160 citations · 317 across the 55 of their papers we have counts for
14 papers · 1 filter
Dataset Inference for Data Provenance and Privacy Auditing in Tabular Foundation Models
Dariush Wahdany, Jesse Cresswell, Naiqing Guan +3
Tabular foundation models (TFMs) are increasingly deployed through black-box APIs and trained on large collections of real-world tabular datasets. As this training paradigm becomes…
Unifying Conformal Language Tasks with In-Context Ensembles
Xiao Shi Huang, Chen-Yuan Lin, Bruce Kuwahara +2
Many NLP tasks, such as summarization and extractive question answering, reduce to retrieving relevant content from documents under two constraints: coverage, retaining enough pert…
Causal Foundation Models
Christopher Stith, Hossein Rahmani, Jesse C. Cresswell
Causal inference is the practice of estimating the effect of a treatment or intervention from data. It traditionally requires a bespoke pipeline for every new problem: first propos…
Training Fair Tabular Foundation Models
Patrik Kenfack, Jesse C. Cresswell, Anthony L. Caterini +2
Tabular Foundation Models (TFMs) have emerged as leading methods for tabular predictive tasks, leveraging in-context learning to predict on new data without task-specific training.…
LakeQuest: A Three-Domain Benchmark for Grounded Question Answering across Data Lakes
Michael Solodko, Steven Gong, Guangwei Yu +3
While modern question answering (QA) systems excel on clean, schema-aligned corpora, real-world knowledge is rarely so neatly packaged. Answering questions over enterprise and scie…
TabPATE: Differentially Private Tabular In-Context Learning Without Public Data
Dariush Wahdany, Matthew Jagielski, Jesse C. Cresswell +2
Tabular foundation models enable accurate in-context learning (ICL) from small labeled datasets, but the private records placed in context can leak through model predictions. We fi…
A Gradient Perspective on RLVR Stability and Winner Advantage Policy Optimization
Prasanth YSS, Zhichen Ren, Rasa Hosseinzadeh +6
Reinforcement learning with verifiable rewards (RLVR) improves language-model reasoning, but GRPO-style optimization remains prone to collapse. We analyse this instability through…
Agentic Monte Carlo: Simulating Reinforcement Learning for Black-Box Agents
Dae Yon Hwang, Raunaq Suri, Valentin Villecroze +4
LLM agents operate in two distinct regimes: open-weight agents amenable to reinforcement learning (RL) and black-box agents whose behaviour must be controlled purely at test time.…
RankJudge: A Multi-Turn LLM-as-a-Judge Synthetic Benchmark Generator
Zhenwei Tang, Zhaoyan Liu, Rasa Hosseinzadeh +3
As interactive LLM-based applications are created and refined, model developers need to evaluate the quality of generated text along many possible axes. For simpler systems, human…
On the Burden of Achieving Fairness in Conformal Prediction
Ziang Gao, Pengqi Liu, Archer Yi Yang +3
Conformal prediction is often calibrated with a single pooled threshold, but this can hide cross-group heterogeneity in score distributions and distort group-wise coverage. We stud…
Conformal Agent Error Attribution
Naihe Feng, Yi Sui, Shiyi Hou +2
When multi-agent systems (MAS) fail, identifying where the decisive error occurred is the first step for automated recovery to an earlier state. Error attribution remains a fundame…
Chartographer: Counterfactual Chart Generation for Evaluating Vision-Language Models
Yifan Jiang, Dae Yon Hwang, Jesse C. Cresswell +1
Chart question-answering (QA) benchmarks aim to pose questions that require visual reasoning to correctly answer, but vision-language models (VLMs) can often reach solutions throug…
Causal Foundation Models with Continuous Treatments
Christopher Stith, Medha Barath, Vahid Balazadeh +2
Causal inference, estimating causal effects from observational data, is a fundamental tool in many disciplines. Of particular importance across a variety of domains is the continuo…
Beyond Procedure: Substantive Fairness in Conformal Prediction
Pengqi Liu, Zijun Yu, Mouloud Belbahri +3
Conformal prediction (CP) offers distribution-free uncertainty quantification for machine learning models, yet its interplay with fairness in downstream decision-making remains und…