Yikai Zhang
Affiliations:- Morgan Stanley, Machine Learning Research, New York, NY, USA
- Rutgers University, Department of Computer Science, Piscataway, NJ, USA
According to our database1,
Yikai Zhang
authored at least 27 papers
between 2019 and 2025.
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Bibliography
2025
Covariate-dependent Graphical Model Estimation via Neural Networks with Statistical Guarantees.
Trans. Mach. Learn. Res., 2025
PivotAlign: Improve Semi-Supervised Learning by Learning Intra-Class Heterogeneity and Aligning with Pivots.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2025
2024
OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024
2023
Frontiers Comput. Sci., 2023
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation.
Proceedings of the International Conference on Machine Learning, 2023
Proceedings of the Eleventh International Conference on Learning Representations, 2023
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023
2022
Algorithm 1024: Spherical Triangle Algorithm: A Fast Oracle for Convex Hull Membership Queries.
ACM Trans. Math. Softw., 2022
Proceedings of the Uncertainty in Artificial Intelligence, 2022
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022
2021
Training Federated GANs with Theoretical Guarantees: A Universal Aggregation Approach.
CoRR, 2021
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Proceedings of the 9th International Conference on Learning Representations, 2021
2020
Multi-modal AsynDGAN: Learn From Distributed Medical Image Data without Sharing Private Information.
CoRR, 2020
Proceedings of the Computer Vision - ECCV 2020, 2020
Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020
2019
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019