Countering Mainstream Bias via End-to-End Adaptive Local Learning: Adaptive Local Learning

Table of Links Abstract and 1 Introduction 2 Preliminaries 3 End-to-End Adaptive Local Learning 3.1 Loss-Driven Mixture-of-Experts 3.2 Synchronized Learning via Adaptive Weight 4 Debiasing Experiments and 4.1 Experimental Setup 4.2 Debiasing Performance 4.3 Ablation Study 4.4 Effect of the Adaptive Weight Module and 4.5 Hyper-parameter Study 5 Related Work 6 Conclusion, Acknowledgements, and References 3 End-to-End Adaptive Local Learning To debias, we propose the end-To-end Adaptive Local Learning (TALL) framework, shown in Figure 2. To address the discrepancy modeling problem, this framework integrates a loss-driven Mixture-of-Experts module to adaptively provide customized models for different users by an end-to-end learning procedure. To address the unsynchronized learning problem, the framework involves an adaptive weight module to synchronize the learning paces of different users by adaptively adjusting weights in the objective function. Authors: (1) Jinhao Pan [0009 −0006 −1574 −6376], Texas A&M University, College Station, TX, USA; (2) Ziwei Zhu [0000 −0002 −3990 −4774], George Mason University, Fairfax, VA, USA; (3) Jianling Wang [0000 −0001 −9916 −0976], Texas A&M University, College Station, TX, USA; (4) Allen Lin [0000 −0003 −0980 −4323], Texas A&M University, College Station, TX, USA; (5) James Caverlee [0000 −0001 −8350 −8528]. Texas A&M University, College Station, TX, USA. This paper is available on arxiv under CC BY 4.0 DEED license. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Preliminaries 2 Preliminaries 3 End-to-End Adaptive Local Learning 3 End-to-End Adaptive Local Learning 3.1 Loss-Driven Mixture-of-Experts 3.1 Loss-Driven Mixture-of-Experts 3.2 Synchronized Learning via Adaptive Weight 3.2 Synchronized Learning via Adaptive Weight 4 Debiasing Experiments and 4.1 Experimental Setup 4 Debiasing Experiments and 4.1 Experimental Setup 4.2 Debiasing Performance 4.2 Debiasing Performance 4.3 Ablation Study Ablation 4.4 Effect of the Adaptive Weight Module and 4.5 Hyper-parameter Study 4.4 Effect of the Adaptive Weight Module and 4.5 Hyper-parameter Study 5 Related Work 5 Related Work 6 Conclusion, Acknowledgements, and References 6 Conclusion, Acknowledgements, and References 3 End-to-End Adaptive Local Learning To debias, we propose the end- T o-end A daptive L ocal L earning ( TALL ) framework, shown in Figure 2. To address the discrepancy modeling problem, this framework integrates a loss-driven Mixture-of-Experts module to adaptively provide customized models for different users by an end-to-end learning procedure. To address the unsynchronized learning problem , the framework involves an adaptive weight module to synchronize the learning paces of different users by adaptively adjusting weights in the objective function. T A L L TALL discrepancy modeling loss-driven Mixture-of-Experts unsynchronized learning problem adaptive weight Authors: (1) Jinhao Pan [0009 −0006 −1574 −6376], Texas A&M University, College Station, TX, USA; (2) Ziwei Zhu [0000 −0002 −3990 −4774], George Mason University, Fairfax, VA, USA; (3) Jianling Wang [0000 −0001 −9916 −0976], Texas A&M University, College Station, TX, USA; (4) Allen Lin [0000 −0003 −0980 −4323], Texas A&M University, College Station, TX, USA; (5) James Caverlee [0000 −0001 −8350 −8528]. Texas A&M University, College Station, TX, USA. Authors: Authors: (1) Jinhao Pan [0009 −0006 −1574 −6376], Texas A&M University, College Station, TX, USA; (2) Ziwei Zhu [0000 −0002 −3990 −4774], George Mason University, Fairfax, VA, USA; (3) Jianling Wang [0000 −0001 −9916 −0976], Texas A&M University, College Station, TX, USA; (4) Allen Lin [0000 −0003 −0980 −4323], Texas A&M University, College Station, TX, USA; (5) James Caverlee [0000 −0001 −8350 −8528]. Texas A&M University, College Station, TX, USA. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

Countering Mainstream Bias via End-to-End Adaptive Local Learning: Adaptive Local Learning

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

A Comprehensive Overview of Image Generation Models: From GANs to Diffusion Techniques

Countering Mainstream Bias via End-to-End Adaptive Local Learning: Abstract and Introduction

Countering Mainstream Bias via End-to-End Adaptive Local Learning: Preliminaries

Countering Mainstream Bias via End-to-End Adaptive Local Learning: Loss-Driven Mixture-of-Experts

Countering Mainstream Bias via End-to-End Adaptive Local Learning: Adaptive Weight

A Comprehensive Overview of Image Generation Models: From GANs to Diffusion Techniques

Countering Mainstream Bias via End-to-End Adaptive Local Learning: Abstract and Introduction

Countering Mainstream Bias via End-to-End Adaptive Local Learning: Preliminaries

Countering Mainstream Bias via End-to-End Adaptive Local Learning: Loss-Driven Mixture-of-Experts

Countering Mainstream Bias via End-to-End Adaptive Local Learning: Adaptive Weight

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps