1 citations · 1 across the 9 of their papers we have counts for
9 papers
U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster
Salva Rühling Cachay, Duncan Watson-Parris, Rose Yu
AI-based weather forecasting now rivals traditional physics-based ensembles, but state-of-the-art (SOTA) models rely on specialized architectures and massive computational budgets,…
Calibrating LLMs with Semantic-level Reward
Fengfei Yu, Ruijia Niu, Dongxia Wu +2
As large language models (LLMs) are deployed in consequential settings such as medical question answering and legal reasoning, the ability to estimate when their outputs are likely…
Divide and Learn: Multi-Objective Combinatorial Optimization at Scale
Esha Singh, Dongxia Wu, Chien-Yi Yang +3
Multi-objective combinatorial optimization seeks Pareto-optimal solutions over exponentially large discrete spaces, yet existing methods sacrifice generality, scalability, or theor…
SimulRAG: Simulator-based RAG for Grounding LLMs in Long-form Scientific QA
Haozhou Xu, Dongxia Wu, Matteo Chinazzi +3
Large Language Models (LLMs) show promise in generating long-form scientific explanations that synthesize evidence and connect multiple factors. However, in long-form scientific qu…
Discovering Latent Causal Graphs from Spatiotemporal Data
Kun Wang, Sumanth Varambally, Duncan Watson-Parris +2
Many important phenomena in scientific fields like climate, neuroscience, and epidemiology are naturally represented as spatiotemporal gridded data with complex interactions. Infer…
Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs
Ruijia Niu, Dongxia Wu, Rose Yu +1
Accurate uncertainty quantification in large language models (LLMs) is essential for reliable confidence estimation, yet fine-tuned LLMs often become overconfident under limited ad…
Diffusion-BBO: Diffusion-Based Inverse Modeling for Online Black-Box Optimization
Dongxia Wu, Nikki Lijing Kuang, Ruijia Niu +2
Online black-box optimization (BBO) aims to optimize an objective function by iteratively querying a black-box oracle in a sample-efficient way. While prior studies focus on forwar…
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes
Dongxia Wu, Tsuyoshi Idé, Aurélie Lozano +5
We address the problem of learning Granger causality from asynchronous, interdependent, multi-type event sequences. In particular, we are interested in discovering instance-level c…
Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling
Ruijia Niu, Dongxia Wu, Kai Kim +3
Multi-fidelity surrogate modeling aims to learn an accurate surrogate at the highest fidelity level by combining data from multiple sources. Traditional methods relying on Gaussian…