160 citations · 317 across the 55 of their papers we have counts for
57 papers
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…
Classifying and Addressing the Diversity of Errors in Retrieval-Augmented Generation Systems
Kin Kwan Leung, Mouloud Belbahri, Yi Sui +4
Retrieval-augmented generation (RAG) is a prevalent approach for building LLM-based question-answering systems that can take advantage of external knowledge databases. Due to the c…
Self-Supervised Representation Learning as Mutual Information Maximization
Akhlaqur Rahman Sabby, Yi Sui, Tongzi Wu +2
Self-supervised representation learning (SSRL) has demonstrated remarkable empirical success, yet its underlying principles remain insufficiently understood. While recent works att…
MaskSQL: Safeguarding Privacy for LLM-Based Text-to-SQL via Abstraction
Sepideh Abedini, Shubhankar Mohapatra, D. B. Emerson +3
Large language models (LLMs) have shown promising performance on tasks that require reasoning, such as text-to-SQL, code generation, and debugging. However, regulatory frameworks w…
Document Summarization with Conformal Importance Guarantees
Bruce Kuwahara, Chen-Yuan Lin, Xiao Shi Huang +5
Automatic summarization systems have advanced rapidly with large language models (LLMs), yet they still lack reliable guarantees on inclusion of critical content in high-stakes dom…
Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality
Naihe Feng, Yi Sui, Shiyi Hou +2
Existing research on Retrieval-Augmented Generation (RAG) primarily focuses on improving overall question-answering accuracy, often overlooking the quality of sub-claims within gen…
Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems
Brendan Leigh Ross, Noël Vouitsis, Atiyeh Ashari Ghomi +8
Although large language models (LLMs) are becoming increasingly capable of solving challenging real-world tasks, accurately quantifying their uncertainty remains a critical open pr…
CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
Vahid Balazadeh, Hamidreza Kamkari, Valentin Thomas +4
Causal effect estimation from observational data is fundamental across various applications. However, selecting an appropriate estimator from dozens of specialized methods demands…
Trustworthy AI Must Account for Interactions
Jesse C. Cresswell
Trustworthy AI encompasses many aspirational aspects for aligning AI systems with human values, including fairness, privacy, robustness, explainability, and uncertainty quantificat…
Zero-Execution Retrieval-Augmented Configuration Tuning of Spark Applications
Raunaq Suri, Ilan Gofman, Guangwei Yu +1
Large-scale data processing is increasingly done using distributed computing frameworks like Apache Spark, which have a considerable number of configurable parameters that affect r…
DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse Tasks
Wei Cui, Tongzi Wu, Jesse C. Cresswell +2
Meta-learning represents a strong class of approaches for solving few-shot learning tasks. Nonetheless, recent research suggests that simply pre-training a generic encoder can pote…