1 citations · 1 across the 12 of their papers we have counts for
12 papers
TLRD: Teaching LLMs to Reason over Tabular Data with Tri-Level Rationale Distillation
Tianyuan Liang, Xuwei Tan, Lei Shi +6
Tabular data is a primary medium for storing real-world information, driving many industrial applications of machine learning. Traditional predictors achieve strong predictive perf…
CAAL: Contextual Bandits based Online Hand-Craft Active Learning Strategy Selection
Shao-An Yin, Jiacong Li, Tianpei Xie +3
The challenge with active learning algorithms is the uncertainty of the statistical distribution of unlabeled data, making it difficult to choose the best hand-crafted strategy. To…
BiasCause: Evaluate Socially Biased Causal Reasoning of Large Language Models
Tian Xie, Tongxin Yin, Vaishakh Keshava +2
While large language models (LLMs) play increasingly significant roles in society, research shows they continue to generate content that reflects social bias against sensitive grou…
How Strategic Agents Respond: Comparing Analytical Models with LLM-Generated Responses in Strategic Classification
Tian Xie, Pavan Rauch, Xueru Zhang
When ML algorithms are deployed to automate human-related decisions, human agents may learn the underlying decision policies and adapt their behavior. Strategic Classification (SC)…
Lookahead Counterfactual Fairness
Zhiqun Zuo, Tian Xie, Xuwei Tan +2
As machine learning (ML) algorithms are used in applications that involve humans, concerns have arisen that these algorithms may be biased against certain social groups. \textit{Co…
Evaluating and Mitigating Social Bias for Large Language Models in Open-ended Settings
Zhao Liu, Tian Xie, Xueru Zhang
Current social bias benchmarks for Large Language Models (LLMs) primarily rely on predefined question formats like multiple-choice, limiting their ability to reflect the complexity…
ProFL: Performative Robust Optimal Federated Learning
Xue Zheng, Tian Xie, Xuwei Tan +2
Performative prediction is a framework that captures distribution shifts that occur during the training of machine learning models due to their deployment. As the trained model is…
Addressing Polarization and Unfairness in Performative Prediction
Kun Jin, Tian Xie, Yang Liu +1
In many real-world applications of machine learning such as recommendations, hiring, and lending, deployed models influence the data they are trained on, leading to feedback loops…
Automating Data Annotation under Strategic Human Agents: Risks and Potential Solutions
Tian Xie, Xueru Zhang
As machine learning (ML) models are increasingly used in social domains to make consequential decisions about humans, they often have the power to reshape data distributions. Human…
Non-linear Welfare-Aware Strategic Learning
Tian Xie, Xueru Zhang
This paper studies algorithmic decision-making in the presence of strategic individual behaviors, where an ML model is used to make decisions about human agents and the latter can…
Algorithmic Decision-Making under Agents with Persistent Improvement
Tian Xie, Xuwei Tan, Xueru Zhang
This paper studies algorithmic decision-making under human's strategic behavior, where a decision maker uses an algorithm to make decisions about human agents, and the latter with…
Learning under Imitative Strategic Behavior with Unforeseeable Outcomes
Tian Xie, Zhiqun Zuo, Mohammad Mahdi Khalili +1
Machine learning systems have been widely used to make decisions about individuals who may behave strategically to receive favorable outcomes, e.g., they may genuinely improve the…