Search Results for author: Jilin Chen

Found 16 papers, 2 papers with code

Flexible text generation for counterfactual fairness probing

no code implementations NAACL (WOAH) 2022 Zee Fryer, Vera Axelrod, Ben Packer, Alex Beutel, Jilin Chen, Kellie Webster

A common approach for testing fairness issues in text-based classifiers is through the use of counterfactuals: does the classifier output change if a sensitive attribute in the input is changed?

Fairness Text Generation

Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning

no code implementations4 Jun 2021 Yuyan Wang, Xuezhi Wang, Alex Beutel, Flavien Prost, Jilin Chen, Ed H. Chi

This presents a multi-dimensional Pareto frontier on (1) the trade-off between group fairness and accuracy with respect to each task, as well as (2) the trade-offs across multiple tasks.

Fairness Multi-Task Learning

Measuring Recommender System Effects with Simulated Users

no code implementations12 Jan 2021 Sirui Yao, Yoni Halpern, Nithum Thain, Xuezhi Wang, Kang Lee, Flavien Prost, Ed H. Chi, Jilin Chen, Alex Beutel

Using this simulation framework, we can (a) isolate the effect of the recommender system from the user preferences, and (b) examine how the system performs not just on average for an "average user" but also the extreme experiences under atypical user behavior.

Collaborative Filtering Recommendation Systems

CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation

no code implementations EMNLP 2020 Tianlu Wang, Xuezhi Wang, Yao Qin, Ben Packer, Kang Li, Jilin Chen, Alex Beutel, Ed Chi

Experiments on real-world NLP datasets demonstrate that our method can generate more diverse and fluent adversarial texts, compared to many existing adversarial text generation approaches.

Adversarial Text Sentiment Analysis +1

Fairness without Demographics through Adversarially Reweighted Learning

3 code implementations NeurIPS 2020 Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed H. Chi

Much of the previous machine learning (ML) fairness literature assumes that protected features such as race and sex are present in the dataset, and relies upon them to mitigate fairness concerns.


Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems

no code implementations5 Nov 2019 Xuezhi Wang, Nithum Thain, Anu Sinha, Flavien Prost, Ed H. Chi, Jilin Chen, Alex Beutel

In addition to the theoretical results, we find on multiple datasets -- including a large-scale real-world recommender system -- that the overall system's end-to-end fairness is largely achievable by improving fairness in individual components.

Fairness Recommendation Systems

Toward a better trade-off between performance and fairness with kernel-based distribution matching

no code implementations25 Oct 2019 Flavien Prost, Hai Qian, Qiuwen Chen, Ed H. Chi, Jilin Chen, Alex Beutel

As recent literature has demonstrated how classifiers often carry unintended biases toward some subgroups, deploying machine learned models to users demands careful consideration of the social consequences.


Recommending what video to watch next: a multitask ranking system

no code implementations RecSys 2019 Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, Ed Chi

In this paper, we introduce a large scale multi-objective ranking system for recommending what video to watch next on an industrial video sharing platform.

Transfer of Machine Learning Fairness across Domains

no code implementations24 Jun 2019 Candice Schumann, Xuezhi Wang, Alex Beutel, Jilin Chen, Hai Qian, Ed H. Chi

A model trained for one setting may be picked up and used in many others, particularly as is common with pre-training and cloud APIs.

BIG-bench Machine Learning Domain Adaptation +1

Fairness in Recommendation Ranking through Pairwise Comparisons

no code implementations2 Mar 2019 Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, Cristos Goodrow

Recommender systems are one of the most pervasive applications of machine learning in industry, with many services using them to match users to products or information.

Fairness Recommendation Systems

Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements

no code implementations14 Jan 2019 Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Allison Woodruff, Christine Luu, Pierre Kreitmann, Jonathan Bischof, Ed H. Chi

In this paper we provide a case-study on the application of fairness in machine learning research to a production classification system, and offer new insights in how to measure and address algorithmic fairness issues.

BIG-bench Machine Learning Fairness

Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts

4 code implementations19 Jul 2018 Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, Ed Chi

In this work, we propose a novel multi-task learning approach, Multi-gate Mixture-of-Experts (MMoE), which explicitly learns to model task relationships from data.

Click-Through Rate Prediction Multi-Task Learning +1

Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations

no code implementations1 Jul 2017 Alex Beutel, Jilin Chen, Zhe Zhao, Ed H. Chi

How can we learn a classifier that is "fair" for a protected or sensitive group, when we do not know if the input to the classifier belongs to the protected group?

Fairness Recommendation Systems

Why Are You More Engaged? Predicting Social Engagement from Word Use

no code implementations26 Feb 2014 Jalal Mahmud, Jilin Chen, Jeffrey Nichols

We present a study to analyze how word use can predict social engagement behaviors such as replies and retweets in Twitter.

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