activity
20192025
most citedRisk-Sensitive Reinforcement Learning: a Martingale Approach to Reward Uncertainty

26 citations · 79 across the 16 of their papers we have counts for

collaborators

17 papers

cs.AI2025

ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering

Rachneet Kaur, Nishan Srishankar, Zhen Zeng +2

Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts-those requiring precise visual inte…

cs.AI2025

TASER: Table Agents for Schema-guided Extraction and Recommendation

Nicole Cho, Kirsty Fielding, William Watson +2

Real-world financial filings report critical information about an entity's investment holdings, essential for assessing that entity's risk, profitability, and relationship profile.…

cs.LG2024

Scalable Representation Learning for Multimodal Tabular Transactions

Natraj Raman, Sumitra Ganesh, Manuela Veloso

Large language models (LLMs) are primarily designed to understand unstructured text. When directly applied to structured formats such as tabular data, they may struggle to discern…

cs.GT2023

Learning Payment-Free Resource Allocation Mechanisms

Sihan Zeng, Sujay Bhatt, Eleonora Kreacic +3

We consider the design of mechanisms that allocate limited resources among self-interested agents using neural networks. Unlike the recent works that leverage machine learning for…

cs.AI2023★ 1 cited

O3D: Offline Data-driven Discovery and Distillation for Sequential Decision-Making with Large Language Models

Yuchen Xiao, Yanchao Sun, Mengda Xu +4

Recent advancements in large language models (LLMs) have exhibited promising performance in solving sequential decision-making problems. By imitating few-shot examples provided in…

cs.LG2023

Sequential Fair Resource Allocation under a Markov Decision Process Framework

Parisa Hassanzadeh, Eleonora Kreacic, Sihan Zeng +2

We study the sequential decision-making problem of allocating a limited resource to agents that reveal their stochastic demands on arrival over a finite horizon. Our goal is to des…