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You can view all the papers in reverse
chronological order, sets of papers related to broad categories such as
machine learning,
natural language processing,
biology,
chemistry,
or physics, or papers in more specific areas including
game theory, inference, semi-supervised learning , information retrieval, or
reinforcement learning.
The list does not include all recent preprints from arXiv or
bioRxiv.
For a more complete list, see my
Google scholar page
Game related papers
- Abhi Gupta, Polina Barabanshchikova, Vikas Garg, Samuel Kaski, and Tommi Jaakkola.
Divide-and-Denoise: A Game-Theoretic Method for Fairly Composing Diffusion Models.
International Conference on Machine Learning (ICML), 2026.
[link]
- Timur Garipov, Sebastiaan De Peuter, Ge Yang, Vikas Garg, Samuel Kaski, and Tommi Jaakkola.
Compositional Sculpting of Iterative Generative Processes.
Neural Information Processing Systems (NeurIPS), 2023.
[link]
- Shangyuan Tong, Timur Garipov, Yang Zhang, Shiyu Chang, and Tommi Jaakkola.
Adversarial support alignment.
The Tenth International Conference on Learning Representations (ICLR), 2022.
[pdf]
- Mo Yu, Yang Zhang, Shiyu Chang, and Tommi S. Jaakkola.
Understanding Interlocking Dynamics of Cooperative Rationalization.
Neural Information Processing Systems (NeurIPS), 2021.
[link]
- Peiyuan Liao, Han Zhao, Keyulu Xu, Tommi Jaakkola, Geoffrey Gordon, Stefanie Jegelka, and Ruslan Salakhutdinov.
Information Obfuscation of Graph Neural Networks.
International Conference on Machine Learning (ICML), 2021.
[link]
- Vikas K. Garg and Tommi Jaakkola.
Predicting deliberative outcomes.
International Conference on Machine Learning (ICML), 2020.
[pdf]
- Shiyu Chang, Yang Zhang, Mo Yu, and Tommi Jaakkola.
Invariant Rationalization.
International Conference on Machine Learning (ICML), 2020.
[link]
- Shiyu Chang, Yang Zhang, Mo Yu, and Tommi Jaakkola.
A Game Theoretic Approach to Class-wise Selective Rationalization.
Neural Information Processing Systems (NeurIPS), 2019.
[pdf]
- Guang-He Lee, Yang Yuan, Shiyu Chang, and Tommi S. Jaakkola.
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers.
Neural Information Processing Systems (NeurIPS), 2019.
[pdf]
- Mo Yu, Shiyu Chang, Yang Zhang, and Tommi Jaakkola.
Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control.
Empirical Methods in Natural Language Processing (EMNLP), 2019.
[pdf]
- Guang-He Lee, Wengong Jin, David Alvarez Melis, and Tommi S. Jaakkola.
Functional Transparency for Structured Data: a Game-Theoretic Approach.
International Conference on Machine Learning (ICML), 2019.
[link]
- Guang-He Lee, David Alvarez Melis, and Tommi S. Jaakkola.
Game theoretic interpretability for temporal modeling.
Fairness, Accountability, and Transparency in Machine Learning (ICML workshop), 2018.
[link]
- Vikas K. Garg and Tommi Jaakkola.
Local Aggregative Games.
Advances in Neural Information Processing Systems (NIPS), 2017.
[pdf]
- Yuan Zhang, Regina Barzilay, and Tommi Jaakkola.
Aspect-augmented Adversarial Networks for Domain Adaptation.
Transactions of the Association for Computational Linguistics (TACL), 2017.
[pdf]
- Mingmin Zhao, Shichao Yue, Dina Katabi, Tommi S. Jaakkola, and Matt T. Bianchi.
Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture.
International Conference on Machine Learning (ICML), 2017.
[pdf]
- Vikas K. Garg and Tommi Jaakkola.
Learning Tree Structured Potential Games.
Advances in Neural Information Processing Systems (NIPS), 2016.
[pdf]
- Tao Lei, Regina Barzilay, and Tommi Jaakkola.
Rationalizing Neural Predictions.
Empirical Methods in Natural Language Processing (EMNLP), 2016.
[pdf]
- Luis Perez-Breva, Luis E. Ortiz, Chen-Hsiang Yeang, and Tommi Jaakkola.
Game theoretic algorithms for protein-DNA binding.
Advances in Neural Information Processing Systems (NIPS), 2006.
[pdf]
