Publications

See Google Scholar for full details.
_{* denotes equal contribution}

Theory of Agreement-on-the-Line in Linear Models and Gaussian Data [arxiv]
Christina Baek, Aditi Raghunathan, Zico Kolter
- 2025 Artificial Intelligence and Statistics (AISTATS)

Context-Parametric Inversion: Why Instruction Finetuning May Not Actually Improve Context Reliance [arxiv]
Saching Goyal*, Christina Baek*, Zico Kolter, Aditi Raghunathan
- 2025 International Conference on Learning Representations (ICLR) (Oral)

Test-Time Adaptation Induces Stronger Accuracy and Agreement-on-the-Line [arxiv]
Eungyeup Kim, Mingjie Sun, Christina Baek, Aditi Raghunathan, J. Zico Kolter
- 2024 Neural Information Processing Systems (NeurIPS)

Why is SAM Robust to Label Noise? [arxiv]
Christina Baek, Zico Kolter, Aditi Raghunathan
- 2023 International Conference in Machine Learning (ICML) Spurious correlations, Invariance, and Stability Workshop
- 2024 International Conference on Learning Representations (ICLR)

On the Joint Interaction of Models, Data, and Features [arxiv]
Yiding Jiang, Christina Baek, Zico Kolter
- 2023 International Conference in Machine Learning (ICML) High-dimensional Learning Dynamics Workshop
- 2024 International Conference on Learning Representations (ICLR) (Oral)

Predicting the Performance of Foundation Models via Agreement-on-the-Line [arxiv]
Aman Mehra, Rahul Saxena, Taeyoun Kim, Christina Baek, Zico Kolter, Aditi Raghunathan
- 2023 Neural Information Processing Systems (NeurIPS) DistShift Workshop
- 2023 Neural Information Processing Systems (NeurIPS) R0-FoMo Workshop
- 2024 Neural Information Processing Systems (NeurIPS)

Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift [arxiv]
Christina Baek, Yiding Jiang, Aditi Raghunathan, Zico Kolter
- 2022 Neural Information Processing Systems (NeurIPS) (Oral)
- 2022 International Conference in Machine Learning (ICML) Principles of Distribution Shift Workshop

Efficient Maximal Coding Rate Reduction by Variational Forms [arxiv]
Christina Baek*, Ziyang Wu*, Kwan Ho Ryan Chan, Tianjiao Ding, Yi Ma, Benjamin D. Haeffele
- 2022 Conference of Computer Vision and Pattern Recognition (CVPR)

Computational Benefits of Intermediate Rewards for Hierarchical Planning [arxiv]
Yuexiang Zhai, Christina Baek, Zhengyuan Zhou, Jiantao Jiao, Yi Ma
- 2022 Journal of Artificial Intelligence Research (JAIR)

Assessing Generalization of SGD via Disagreement [arxiv]
Yiding Jiang*, Vaishnavh Nagarajan*, Christina Baek, and J. Zico Kolter
- 2021 International Conference in Machine Learning (ICML) Workshop on Overparameterization: Pitfalls & Opportunities
- 2022 International Conference on Learning Representations (ICLR) (Spotlight)

Incremental Learning via Rate Reduction [arxiv]
Ziyang Wu*, Christina Baek*, Chong You, and Yi Ma
- 2021 Conference of Computer Vision and Pattern Recognition (CVPR)
- 2021 International Conference in Machine Learning (ICML) Workshop on Theory and Foundation of Continual Learning (Oral)

The Landscape of Genetic Content in the Gut and Oral Human Microbiome [pubmed]
Braden Tierney, Zhen Yang, Jacob Luber, Marc Beaudin, Marsha Wibowo, Christina Baek, Chirag Patel, and Aleksandar Kostic
- Cell Host and Microbe, 26(2): 283-295, 2019

USP11 enhances TGF-b-induced epithelial-mesenchymal plasticity and human breast cancer metastasis [pubmed]
Daniel Garcia, Christina Baek, Monica Estrada, Tiffani Tysl, Eric Bennett, Jing Yang, and John Chang
- Molecular Cancer Research, 16(7): 1172-1184, 2018

Inhibition of Spontaneous and Experimental Lung Metastasis of Soft-Tissue Sarcoma by Tumor-Targeting Salmonella typhimurium A1-R [pubmed]
Shinji Miwa, Yong Zhang, Kyung-Eun Baek, Fuminari Uehara, Shuya Yano, Mako Yamamoto, Yukihiko Hiroshima, Yasunori Matsumoto, Hiroaki Kimura, Katsuhiro Hayashi, Norio Yamamoto, Michael Bouvet, Hiroyuki Tsuchiya, Robert Hoffman, and Ming Zhao.
- Oncotarget, 5(24): 12849-12861, 2014

_{Last updated Jan 22, 2024}