11 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…
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…
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…
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)…