Improved Analysis of Clipping Algorithms for Non-convex Optimization. - Bohang Zhang; Jikai Jin; Cong Fang 001; Liwei Wang 001 (NeurIPS, 2020)

Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons. - Bohang Zhang; Tianle Cai; Zhou Lu; Di He 001; Liwei Wang 001 (ICML, 2021)

Non-convex Distributionally Robust Optimization: Non-asymptotic Analysis. - Jikai Jin; Bohang Zhang; Haiyang Wang; Liwei Wang 001 (NeurIPS, 2021)

Towards Certifying 𝓁∞ Robustness using Neural Networks with 𝓁∞-dist Neurons. - Bohang Zhang; Tianle Cai; Zhou Lu; Di He 001; Liwei Wang 001 (CoRR, 2021)

Boosting the Certified Robustness of L-infinity Distance Nets. - Bohang Zhang; Du Jiang; Di He 001; Liwei Wang 001 (ICLR, 2022)

Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective. - Bohang Zhang; Du Jiang; Di He 001; Liwei Wang 001 (NeurIPS, 2022)

Rethinking the Expressive Power of GNNs via Graph Biconnectivity. - Bohang Zhang; Shengjie Luo; Liwei Wang 001; Di He 001 (ICLR, 2023)

Finding Generalization Measures by Contrasting Signal and Noise. - Jiaye Teng; Bohang Zhang; Ruichen Li; Haowei He; Yequan Wang; Yan Tian; Yang Yuan (ICML, 2023)

A Complete Expressiveness Hierarchy for Subgraph GNNs via Subgraph Weisfeiler-Lehman Tests. - Bohang Zhang; Guhao Feng; Yiheng Du; Di He 001; Liwei Wang 001 (ICML, 2023)

Enhanced Sliding Window Superposition Coding for Industrial Automation. - Bohang Zhang; Zhaojun Nan; Sheng Zhou 001; Zhisheng Niu (IWCMC, 2023)

Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspective. - Guhao Feng; Bohang Zhang; Yuntian Gu; Haotian Ye; Di He 001; Liwei Wang 001 (NeurIPS, 2023)

Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness. - Bohang Zhang; Jingchu Gai; Yiheng Du; Qiwei Ye; Di He 001; Liwei Wang 001 (ICLR, 2024)

Do Efficient Transformers Really Save Computation? - Kai Yang; Jan Ackermann; Zhenyu He 012; Guhao Feng; Bohang Zhang; Yunzhen Feng; Qiwei Ye; Di He 001; Liwei Wang 001 (ICML, 2024)

On the Expressive Power of Spectral Invariant Graph Neural Networks. - Bohang Zhang; Lingxiao Zhao; Haggai Maron (ICML, 2024)

Can Graph Learning Improve Planning in LLM-based Agents? - Xixi Wu; Yifei Shen; Caihua Shan; Kaitao Song; Siwei Wang; Bohang Zhang; Jiarui Feng; Hong Cheng; Wei Chen; Yun Xiong; Dongsheng Li (NeurIPS, 2024)

Can Graph Learning Improve Task Planning? - Xixi Wu; Yifei Shen; Caihua Shan; Kaitao Song; Siwei Wang; Bohang Zhang; Jiarui Feng; Hong Cheng; Wei Chen; Yun Xiong; Dongsheng Li (CoRR, 2024)

How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension. - Xinnan Dai; Haohao Qu; Yifen Shen; Bohang Zhang; Qihao Wen; Wenqi Fan; Dongsheng Li 002; Jiliang Tang; Caihua Shan (CoRR, 2024)

Ultimate axial strength prediction of concrete-filled double-skin steel tube columns using soft computing methods. - Bohang Zhang; Xinhua Xue (Eng. Appl. Artif. Intell., 2024)

How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension. - Xinnan Dai; Haohao Qu; Yifei Shen; Bohang Zhang; Qihao Wen; Wenqi Fan; Dongsheng Li 002; Jiliang Tang; Caihua Shan (ICLR, 2025)

Homomorphism Expressivity of Spectral Invariant Graph Neural Networks. - Jingchu Gai; Yiheng Du; Bohang Zhang; Haggai Maron; Liwei Wang 001 (ICLR, 2025)

Beyond Atoms: Enhancing Molecular Pretrained Representations with 3D Space Modeling. - Shuqi Lu; Xiaohong Ji; Bohang Zhang; Lin Yao 003; Siyuan Liu; Zhifeng Gao; Linfeng Zhang 002; Guolin Ke (CoRR, 2025)

From Sequence to Structure: Uncovering Substructure Reasoning in Transformers. - Xinnan Dai; Kai Yang; Jay Revolinsky; Kai Guo 003; Aoran Wang; Bohang Zhang; Jiliang Tang (CoRR, 2025)