Nearly Instance Optimal Sample Complexity Bounds for Top-k Arm Selection. - Lijie Chen 001; Jian Li 015; Mingda Qiao (AISTATS, 2017)

Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration. - Lijie Chen 001; Anupam Gupta 001; Jian Li 015; Mingda Qiao; Ruosong Wang (COLT, 2017)

Towards Instance Optimal Bounds for Best Arm Identification. - Lijie Chen 001; Jian Li 015; Mingda Qiao (COLT, 2017)

Collaborative PAC Learning. - Avrim Blum; Nika Haghtalab; Ariel D. Procaccia; Mingda Qiao (NIPS, 2017)

Practical Algorithms for Best-K Identification in Multi-Armed Bandits. - Haotian Jiang; Jian Li 015; Mingda Qiao (CoRR, 2017)

Do Outliers Ruin Collaboration? - Mingda Qiao (ICML, 2018)

Learning Discrete Distributions from Untrusted Batches. - Mingda Qiao; Gregory Valiant (ITCS, 2018)

Low-Distortion Social Welfare Functions. - Gerdus Benadè; Ariel D. Procaccia; Mingda Qiao (AAAI, 2019)

A Theory of Selective Prediction. - Mingda Qiao; Gregory Valiant (COLT, 2019)

On Generalization Error Bounds of Noisy Gradient Methods for Non-Convex Learning. - Jian Li 015; Xuanyuan Luo; Mingda Qiao (ICLR, 2020)

Decision Tree Heuristics Can Fail, Even in the Smoothed Setting. - Guy Blanc; Jane Lange; Mingda Qiao; Li-Yang Tan (APPROX-RANDOM, 2021)

Exponential Weights Algorithms for Selective Learning. - Mingda Qiao; Gregory Valiant (COLT, 2021)

Stronger calibration lower bounds via sidestepping. - Mingda Qiao; Gregory Valiant (STOC, 2021)

A Fourier Approach to Mixture Learning. - Mingda Qiao; Guru Guruganesh; Ankit Singh Rawat; Kumar Avinava Dubey; Manzil Zaheer (NeurIPS, 2022)

Open Problem: Properly learning decision trees in polynomial time? - Guy Blanc; Jane Lange; Mingda Qiao; Li-Yang Tan (CoRR, 2022)

Properly Learning Decision Trees in almost Polynomial Time. - Guy Blanc; Jane Lange; Mingda Qiao; Li-Yang Tan (J. ACM, 2022)

Online Pen Testing. - Mingda Qiao; Gregory Valiant (ITCS, 2023)

On the Distance from Calibration in Sequential Prediction. - Mingda Qiao; Letian Zheng (COLT, 2024)

Collaborative Learning with Different Labeling Functions. - Yuyang Deng; Mingda Qiao (ICML, 2024)

A Combinatorial Approach to Robust PCA. - Weihao Kong; Mingda Qiao; Rajat Sen (ITCS, 2024)

Truthfulness of Calibration Measures. - Nika Haghtalab; Mingda Qiao; Kunhe Yang; Eric Zhao 003 (NeurIPS, 2024)

Truthfulness of Decision-Theoretic Calibration Measures. - Mingda Qiao; Eric Zhao 003 (COLT, 2025)

Leakage-Robust Bayesian Persuasion. - Nika Haghtalab; Mingda Qiao; Kunhe Yang (EC, 2025)

Platforms for Efficient and Incentive-Aware Collaboration. - Nika Haghtalab; Mingda Qiao; Kunhe Yang (SODA, 2025)

Optimal k-Secretary with Logarithmic Memory. - Mingda Qiao; Wei Zhang (CoRR, 2025)

Online Prediction with Limited Selectivity. - Licheng Liu; Mingda Qiao (CoRR, 2025)