Search Results for author: Dakuo Wang

Found 32 papers, 10 papers with code

Towards a Progression-Aware Autonomous Dialogue Agent

no code implementations7 May 2022 Abraham Sanders, Tomek Strzalkowski, Mei Si, Albert Chang, Deepanshu Dey, Jonas Braasch, Dakuo Wang

Recent advances in large-scale language modeling and generation have enabled the creation of dialogue agents that exhibit human-like responses in a wide range of conversational scenarios spanning a diverse set of tasks, from general chit-chat to focused goal-oriented discourse.

Language Modelling

A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction

1 code implementation1 May 2022 Yong Xie, Dakuo Wang, Pin-Yu Chen, JinJun Xiong, Sijia Liu, Sanmi Koyejo

More and more investors and machine learning models rely on social media (e. g., Twitter and Reddit) to gather real-time information and sentiment to predict stock price movements.

Adversarial Attack Combinatorial Optimization +1

DeepShovel: An Online Collaborative Platform for Data Extraction in Geoscience Literature with AI Assistance

no code implementations21 Feb 2022 Shao Zhang, Yuting Jia, Hui Xu, Ying Wen, Dakuo Wang, Xinbing Wang

Geoscientists, as well as researchers in many fields, need to read a huge amount of literature to locate, extract, and aggregate relevant results and data to enable future research or to build a scientific database, but there is no existing system to support this use case well.

StoryBuddy: A Human-AI Collaborative Chatbot for Parent-Child Interactive Storytelling with Flexible Parental Involvement

1 code implementation13 Feb 2022 Zheng Zhang, Ying Xu, Yanhao Wang, Bingsheng Yao, Daniel Ritchie, Tongshuang Wu, Mo Yu, Dakuo Wang, Toby Jia-Jun Li

Despite its benefits for children's skill development and parent-child bonding, many parents do not often engage in interactive storytelling by having story-related dialogues with their child due to limited availability or challenges in coming up with appropriate questions.

Chatbot

Human-Centered AI for Data Science: A Systematic Approach

no code implementations3 Oct 2021 Dakuo Wang, Xiaojuan Ma, April Yi Wang

Human-Centered AI (HCAI) refers to the research effort that aims to design and implement AI techniques to support various human tasks, while taking human needs into consideration and preserving human control.

AutoML

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

Preserve, Promote, or Attack? GNN Explanation via Topology Perturbation

no code implementations25 Mar 2021 Yi Sun, Abel Valente, Sijia Liu, Dakuo Wang

Prior works on formalizing explanations of a graph neural network (GNN) focus on a single use case - to preserve the prediction results through identifying important edges and nodes.

Image Classification

AutoDS: Towards Human-Centered Automation of Data Science

no code implementations13 Jan 2021 Dakuo Wang, Josh Andres, Justin Weisz, Erick Oduor, Casey Dugan

Only till recently, machine learning(ML) researchers have developed promising automation techniques to aid data workers in these tasks.

AutoML

How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study

no code implementations13 Jan 2021 David Piorkowski, Soya Park, April Yi Wang, Dakuo Wang, Michael Muller, Felix Portnoy

The development of AI applications is a multidisciplinary effort, involving multiple roles collaborating with the AI developers, an umbrella term we use to include data scientists and other AI-adjacent roles on the same team.

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.

AutoML

"Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment

no code implementations4 Jan 2021 Dakuo Wang, Liuping Wang, Zhan Zhang, Ding Wang, Haiyi Zhu, Yvonne Gao, Xiangmin Fan, Feng Tian

Artificial intelligence (AI) technology has been increasingly used in the implementation of advanced Clinical Decision Support Systems (CDSS).

Decision Making

CASS: Towards Building a Social-Support Chatbot for Online Health Community

2 code implementations4 Jan 2021 Liuping Wang, Dakuo Wang, Feng Tian, Zhenhui Peng, Xiangmin Fan, Zhan Zhang, Shuai Ma, Mo Yu, Xiaojuan Ma, Hongan Wang

Chatbots systems, despite their popularity in today's HCI and CSCW research, fall short for one of the two reasons: 1) many of the systems use a rule-based dialog flow, thus they can only respond to a limited number of pre-defined inputs with pre-scripted responses; or 2) they are designed with a focus on single-user scenarios, thus it is unclear how these systems may affect other users or the community.

Chatbot

Solving Constrained CASH Problems with ADMM

no code implementations17 Jun 2020 Parikshit Ram, Sijia Liu, Deepak Vijaykeerthi, Dakuo Wang, Djallel Bouneffouf, Greg Bramble, Horst Samulowitz, Alexander G. Gray

The CASH problem has been widely studied in the context of automated configurations of machine learning (ML) pipelines and various solvers and toolkits are available.

Fairness

How do Data Science Workers Collaborate? Roles, Workflows, and Tools

no code implementations18 Jan 2020 Amy X. Zhang, Michael Muller, Dakuo Wang

We also found that the collaborative practices workers employ, such as documentation, vary according to the kinds of tools they use.

AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates

no code implementations13 Dec 2019 Daniel Karl I. Weidele, Justin D. Weisz, Eno Oduor, Michael Muller, Josh Andres, Alexander Gray, Dakuo Wang

Artificial Intelligence (AI) can now automate the algorithm selection, feature engineering, and hyperparameter tuning steps in a machine learning workflow.

AutoML Feature Engineering

Context-Aware Conversation Thread Detection in Multi-Party Chat

no code implementations IJCNLP 2019 Ming Tan, Dakuo Wang, Yupeng Gao, Haoyu Wang, Saloni Potdar, Xiaoxiao Guo, Shiyu Chang, Mo Yu

In multi-party chat, it is common for multiple conversations to occur concurrently, leading to intermingled conversation threads in chat logs.

How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?

no code implementations8 Sep 2019 Yaoli Mao, Dakuo Wang, Michael Muller, Kush R. Varshney, Ioana Baldini, Casey Dugan, AleksandraMojsilović

Our findings suggest that besides the glitches in the collaboration readiness, technology readiness, and coupling of work dimensions, the tensions that exist in the common ground building process influence the collaboration outcomes, and then persist in the actual collaboration process.

Group Chat Ecology in Enterprise Instant Messaging: How Employees Collaborate Through Multi-User Chat Channels on Slack

no code implementations4 Jun 2019 Dakuo Wang, Haoyu Wang, Mo Yu, Zahra Ashktorab, Ming Tan

We cross-referenced 117 project teams and their team-based Slack channels and identified 57 teams that appeared in both datasets, then we built a regression model to reveal the relationship between these group communication styles and the project team performance.

An ADMM Based Framework for AutoML Pipeline Configuration

no code implementations1 May 2019 Sijia Liu, Parikshit Ram, Deepak Vijaykeerthy, Djallel Bouneffouf, Gregory Bramble, Horst Samulowitz, Dakuo Wang, Andrew Conn, Alexander Gray

We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervised learning pipelines.

AutoML

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