| 2026 | ICALP | Optimal k-Secretary with Logarithmic Memory. | Mingda Qiao, Wei Zhang |
| 2025 | COLT | Truthfulness of Decision-Theoretic Calibration Measures. | Mingda Qiao, Eric Zhao |
| 2025 | SODA | Platforms for Efficient and Incentive-Aware Collaboration. | Nika Haghtalab, Mingda Qiao, Kunhe Yang |
| 2024 | COLT | On the Distance from Calibration in Sequential Prediction. | Mingda Qiao, Letian Zheng |
| 2024 | ICML | Collaborative Learning with Different Labeling Functions. | Yuyang Deng, Mingda Qiao |
| 2021 | COLT | Exponential Weights Algorithms for Selective Learning. | Mingda Qiao, Gregory Valiant |
| 2021 | FOCS | Properly learning decision trees in almost polynomial time. | Guy Blanc, Jane Lange, Mingda Qiao, Li-Yang Tan |
| 2021 | STOC | Stronger calibration lower bounds via sidestepping. | Mingda Qiao, Gregory Valiant |
| 2020 | ICLR | On Generalization Error Bounds of Noisy Gradient Methods for Non-Convex Learning. | Jian Li, Xuanyuan Luo, Mingda Qiao |
| 2019 | AAAI | Low-Distortion Social Welfare Functions. | Gerdus Benad, Ariel D. Procaccia, Mingda Qiao |
| 2019 | COLT | A Theory of Selective Prediction. | Mingda Qiao, Gregory Valiant |
| 2018 | ICML | Do Outliers Ruin Collaboration? | Mingda Qiao |
| 2017 | AISTATS | Nearly Instance Optimal Sample Complexity Bounds for Top-k Arm Selection. | Lijie Chen, Jian Li, Mingda Qiao |
| 2017 | COLT | Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration. | Lijie Chen, Anupam Gupta, Jian Li, Mingda Qiao, Ruosong Wang |
| 2017 | COLT | Towards Instance Optimal Bounds for Best Arm Identification. | Lijie Chen, Jian Li, Mingda Qiao |