738 citations · 909 across the 16 of their papers we have counts for
18 papers
Greedy Decoding Is Not Precision-Invariant: Cross-Precision Output Divergence in LLM Inference
Gaoyuan Du, Anam Nawaz Khan, Rex Zhou +4
Greedy decoding from large language models is commonly treated as deterministic. We show it is not precision-invariant: the same model, prompt, and decoding algorithm produce diffe…
When Does Context Routing Help? A Systematic Study of Multi-Modal Fusion in Time Series Forecasting
Ruizhe Zhou, Gaoyuan Du, Xiaoyang Liu +4
Multi-modal time series forecasting methods integrate auxiliary context into temporal predictions through increasingly sophisticated fusion mechanisms. A growing body of work repor…
SPADE: Faster Drug Discovery by Learning from Sparse Data
Rahul Nandakumar, Ben Fauber, Deepayan Chakrabarti
Drug discovery seeks molecules (ligands) that bind strongly and selectively to a target protein. However, fewer than 5% of candidate ligands pass the bar for even the early stages…
COPYCOP: Ownership Verification for Graph Neural Networks
Rahul Nandakumar, Deepayan Chakrabarti
Given two GNNs that output node embeddings, how can we determine if they were trained independently? An adversary could have trained one GNN specifically to mimic the other GNN's e…
From Accuracy to Auditability: A Survey of Determinism in Financial AI Systems
Ruizhe Zhou, Xiaoyang Liu, Gaoyuan Du +4
Deploying machine learning in regulated financial environments -- credit risk, fraud detection, and anti-money laundering -- exposes critical vulnerabilities in algorithmic reprodu…
Learning to Query History: Nonstationary Classification via Learned Retrieval
Jimmy Gammell, Bishal Thapaliya, Yoon Jung +3
Nonstationarity is ubiquitous in practical classification settings, leading deployed models to perform poorly even when they generalize well to holdout sets available at training t…