436 citations · 763 across the 12 of their papers we have counts for
12 papers
Domain-Adaptive Text Classification with Structured Knowledge from Unlabeled Data
Tian Li, Xiang Chen, Zhen Dong +4
Domain adaptive text classification is a challenging problem for the large-scale pretrained language models because they often require expensive additional labeled data to adapt to…
PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map
Chenfeng Xu, Tian Li, Chen Tang +5
Deep learning has recently achieved significant progress in trajectory forecasting. However, the scarcity of trajectory data inhibits the data-hungry deep-learning models from lear…
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing
Mikhail Khodak, Renbo Tu, Tian Li +4
Tuning hyperparameters is a crucial but arduous part of the machine learning pipeline. Hyperparameter optimization is even more challenging in federated learning, where models are…
You Only Group Once: Efficient Point-Cloud Processing with Token Representation and Relation Inference Module
Chenfeng Xu, Bohan Zhai, Bichen Wu +5
3D point-cloud-based perception is a challenging but crucial computer vision task. A point-cloud consists of a sparse, unstructured, and unordered set of points. To understand a po…
Heterogeneity for the Win: One-Shot Federated Clustering
Don Kurian Dennis, Tian Li, Virginia Smith
In this work, we explore the unique challenges -- and opportunities -- of unsupervised federated learning (FL). We develop and analyze a one-shot federated clustering scheme, -F…
Cross-Domain Sentiment Classification with In-Domain Contrastive Learning
Tian Li, Xiang Chen, Shanghang Zhang +2
Contrastive learning (CL) has been successful as a powerful representation learning method. In this paper, we propose a contrastive learning framework for cross-domain sentiment cl…
Ditto: Fair and Robust Federated Learning Through Personalization
Tian Li, Shengyuan Hu, Ahmad Beirami +1
Fairness and robustness are two important concerns for federated learning systems. In this work, we identify that robustness to data and model poisoning attacks and fairness, measu…
Cross-Domain Sentiment Classification with Contrastive Learning and Mutual Information Maximization
Tian Li, Xiang Chen, Shanghang Zhang +2
Contrastive learning (CL) has been successful as a powerful representation learning method. In this work we propose CLIM: Contrastive Learning with mutual Information Maximization,…
Tilted Empirical Risk Minimization
Tian Li, Ahmad Beirami, Maziar Sanjabi +1
Empirical risk minimization (ERM) is typically designed to perform well on the average loss, which can result in estimators that are sensitive to outliers, generalize poorly, or tr…
Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketches
Tian Li, Zaoxing Liu, Vyas Sekar +1
Communication and privacy are two critical concerns in distributed learning. Many existing works treat these concerns separately. In this work, we argue that a natural connection e…
Enhancing the Privacy of Federated Learning with Sketching
Zaoxing Liu, Tian Li, Virginia Smith +1
In response to growing concerns about user privacy, federated learning has emerged as a promising tool to train statistical models over networks of devices while keeping data local…
Federated Optimization in Heterogeneous Networks
Tian Li, Anit Kumar Sahu, Manzil Zaheer +3
Federated Learning is a distributed learning paradigm with two key challenges that differentiate it from traditional distributed optimization: (1) significant variability in terms…