14 citations · 33 across the 8 of their papers we have counts for
7 papers · 1 filter
Evaluating LLMs When They Do Not Know the Answer: Statistical Evaluation of Mathematical Reasoning via Comparative Signals
Zihan Dong, Zhixian Zhang, Yang Zhou +3
Evaluating mathematical reasoning in LLMs is constrained by limited benchmark sizes and inherent model stochasticity, yielding high-variance accuracy estimates and unstable ranking…
Uplift Modeling based on Graph Neural Network Combined with Causal Knowledge
Haowen Wang, Xinyan Ye, Yangze Zhou +3
Uplift modeling is a fundamental component of marketing effect modeling, which is commonly employed to evaluate the effects of treatments on outcomes. Through uplift modeling, we c…
Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation Types
Jincheng Zhou, Yucheng Zhang, Jianfei Gao +2
The task of fully inductive link prediction in knowledge graphs has gained significant attention, with various graph neural networks being proposed to address it. This task present…
Bias Challenges in Counterfactual Data Augmentation
S Chandra Mouli, Yangze Zhou, Bruno Ribeiro
Deep learning models tend not to be out-of-distribution robust primarily due to their reliance on spurious features to solve the task. Counterfactual data augmentations provide a g…
OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs
Yangze Zhou, Gitta Kutyniok, Bruno Ribeiro
This work provides the first theoretical study on the ability of graph Message Passing Neural Networks (gMPNNs) -- such as Graph Neural Networks (GNNs) -- to perform inductive out-…
Set Twister for Single-hop Node Classification
Yangze Zhou, Vinayak Rao, Bruno Ribeiro
Node classification is a central task in relational learning, with the current state-of-the-art hinging on two key principles: (i) predictions are permutation-invariant to the orde…
Size-Invariant Graph Representations for Graph Classification Extrapolations
Beatrice Bevilacqua, Yangze Zhou, Bruno Ribeiro
In general, graph representation learning methods assume that the train and test data come from the same distribution. In this work we consider an underexplored area of an otherwis…