language models (LLMs) that users can query for a fee. We review the cost associated with querying popular LLM APIs, e.g. GPT-4, ChatGPT,…
How to Backpropagate through Hungarian in Your DETR?
Lingji Chen, Alok Sharma, Chinmay Shirore +2
The DEtection TRansformer (DETR) approach, which uses a transformer encoder-decoder architecture and a set-based global loss, has become a building block in many transformer based…
SEAL : Interactive Tool for Systematic Error Analysis and Labeling
Nazneen Rajani, Weixin Liang, Lingjiao Chen +2
With the advent of Transformers, large language models (LLMs) have saturated well-known NLP benchmarks and leaderboards with high aggregate performance. However, many times these m…
HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions
Lingjiao Chen, Zhihua Jin, Sabri Eyuboglu +3
Commercial ML APIs offered by providers such as Google, Amazon and Microsoft have dramatically simplified ML adoption in many applications. Numerous companies and academics pay to…
Estimating and Explaining Model Performance When Both Covariates and Labels Shift
Lingjiao Chen, Matei Zaharia, James Zou
Deployed machine learning (ML) models often encounter new user data that differs from their training data. Therefore, estimating how well a given model might perform on the new dat…
Solon: Communication-efficient Byzantine-resilient Distributed Training via Redundant Gradients
Lingjiao Chen, Leshang Chen, Hongyi Wang +2
There has been a growing need to provide Byzantine-resilience in distributed model training. Existing robust distributed learning algorithms focus on developing sophisticated robus…
Did the Model Change? Efficiently Assessing Machine Learning API Shifts
Lingjiao Chen, Tracy Cai, Matei Zaharia +1
Machine learning (ML) prediction APIs are increasingly widely used. An ML API can change over time due to model updates or retraining. This presents a key challenge in the usage of…
Efficient Online ML API Selection for Multi-Label Classification Tasks
Lingjiao Chen, Matei Zaharia, James Zou
Multi-label classification tasks such as OCR and multi-object recognition are a major focus of the growing machine learning as a service industry. While many multi-label prediction…
FrugalML: How to Use ML Prediction APIs More Accurately and Cheaply
Lingjiao Chen, Matei Zaharia, James Zou
Prediction APIs offered for a fee are a fast-growing industry and an important part of machine learning as a service. While many such services are available, the heterogeneity in t…
The Effect of Network Width on the Performance of Large-batch Training
Lingjiao Chen, Hongyi Wang, Jinman Zhao +2
Distributed implementations of mini-batch stochastic gradient descent (SGD) suffer from communication overheads, attributed to the high frequency of gradient updates inherent in sm…
Model-based Pricing for Machine Learning in a Data Marketplace
Lingjiao Chen, Paraschos Koutris, Arun Kumar
Data analytics using machine learning (ML) has become ubiquitous in science, business intelligence, journalism and many other domains. While a lot of work focuses on reducing the t…
DRACO: Byzantine-resilient Distributed Training via Redundant Gradients
Lingjiao Chen, Hongyi Wang, Zachary Charles +1
Distributed model training is vulnerable to byzantine system failures and adversarial compute nodes, i.e., nodes that use malicious updates to corrupt the global model stored at a…
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training
Xi Wu, Uyeong Jang, Jiefeng Chen +2
In this paper we study leveraging confidence information induced by adversarial training to reinforce adversarial robustness of a given adversarially trained model. A natural measu…
Tuple-oriented Compression for Large-scale Mini-batch Stochastic Gradient Descent
Fengan Li, Lingjiao Chen, Yijing Zeng +4
Data compression is a popular technique for improving the efficiency of data processing workloads such as SQL queries and more recently, machine learning (ML) with classical batch…
Towards Linear Algebra over Normalized Data
Lingjiao Chen, Arun Kumar, Jeffrey Naughton +1
Providing machine learning (ML) over relational data is a mainstream requirement for data analytics systems. While almost all the ML tools require the input data to be presented as…