Summary of Generating Robust Counterfactual Witnesses For Graph Neural Networks, by Dazhuo Qiu et al.
Generating Robust Counterfactual Witnesses for Graph Neural Networksby Dazhuo Qiu, Mengying Wang, Arijit Khan, Yinghui…
Generating Robust Counterfactual Witnesses for Graph Neural Networksby Dazhuo Qiu, Mengying Wang, Arijit Khan, Yinghui…
URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learningby Duanyi Yao, Songze Li, Xueluan Gong,…
Data-Driven Invertible Neural Surrogates of Atmospheric Transmissionby James Koch, Brenda Forland, Bruce Bernacki, Timothy Doster,…
Neural Dynamic Data Valuationby Zhangyong Liang, Huanhuan Gao, Ji ZhangFirst submitted to arxiv on: 30…
Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interferenceby Haoxuan Li, Chunyuan Zheng,…
On Training a Neural Network to Explain Binariesby Alexander Interrante-Grant, Andy Davis, Heather Preslier, Tim…
Analyzing and Exploring Training Recipes for Large-Scale Transformer-Based Weather Predictionby Jared D. Willard, Peter Harrington,…
Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networksby Yunzhen Feng, Tim G. J.…
Landmark Alternating Diffusionby Sing-Yuan Yeh, Hau-Tieng Wu, Ronen Talmon, Mao-Pei TsuiFirst submitted to arxiv on:…
Provably Robust Conformal Prediction with Improved Efficiencyby Ge Yan, Yaniv Romano, Tsui-Wei WengFirst submitted to…