Search Results for author: Wenlong Ji

Found 6 papers, 2 papers with code

Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal Effects

1 code implementation12 Oct 2023 Wenlong Ji, Lihua Lei, Asher Spector

Finally, we propose an efficient computational framework, enabling implementation on many practical problems in causal inference.

Causal Inference valid

Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data

1 code implementation13 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.

Contrastive Learning

Importance Tempering: Group Robustness for Overparameterized Models

no code implementations19 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.

imbalanced classification

An Unconstrained Layer-Peeled Perspective on Neural Collapse

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.

The Power of Contrast for Feature Learning: A Theoretical Analysis

no code implementations6 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.

Contrastive Learning Self-Supervised Learning +1

How Gradient Descent Separates Data with Neural Collapse: A Layer-Peeled Perspective

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.

Inductive Bias

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