1 code implementation • 12 Oct 2023 • Wenlong Ji, Lihua Lei, Asher Spector
Finally, we propose an efficient computational framework, enabling implementation on many practical problems in causal inference.
1 code implementation • 13 Feb 2023 • Ryumei Nakada, Halil Ibrahim Gulluk, Zhun Deng, Wenlong Ji, James Zou, Linjun Zhang
We show that the algorithm can detect the ground-truth pairs and improve performance by fully exploiting unpaired datasets.
no code implementations • 19 Sep 2022 • Yiping Lu, Wenlong Ji, Zachary Izzo, Lexing Ying
In this paper, we propose importance tempering to improve the decision boundary and achieve consistently better results for overparameterized models.
no code implementations • ICLR 2022 • Wenlong Ji, Yiping Lu, Yiliang Zhang, Zhun Deng, Weijie J. Su
We prove that gradient flow on this model converges to critical points of a minimum-norm separation problem exhibiting neural collapse in its global minimizer.
no code implementations • 6 Oct 2021 • Wenlong Ji, Zhun Deng, Ryumei Nakada, James Zou, Linjun Zhang
Contrastive learning has achieved state-of-the-art performance in various self-supervised learning tasks and even outperforms its supervised counterpart.
no code implementations • NeurIPS 2021 • Wenlong Ji, Yiping Lu, Yiliang Zhang, Zhun Deng, Weijie J Su
In this paper, we derive a landscape analysis to the surrogate model to study the inductive bias of the neural features and parameters from neural networks with cross-entropy.