Tian Li | DeepAI

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492 publications
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133 publications
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132 publications
Kurt Keutzer
112 publications
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79 publications
Manzil Zaheer
69 publications
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Yuanwei Liu
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Arumugam Nallanathan
54 publications

∙ 04/22/2023

Dehazing-NeRF: Neural Radiance Fields from Hazy Images

Neural Radiance Field (NeRF) has received much attention in recent years...

0 Tian Li, et al. ∙

∙ 12/01/2022

Differentially Private Adaptive Optimization with Delayed Preconditioners

Privacy noise may negate the benefits of using adaptive optimizers in di...

0 Tian Li, et al. ∙

∙ 06/20/2022

Domain-Adaptive Text Classification with Structured Knowledge from Unlabeled Data

Domain adaptive text classification is a challenging problem for the lar...

0 Tian Li, et al. ∙

∙ 06/18/2022

Motley: Benchmarking Heterogeneity and Personalization in Federated Learning

Personalized federated learning considers learning models unique to each...

0 Shanshan Wu, et al. ∙

∙ 05/30/2022

To Federate or Not To Federate: Incentivizing Client Participation in Federated Learning

Federated learning (FL) facilitates collaboration between a group of cli...

0 Yae Jee Cho, et al. ∙

∙ 04/21/2022

PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map

Deep learning has recently achieved significant progress in trajectory f...

0 Chenfeng Xu, et al. ∙

∙ 02/12/2022

Private Adaptive Optimization with Side Information

Adaptive optimization methods have become the default solvers for many m...

0 Tian Li, et al. ∙

∙ 12/23/2021

Reinforced Meta-path Selection for Recommendation on Heterogeneous Information Networks

Heterogeneous Information Networks (HINs) capture complex relations amon...

0 Wentao Ning, et al. ∙

∙ 09/13/2021

On Tilted Losses in Machine Learning: Theory and Applications

Exponential tilting is a technique commonly used in fields such as stati...

0 Tian Li, et al. ∙

∙ 06/08/2021

Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing

Tuning hyperparameters is a crucial but arduous part of the machine lear...

0 Mikhail Khodak, et al. ∙

∙ 03/18/2021

You Only Group Once: Efficient Point-Cloud Processing with Token Representation and Relation Inference Module

3D point-cloud-based perception is a challenging but crucial computer vi...

0 Chenfeng Xu, et al. ∙

∙ 03/01/2021

Heterogeneity for the Win: One-Shot Federated Clustering

In this work, we explore the unique challenges – and opportunities – of ...

0 Don Kurian Dennis, et al. ∙

∙ 12/08/2020

Federated Multi-Task Learning for Competing Constraints

In addition to accuracy, fairness and robustness are two critical concer...

0 Tian Li, et al. ∙

∙ 12/05/2020

Cross-Domain Sentiment Classification with In-Domain Contrastive Learning

Contrastive learning (CL) has been successful as a powerful representati...

0 Tian Li, et al. ∙

∙ 10/30/2020

Cross-Domain Sentiment Classification With Contrastive Learning and Mutual Information Maximization

Contrastive learning (CL) has been successful as a powerful representati...

0 Tian Li, et al. ∙

∙ 07/02/2020

Tilted Empirical Risk Minimization

Empirical risk minimization (ERM) is typically designed to perform well ...

0 Tian Li, et al. ∙

∙ 03/07/2020

Outage Behaviors of NOMA-based Satellite Network over Shadowed-Rician Fading Channels

This paper investigates the application of non-orthogonal multiple acces...

0 Xinwei Yue, et al. ∙

∙ 01/07/2020

FedDANE: A Federated Newton-Type Method

Federated learning aims to jointly learn statistical models over massive...

0 Tian Li, et al. ∙

∙ 11/05/2019

Enhancing the Privacy of Federated Learning with Sketching

In response to growing concerns about user privacy, federated learning h...

0 Zaoxing Liu, et al. ∙

∙ 11/03/2019

Privacy for Free: Communication-Efficient Learning with Differential Privacy Using Sketches

Communication and privacy are two critical concerns in distributed learn...

0 Tian Li, et al. ∙

∙ 08/21/2019

Federated Learning: Challenges, Methods, and Future Directions

Federated learning involves training statistical models over remote devi...

9 Tian Li, et al. ∙

∙ 05/25/2019

Fair Resource Allocation in Federated Learning

Federated learning involves training statistical models in massive, hete...

0 Tian Li, et al. ∙

∙ 12/14/2018

On the Convergence of Federated Optimization in Heterogeneous Networks

The burgeoning field of federated learning involves training machine lea...

0 Anit Kumar Sahu, et al. ∙

∙ 12/03/2018

LEAF: A Benchmark for Federated Settings

Modern federated networks, such as those comprised of wearable devices, ...

0 Sebastian Caldas, et al. ∙

∙ 08/24/2017

Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads

We present ease.ml, a declarative machine learning service platform we b...

0 Tian Li, et al. ∙

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