Deep Quaternion Features for Privacy Protection. - Hao Zhang 063; Yiting Chen 003; Liyao Xiang; Haotian Ma; Jie Shi; Quanshi Zhang (CoRR, 2020)

Rotation-Equivariant Neural Networks for Privacy Protection. - Hao Zhang 063; Yiting Chen 003; Haotian Ma; Xu Cheng 005; Qihan Ren; Liyao Xiang; Jie Shi; Quanshi Zhang (CoRR, 2020)

Game-Theoretic Interactions of Different Orders. - Hao Zhang 063; Xu Cheng 005; Yiting Chen 003; Quanshi Zhang (CoRR, 2020)

Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness. - Jie Ren 018; Die Zhang; Yisen Wang 001; Lu Chen; Zhanpeng Zhou; Yiting Chen 003; Xu Cheng 005; Xin Wang 108; Meng Zhou; Jie Shi; Quanshi Zhang (NeurIPS, 2021)

Game-theoretic Understanding of Adversarially Learned Features. - Jie Ren 018; Die Zhang; Yisen Wang 001; Lu Chen; Zhanpeng Zhou; Xu Cheng 005; Xin Wang 108; Yiting Chen 003; Jie Shi; Quanshi Zhang (CoRR, 2021)

OoD-Bench: Benchmarking and Understanding Out-of-Distribution Generalization Datasets and Algorithms. - Nanyang Ye 001; Kaican Li; Lanqing Hong; Haoyue Bai 001; Yiting Chen 003; Fengwei Zhou; Zhenguo Li (CoRR, 2021)

DICE: Domain-attack Invariant Causal Learning for Improved Data Privacy Protection and Adversarial Robustness. - Qibing Ren; Yiting Chen 003; Yichuan Mo; Qitian Wu; Junchi Yan (KDD, 2022)

Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency Domain. - Yiting Chen 003; Qibing Ren; Junchi Yan (NeurIPS, 2022)

Towards One-shot Neural Combinatorial Solvers: Theoretical and Empirical Notes on the Cardinality-Constrained Case. - Runzhong Wang; Li Shen 008; Yiting Chen 003; Xiaokang Yang 001; Dacheng Tao; Junchi Yan (ICLR, 2023)

Energy-based Out-of-Distribution Detection for Graph Neural Networks. - Qitian Wu; Yiting Chen 003; Chenxiao Yang; Junchi Yan (ICLR, 2023)

Going Beyond Neural Network Feature Similarity: The Network Feature Complexity and Its Interpretation Using Category Theory. - Yiting Chen 003; Zhanpeng Zhou; Junchi Yan (ICLR, 2024)

Graph Out-of-Distribution Detection Goes Neighborhood Shaping. - Tianyi Bao; Qitian Wu; Zetian Jiang; Yiting Chen 003; Jiawei Sun; Junchi Yan (ICML, 2024)

Unveiling The Matthew Effect Across Channels: Assessing Layer Width Sufficiency via Weight Norm Variance. - Yiting Chen 003; Jiazi Bu; Junchi Yan (NeurIPS, 2024)

What Rotary Position Embedding Can Tell Us: Identifying Query and Key Weights Corresponding to Basic Syntactic or High-level Semantic Information. - Yiting Chen 003; Junchi Yan (NeurIPS, 2024)

Regularizing Energy among Training Samples for Out-of-Distribution Generalization. - Yiting Chen 003; Qitian Wu; Junchi Yan (ICLR, 2025)