| 2025 | AISTATS | Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence. | Berfin Simsek, Amire Bendjeddou, Daniel Hsu |
| 2025 | COLT | Learning Compositional Functions with Transformers from Easy-to-Hard Data. | Zixuan Wang, Eshaan Nichani, Alberto Bietti, Alex Damian, Daniel Hsu, Jason D. Lee, Denny Wu |
| 2024 | COLT | On the sample complexity of parameter estimation in logistic regression with normal design. | Daniel Hsu, Arya Mazumdar |
| 2024 | ICML | Multi-group Learning for Hierarchical Groups. | Samuel Deng, Daniel Hsu |
| 2024 | ICML | Transformers, parallel computation, and logarithmic depth. | Clayton Sanford, Daniel Hsu, Matus Telgarsky |
| 2024 | ICML | Transformers Provably Learn Sparse Token Selection While Fully-Connected Nets Cannot. | Zixuan Wang, Stanley Wei, Daniel Hsu, Jason D. Lee |
| 2022 | AISTATS | Learning Tensor Representations for Meta-Learning. | Samuel Deng, Yilin Guo, Daniel Hsu, Debmalya Mandal |
| 2022 | ICML | Simple and near-optimal algorithms for hidden stratification and multi-group learning. | Christopher J. Tosh, Daniel Hsu |
| 2021 | AISTATS | On the proliferation of support vectors in high dimensions. | Daniel Hsu, Vidya Muthukumar, Ji Xu |
| 2021 | ALT | Contrastive learning, multi-view redundancy, and linear models. | Christopher Tosh, Akshay Krishnamurthy, Daniel Hsu |
| 2021 | COLT | On the Approximation Power of Two-Layer Networks of Random ReLUs. | Daniel Hsu, Clayton Sanford, Rocco A. Servedio, Emmanouil V. Vlatakis-Gkaragkounis |
| 2021 | ICLR | Generalization bounds via distillation. | Daniel Hsu, Ziwei Ji, Matus Telgarsky, Lan Wang |
| 2020 | AISTATS | Diameter-based Interactive Structure Discovery. | Christopher Tosh, Daniel Hsu |
| 2020 | EMNLP | Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher. | Giannis Karamanolakis, Daniel Hsu, Luis Gravano |
| 2019 | AISTATS | Correcting the bias in least squares regression with volume-rescaled sampling. | Michal Derezinski, Manfred K. Warmuth, Daniel Hsu |
| 2019 | ALT | Attribute-efficient learning of monomials over highly-correlated variables. | Alexandr Andoni, Rishabh Dudeja, Daniel Hsu, Kiran Vodrahalli |
| 2019 | EMNLP | Leveraging Just a Few Keywords for Fine-Grained Aspect Detection Through Weakly Supervised Co-Training. | Giannis Karamanolakis, Daniel Hsu, Luis Gravano |
| 2019 | ICML | A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization. | Yucheng Chen, Matus Telgarsky, Chao Zhang, Bolton Bailey, Daniel Hsu, Jian Peng |
| 2019 | ICML | Teaching a black-box learner. | Sanjoy Dasgupta, Daniel Hsu, Stefanos Poulis, Xiaojin Zhu |
| 2019 | SP | Certified Robustness to Adversarial Examples with Differential Privacy. | Mathias Lcuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, Suman Jana |
| 2019 | SOSP | Privacy accounting and quality control in the sage differentially private ML platform. | Mathias Lcuyer, Riley Spahn, Kiran Vodrahalli, Roxana Geambasu, Daniel Hsu |
| 2018 | COLT | Learning Single-Index Models in Gaussian Space. | Rishabh Dudeja, Daniel Hsu |
| 2017 | ALT | Parameter identification in Markov chain choice models. | Arushi Gupta, Daniel Hsu |
| 2009 | IC3K | Constructing a Computer Simulated Experiment Testing System. | Ben Yang, Shardrom Johnson, Daniel Hsu |
| 2004 | CHI | a CAPpella: programming by demonstration of context-aware applications. | Anind K. Dey, Raffay Hamid, Chris Beckmann, Ian Li, Daniel Hsu |