Search Results for author: Yunfeng Zhang

Found 26 papers, 6 papers with code

Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI

no code implementations22 Jun 2022 Q. Vera Liao, Yunfeng Zhang, Ronny Luss, Finale Doshi-Velez, Amit Dhurandhar

We argue that one way to close the gap is to develop evaluation methods that account for different user requirements in these usage contexts.

De-biasing "bias" measurement

1 code implementation11 May 2022 Kristian Lum, Yunfeng Zhang, Amanda Bower

When a model's performance differs across socially or culturally relevant groups--like race, gender, or the intersections of many such groups--it is often called "biased."

Decision Making Fairness

Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation

no code implementations25 Apr 2022 Vivian Lai, Samuel Carton, Rajat Bhatnagar, Q. Vera Liao, Yunfeng Zhang, Chenhao Tan

Despite impressive performance in many benchmark datasets, AI models can still make mistakes, especially among out-of-distribution examples.

A Collaborative Attention Adaptive Network for Financial Market Forecasting

no code implementations29 Sep 2021 Qiuyue Zhang, Yunfeng Zhang, Fangxun Bao, Caiming Zhang, Peide Liu, Xunxiang Yao

However, taking into account the differences of different data types, how to use a fusion method adapted to financial data to fuse real market prices and tweets from social media, so that the prediction model can fully integrate different types of data remains a challenging problem.

Uncertainty Quantification 360: A Holistic Toolkit for Quantifying and Communicating the Uncertainty of AI

1 code implementation2 Jun 2021 Soumya Ghosh, Q. Vera Liao, Karthikeyan Natesan Ramamurthy, Jiri Navratil, Prasanna Sattigeri, Kush R. Varshney, Yunfeng Zhang

In this paper, we describe an open source Python toolkit named Uncertainty Quantification 360 (UQ360) for the uncertainty quantification of AI models.


D2S: Document-to-Slide Generation Via Query-Based Text Summarization

1 code implementation NAACL 2021 Edward Sun, Yufang Hou, Dakuo Wang, Yunfeng Zhang, Nancy X. R. Wang

Presentations are critical for communication in all areas of our lives, yet the creation of slide decks is often tedious and time-consuming.

Question Answering Text Summarization

Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML

no code implementations9 Apr 2021 Shweta Narkar, Yunfeng Zhang, Q. Vera Liao, Dakuo Wang, Justin D Weisz

Automated Machine Learning (AutoML) is a rapidly growing set of technologies that automate the model development pipeline by searching model space and generating candidate models.

AutoML Feature Importance

IPAPRec: A promising tool for learning high-performance mapless navigation skills with deep reinforcement learning

no code implementations22 Mar 2021 Wei zhang, Yunfeng Zhang, Ning Liu, Kai Ren, Pengfei Wang

This paper studies how to improve the generalization performance and learning speed of the navigation agents trained with deep reinforcement learning (DRL).


How Much Automation Does a Data Scientist Want?

no code implementations7 Jan 2021 Dakuo Wang, Q. Vera Liao, Yunfeng Zhang, Udayan Khurana, Horst Samulowitz, Soya Park, Michael Muller, Lisa Amini

There is an active research thread in AI, \autoai, that aims to develop systems for automating end-to-end the DS/ML Lifecycle.


Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making

no code implementations15 Oct 2020 Charvi Rastogi, Yunfeng Zhang, Dennis Wei, Kush R. Varshney, Amit Dhurandhar, Richard Tomsett

We, then, conduct a second user experiment which shows that our time allocation strategy with explanation can effectively de-anchor the human and improve collaborative performance when the AI model has low confidence and is incorrect.

Decision Making

Active Learning++: Incorporating Annotator's Rationale using Local Model Explanation

no code implementations6 Sep 2020 Bhavya Ghai, Q. Vera Liao, Yunfeng Zhang, Klaus Mueller

The similarity score between feature rankings provided by the annotator and the local model explanation is used to assign a weight to each corresponding committee model.

Active Learning Feature Importance

Schrödinger equations on compact globally symmetric spaces

no code implementations1 May 2020 Yunfeng Zhang

In this article, we establish scale-invariant Strichartz estimates for the Schr\"odinger equation on arbitrary compact globally symmetric spaces and some bilinear Strichartz estimates on products of rank-one spaces.

Analysis of PDEs Representation Theory

Model Agnostic Multilevel Explanations

no code implementations NeurIPS 2020 Karthikeyan Natesan Ramamurthy, Bhanukiran Vinzamuri, Yunfeng Zhang, Amit Dhurandhar

The method can also leverage side information, where users can specify points for which they may want the explanations to be similar.

Joint Optimization of AI Fairness and Utility: A Human-Centered Approach

no code implementations5 Feb 2020 Yunfeng Zhang, Rachel K. E. Bellamy, Kush R. Varshney

Today, AI is increasingly being used in many high-stakes decision-making applications in which fairness is an important concern.

Decision Making Fairness

Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience

no code implementations24 Jan 2020 Bhavya Ghai, Q. Vera Liao, Yunfeng Zhang, Rachel Bellamy, Klaus Mueller

We conducted an empirical study comparing the model learning outcomes, feedback content and experience with XAL, to that of traditional AL and coactive learning (providing the model's prediction without the explanation).

Active Learning

Consumer-Driven Explanations for Machine Learning Decisions: An Empirical Study of Robustness

no code implementations13 Jan 2020 Michael Hind, Dennis Wei, Yunfeng Zhang

Many proposed methods for explaining machine learning predictions are in fact challenging to understand for nontechnical consumers.

Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making

no code implementations7 Jan 2020 Yunfeng Zhang, Q. Vera Liao, Rachel K. E. Bellamy

In these scenarios, full automation is often undesirable, not only due to the significance of the outcome, but also because human experts can draw on their domain knowledge complementary to the model's to ensure task success.

Decision Making

Leveraging Latent Features for Local Explanations

2 code implementations29 May 2019 Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Yunfeng Zhang, Karthikeyan Shanmugam, Chun-Chen Tu

As the application of deep neural networks proliferates in numerous areas such as medical imaging, video surveillance, and self driving cars, the need for explaining the decisions of these models has become a hot research topic, both at the global and local level.

General Classification Self-Driving Cars

Bootstrapping Conversational Agents With Weak Supervision

no code implementations14 Dec 2018 Neil Mallinar, Abhishek Shah, Rajendra Ugrani, Ayush Gupta, Manikandan Gurusankar, Tin Kam Ho, Q. Vera Liao, Yunfeng Zhang, Rachel K. E. Bellamy, Robert Yates, Chris Desmarais, Blake McGregor

We report on a user study that shows positive user feedback for this new approach to build conversational agents, and demonstrates the effectiveness of using data programming for auto-labeling.

Joint association and classification analysis of multi-view data

no code implementations20 Nov 2018 Yunfeng Zhang, Irina Gaynanova

A distinct advantage of JACA is that it can be applied to the multi-view data with block-missing structure, that is to cases where a subset of views or class labels is missing for some subjects.

Classification General Classification

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