Search Results for author: Xinyi Xu

Found 20 papers, 10 papers with code

DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning

1 code implementation22 May 2024 Zijian Zhou, Xiaoqiang Lin, Xinyi Xu, Alok Prakash, Daniela Rus, Bryan Kian Hsiang Low

In-context learning (ICL) allows transformer-based language models that are pre-trained on general text to quickly learn a specific task with a few "task demonstrations" without updating their parameters, significantly boosting their flexibility and generality.

In-Context Learning

Fair yet Asymptotically Equal Collaborative Learning

1 code implementation9 Jun 2023 Xiaoqiang Lin, Xinyi Xu, See-Kiong Ng, Chuan-Sheng Foo, Bryan Kian Hsiang Low

In collaborative learning with streaming data, nodes (e. g., organizations) jointly and continuously learn a machine learning (ML) model by sharing the latest model updates computed from their latest streaming data.

Fairness Incremental Learning

On the Convergence of the Shapley Value in Parametric Bayesian Learning Games

1 code implementation16 May 2022 Lucas Agussurja, Xinyi Xu, Bryan Kian Hsiang Low

We show that for any two players, under some regularity conditions, their difference in Shapley value converges in probability to the difference in Shapley value of a limiting game whose characteristic function is proportional to the log-determinant of the joint Fisher information.

Bayesian Inference

AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRI

1 code implementation12 May 2022 Wen Shi, Haoan Xu, Cong Sun, Jiwei Sun, Yamin Li, Xinyi Xu, Tianshu Zheng, Yi Zhang, Guangbin Wang, Dan Wu

Multi-slice magnetic resonance images of the fetal brain are usually contaminated by severe and arbitrary fetal and maternal motion.

Super-Resolution

Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards

1 code implementation17 Dec 2021 Sebastian Shenghong Tay, Xinyi Xu, Chuan Sheng Foo, Bryan Kian Hsiang Low

This paper presents a novel collaborative generative modeling (CGM) framework that incentivizes collaboration among self-interested parties to contribute data to a pool for training a generative model (e. g., GAN), from which synthetic data are drawn and distributed to the parties as rewards commensurate to their contributions.

BIG-bench Machine Learning Data Valuation +1

Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning

no code implementations NeurIPS 2021 Xinyi Xu, Lingjuan Lyu, Xingjun Ma, Chenglin Miao, Chuan Sheng Foo, Bryan Kian Hsiang Low

In this paper, we adopt federated learning as a gradient-based formalization of collaborative machine learning, propose a novel cosine gradient Shapley value to evaluate the agents’ uploaded model parameter updates/gradients, and design theoretically guaranteed fair rewards in the form of better model performance.

BIG-bench Machine Learning Fairness +1

Validation Free and Replication Robust Volume-based Data Valuation

no code implementations NeurIPS 2021 Xinyi Xu, Zhaoxuan Wu, Chuan Sheng Foo, Bryan Kian Hsiang Low

We observe that the diversity of the data points is an inherent property of the dataset that is independent of validation.

Data Valuation Diversity

Group Contrastive Self-Supervised Learning on Graphs

no code implementations20 Jul 2021 Xinyi Xu, Cheng Deng, Yaochen Xie, Shuiwang Ji

Our framework embeds the given graph into multiple subspaces, of which each representation is prompted to encode specific characteristics of graphs.

Contrastive Learning Self-Supervised Learning

A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning

2 code implementations20 Nov 2020 Xinyi Xu, Lingjuan Lyu

In this paper, we propose a novel Robust and Fair Federated Learning (RFFL) framework to achieve collaborative fairness and adversarial robustness simultaneously via a reputation mechanism.

Adversarial Defense Adversarial Robustness +2

Searching k-Optimal Goals for an Orienteering Problem on a Specialized Graph with Budget Constraints

no code implementations2 Nov 2020 Abhinav Sharma, Advait Deshpande, Yanming Wang, Xinyi Xu, Prashan Madumal, Anbin Hou

We propose a novel non-randomized anytime orienteering algorithm for finding k-optimal goals that maximize reward on a specialized graph with budget constraints.

Collaborative Fairness in Federated Learning

1 code implementation27 Aug 2020 Lingjuan Lyu, Xinyi Xu, Qian Wang

In current deep learning paradigms, local training or the Standalone framework tends to result in overfitting and thus poor generalizability.

Fairness Federated Learning

Hierarchical Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection

no code implementations8 Aug 2020 Xinyi Xu, Tiancheng Huang, Pengfei Wei, Akshay Narayan, Tze-Yun Leong

This work is inspired by recent advances in hierarchical reinforcement learning (HRL) (Barto and Mahadevan 2003; Hengst 2010), and improvements in learning efficiency from heuristic-based subgoal selection, experience replay (Lin 1993; Andrychowicz et al. 2017), and task-based curriculum learning (Bengio et al. 2009; Zaremba and Sutskever 2014).

Decision Making Hierarchical Reinforcement Learning +4

Deep Asymmetric Metric Learning via Rich Relationship Mining

no code implementations CVPR 2019 Xinyi Xu, Yanhua Yang, Cheng Deng, Feng Zheng

The asymmetric structure enables the two data streams to interlace each other, which allows for the informative comparison between new data pairs over iterations.

Face Verification Image Retrieval +3

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