283 citations · 2.1k across the 112 of their papers we have counts for
11 papers · 1 filter
Adaptive Auto-Harness: Sustained Self-Improvement for Agentic System Deployment on Open-Ended Task Streams
Zewen Liu, Zhan Shi, Yisi Sang +7
Auto-harness systems such as A-Evolve, GEPA, and Meta-Harness improve LLM agents by optimizing prompts, skills, tools, memories, and supporting infrastructure from execution feedba…
WEBSERV: A Full-Stack and RL-Ready Web Environment for Training Web Agents at Scale
Yuxuan Lu, Ziyi Wang, Jing Huang +10
Reinforcement learning (RL) for web agents demands environments that are both effective for evaluation and efficient enough for large-scale on-policy training. Current web environm…
SepsisCalc: Integrating Clinical Calculators into Early Sepsis Prediction via Dynamic Temporal Graph Construction
Changchang Yin, Shihan Fu, Bingsheng Yao +5
Sepsis is an organ dysfunction caused by a deregulated immune response to an infection. Early sepsis prediction and identification allow for timely intervention, leading to improve…
SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing
Changchang Yin, Pin-Yu Chen, Bingsheng Yao +3
Sepsis is the leading cause of in-hospital mortality in the USA. Early sepsis onset prediction and diagnosis could significantly improve the survival of sepsis patients. Existing p…
Human-Centered AI for Data Science: A Systematic Approach
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 a…
Preserve, Promote, or Attack? GNN Explanation via Topology Perturbation
Yi Sun, Abel Valente, Sijia Liu +1
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 node…
How Much Automation Does a Data Scientist Want?
Dakuo Wang, Q. Vera Liao, Yunfeng Zhang +5
Data science and machine learning (DS/ML) are at the heart of the recent advancements of many Artificial Intelligence (AI) applications. There is an active research thread in AI, \…
Solving Constrained CASH Problems with ADMM
Parikshit Ram, Sijia Liu, Deepak Vijaykeerthi +5
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. However, CASH…
Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems
Jaimie Drozdal, Justin Weisz, Dakuo Wang +6
We explore trust in a relatively new area of data science: Automated Machine Learning (AutoML). In AutoML, AI methods are used to generate and optimize machine learning models by a…
AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates
Daniel Karl I. Weidele, Justin D. Weisz, Eno Oduor +4
Artificial Intelligence (AI) can now automate the algorithm selection, feature engineering, and hyperparameter tuning steps in a machine learning workflow. Commonly known as AutoML…
An ADMM Based Framework for AutoML Pipeline Configuration
Sijia Liu, Parikshit Ram, Deepak Vijaykeerthy +6
We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervis…