Li, Shanda

15 publications

ICLR 2026 CoMind: Towards Community-Driven Agents for Machine Learning Engineering Sijie Li, Weiwei Sun, Shanda Li, Ameet Talwalkar, Yiming Yang

TMLR 2026 CodePDE: An Inference Framework for LLM-Driven PDE Solver Generation Shanda Li, Tanya Marwah, Junhong Shen, Weiwei Sun, Andrej Risteski, Yiming Yang, Ameet Talwalkar

ICLR 2026 FrontierCO: Real-World and Large-Scale Evaluation of Machine Learning Solvers for Combinatorial Optimization Shengyu Feng, Weiwei Sun, Shanda Li, Ameet Talwalkar, Yiming Yang

ICLR 2026 Sample Complexity and Representation Ability of Test-Time Scaling Paradigms Baihe Huang, Shanda Li, Tianhao Wu, Yiming Yang, Ameet Talwalkar, Kannan Ramchandran, Michael I. Jordan, Jiantao Jiao

ICLR 2026 Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces Mike A Merrill, Alexander Glenn Shaw, Nicholas Carlini, Boxuan Li, Harsh Raj, Ivan Bercovich, Lin Shi, Jeong Yeon Shin, Thomas Walshe, E. Kelly Buchanan, Junhong Shen, Guanghao Ye, Haowei Lin, Jason Poulos, Maoyu Wang, Marianna Nezhurina, Di Lu, Orfeas Menis Mastromichalakis, Zhiwei Xu, Zizhao Chen, Yue Liu, Robert Zhang, Leon Liangyu Chen, Anurag Kashyap, Jan-Lucas Uslu, Jeffrey Li, Jianbo Wu, Minghao Yan, Song Bian, Vedang Sharma, Ke Sun, Steven Dillmann, Akshay Anand, Andrew Lanpouthakoun, Bardia Koopah, Changran Hu, Etash Kumar Guha, Gabriel H. S. Dreiman, Jiacheng Zhu, Karl Krauth, Li Zhong, Niklas Muennighoff, Robert Kwesi Amanfu, Shangyin Tan, Shreyas Pimpalgaonkar, Tushar Aggarwal, Xiangning Lin, Xin Lan, Xuandong Zhao, Yiqing Liang, Yuanli Wang, Zilong Wang, Changzhi Zhou, David Heineman, Hange Liu, Harsh Trivedi, John Yang, Junhong Lin, Manish Shetty, Michael Yang, Nabil Omi, Negin Raoof, Shanda Li, Terry Yue Zhuo, Wuwei Lin, Yiwei Dai, Yuxin Wang, Wenhao Chai, Shang Zhou, Dariush Wahdany, Ziyu She, Jiaming Hu, Zhikang Dong, Yuxuan Zhu, Sasha Cui, Ahson Saiyed, Arinbjörn Kolbeinsson, Christopher Michael Rytting, Ryan Marten, Yixin Wang, Jenia Jitsev, Alex Dimakis, Andy Konwinski, Ludwig Schmidt

ICLR 2025 Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for LLM Problem-Solving Yangzhen Wu, Zhiqing Sun, Shanda Li, Sean Welleck, Yiming Yang

ICML 2025 Maximal Update Parametrization and Zero-Shot Hyperparameter Transfer for Fourier Neural Operators Shanda Li, Shinjae Yoo, Yiming Yang

ICLR 2025 TFG-Flow: Training-Free Guidance in Multimodal Generative Flow Haowei Lin, Shanda Li, Haotian Ye, Yiming Yang, Stefano Ermon, Yitao Liang, Jianzhu Ma

ICLR 2024 Functional Interpolation for Relative Positions Improves Long Context Transformers Shanda Li, Chong You, Guru Guruganesh, Joshua Ainslie, Santiago Ontanon, Manzil Zaheer, Sumit Sanghai, Yiming Yang, Sanjiv Kumar, Srinadh Bhojanapalli

NeurIPSW 2024 Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for LLM Problem-Solving Yangzhen Wu, Zhiqing Sun, Shanda Li, Sean Welleck, Yiming Yang

AISTATS 2024 Learning a Fourier Transform for Linear Relative Positional Encodings in Transformers Krzysztof Choromanski, Shanda Li, Valerii Likhosherstov, Kumar Avinava Dubey, Shengjie Luo, Di He, Yiming Yang, Tamas Sarlos, Thomas Weingarten, Adrian Weller

AISTATS 2023 Learning Physics-Informed Neural Networks Without Stacked Back-Propagation Di He, Shanda Li, Wenlei Shi, Xiaotian Gao, Jia Zhang, Jiang Bian, Liwei Wang, Tie-Yan Liu

NeurIPS 2022 Is $l^2$ Physics Informed Loss Always Suitable for Training Physics Informed Neural Network? Chuwei Wang, Shanda Li, Di He, Liwei Wang

NeurIPS 2022 Your Transformer May Not Be as Powerful as You Expect Shengjie Luo, Shanda Li, Shuxin Zheng, Tie-Yan Liu, Liwei Wang, Di He

NeurIPS 2021 Stable, Fast and Accurate: Kernelized Attention with Relative Positional Encoding Shengjie Luo, Shanda Li, Tianle Cai, Di He, Dinglan Peng, Shuxin Zheng, Guolin Ke, Liwei Wang, Tie-Yan Liu