Search Results for author: Tian Li

Found 26 papers, 15 papers with code

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

no code implementations30 May 2022 Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, Gauri Joshi

Federated learning (FL) facilitates collaboration between a group of clients who seek to train a common machine learning model without directly sharing their local data.

Federated Learning

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

no code implementations21 Apr 2022 Chenfeng Xu, Tian Li, Chen Tang, Lingfeng Sun, Kurt Keutzer, Masayoshi Tomizuka, Alireza Fathi, Wei Zhan

It is hard to replicate these approaches in trajectory forecasting due to the lack of adequate trajectory data (e. g., 34K samples in the nuScenes dataset).

Contrastive Learning Natural Language Processing +2

Private Adaptive Optimization with Side Information

1 code implementation12 Feb 2022 Tian Li, Manzil Zaheer, Sashank J. Reddi, Virginia Smith

Adaptive optimization methods have become the default solvers for many machine learning tasks.

Diverse Client Selection for Federated Learning via Submodular Maximization

no code implementations ICLR 2022 Ravikumar Balakrishnan, Tian Li, Tianyi Zhou, Nageen Himayat, Virginia Smith, Jeff Bilmes

In every communication round of federated learning, a random subset of clients communicate their model updates back to the server which then aggregates them all.

Fairness Federated Learning

On Tilted Losses in Machine Learning: Theory and Applications

1 code implementation13 Sep 2021 Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith

Finally, we demonstrate that TERM can be used for a multitude of applications in machine learning, such as enforcing fairness between subgroups, mitigating the effect of outliers, and handling class imbalance.

Fairness Learning Theory

Heterogeneity for the Win: One-Shot Federated Clustering

no code implementations1 Mar 2021 Don Kurian Dennis, Tian Li, Virginia Smith

In this work, we explore the unique challenges -- and opportunities -- of unsupervised federated learning (FL).

Federated Learning

Experimental study of decoherence of the two-mode squeezed vacuum state via second harmonic generation

no code implementations22 Dec 2020 Fu Li, Tian Li, Girish S. Agarwal

Such a correlation is the most important characteristic of a two-mode squeezed state.

Optics Quantum Physics

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

1 code implementation30 Oct 2020 Tian Li, Xiang Chen, Shanghang Zhang, Zhen Dong, Kurt Keutzer

Due to scarcity of labels on the target domain, we introduce mutual information maximization (MIM) apart from CL to exploit the features that best support the final prediction.

Contrastive Learning General Classification +3

Tilted Empirical Risk Minimization

1 code implementation ICLR 2021 Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith

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 treat subgroups unfairly.

Fairness

FedDANE: A Federated Newton-Type Method

1 code implementation7 Jan 2020 Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith

Federated learning aims to jointly learn statistical models over massively distributed remote devices.

Distributed Optimization Federated Learning

Enhancing the Privacy of Federated Learning with Sketching

no code implementations5 Nov 2019 Zaoxing Liu, Tian Li, Virginia Smith, Vyas Sekar

Federated learning methods run training tasks directly on user devices and do not share the raw user data with third parties.

Federated Learning

Federated Learning: Challenges, Methods, and Future Directions

1 code implementation21 Aug 2019 Tian Li, Anit Kumar Sahu, Ameet Talwalkar, Virginia Smith

Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized.

Distributed Optimization Federated Learning +1

Federated Optimization in Heterogeneous Networks

11 code implementations14 Dec 2018 Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith

Theoretically, we provide convergence guarantees for our framework when learning over data from non-identical distributions (statistical heterogeneity), and while adhering to device-level systems constraints by allowing each participating device to perform a variable amount of work (systems heterogeneity).

Distributed Optimization Federated Learning

LEAF: A Benchmark for Federated Settings

5 code implementations3 Dec 2018 Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu, Tian Li, Jakub Konečný, H. Brendan McMahan, Virginia Smith, Ameet Talwalkar

Modern federated networks, such as those comprised of wearable devices, mobile phones, or autonomous vehicles, generate massive amounts of data each day.

Autonomous Vehicles Federated Learning +2

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

no code implementations24 Aug 2017 Tian Li, Jie Zhong, Ji Liu, Wentao Wu, Ce Zhang

We ask, as a "service provider" that manages a shared cluster of machines among all our users running machine learning workloads, what is the resource allocation strategy that maximizes the global satisfaction of all our users?

Fairness Image Classification +2

A Multilingual Natural Stress Emotion Database

no code implementations LREC 2012 Xin Zuo, Tian Li, Pascale Fung

In this paper, we describe an ongoing effort in collecting and annotating a multilingual speech database of natural stress emotion from university students.

Emotion Recognition Speech Synthesis

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