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Mingda Qiao
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2020 – today
- 2024
- [c18]Mingda Qiao, Letian Zheng:
On the Distance from Calibration in Sequential Prediction. COLT 2024: 4307-4357 - [c17]Yuyang Deng, Mingda Qiao:
Collaborative Learning with Different Labeling Functions. ICML 2024 - [c16]Weihao Kong, Mingda Qiao, Rajat Sen:
A Combinatorial Approach to Robust PCA. ITCS 2024: 70:1-70:22 - [i20]Mingda Qiao, Letian Zheng:
On the Distance from Calibration in Sequential Prediction. CoRR abs/2402.07458 (2024) - [i19]Yuyang Deng, Mingda Qiao:
Collaborative Learning with Different Labeling Functions. CoRR abs/2402.10445 (2024) - [i18]Nika Haghtalab, Mingda Qiao, Kunhe Yang:
Platforms for Efficient and Incentive-Aware Collaboration. CoRR abs/2402.15169 (2024) - [i17]Nika Haghtalab, Mingda Qiao, Kunhe Yang, Eric Zhao:
Truthfulness of Calibration Measures. CoRR abs/2407.13979 (2024) - 2023
- [c15]Mingda Qiao, Gregory Valiant:
Online Pen Testing. ITCS 2023: 91:1-91:26 - [i16]Weihao Kong, Mingda Qiao, Rajat Sen:
A Combinatorial Approach to Robust PCA. CoRR abs/2311.16416 (2023) - 2022
- [j1]Guy Blanc
, Jane Lange
Properly Learning Decision Trees in almost Polynomial Time. J. ACM 69(6): 39:1-39:19 (2022) - [c14]Mingda Qiao, Guru Guruganesh, Ankit Singh Rawat, Kumar Avinava Dubey, Manzil Zaheer:
A Fourier Approach to Mixture Learning. NeurIPS 2022 - [i15]Guy Blanc, Jane Lange, Mingda Qiao, Li-Yang Tan:
Open Problem: Properly learning decision trees in polynomial time? CoRR abs/2206.14431 (2022) - [i14]Mingda Qiao, Gregory Valiant:
Online Pen Testing. CoRR abs/2210.00655 (2022) - [i13]Mingda Qiao, Guru Guruganesh, Ankit Singh Rawat, Avinava Dubey, Manzil Zaheer:
A Fourier Approach to Mixture Learning. CoRR abs/2210.02415 (2022) - 2021
- [c13]Guy Blanc, Jane Lange, Mingda Qiao, Li-Yang Tan:
Decision Tree Heuristics Can Fail, Even in the Smoothed Setting. APPROX-RANDOM 2021: 45:1-45:16 - [c12]Mingda Qiao, Gregory Valiant:
Exponential Weights Algorithms for Selective Learning. COLT 2021: 3833-3858 - [c11]Guy Blanc, Jane Lange, Mingda Qiao
Properly learning decision trees in almost polynomial time. FOCS 2021: 920-929 - [c10]Mingda Qiao
Stronger calibration lower bounds via sidestepping. STOC 2021: 456-466 - [i12]Mingda Qiao, Gregory Valiant:
Exponential Weights Algorithms for Selective Learning. CoRR abs/2106.15662 (2021) - [i11]Guy Blanc, Jane Lange, Mingda Qiao, Li-Yang Tan:
Decision tree heuristics can fail, even in the smoothed setting. CoRR abs/2107.00819 (2021) - [i10]Guy Blanc, Jane Lange, Mingda Qiao, Li-Yang Tan:
Properly learning decision trees in almost polynomial time. CoRR abs/2109.00637 (2021) - 2020
- [c9]Jian Li, Xuanyuan Luo, Mingda Qiao:
On Generalization Error Bounds of Noisy Gradient Methods for Non-Convex Learning. ICLR 2020 - [i9]Mingda Qiao, Gregory Valiant:
Stronger Calibration Lower Bounds via Sidestepping. CoRR abs/2012.03454 (2020)
2010 – 2019
- 2019
- [c8]Gerdus Benadè, Ariel D. Procaccia, Mingda Qiao:
Low-Distortion Social Welfare Functions. AAAI 2019: 1788-1795 - [c7]Mingda Qiao, Gregory Valiant:
A Theory of Selective Prediction. COLT 2019: 2580-2594 - [i8]Jian Li, Xuanyuan Luo, Mingda Qiao:
On Generalization Error Bounds of Noisy Gradient Methods for Non-Convex Learning. CoRR abs/1902.00621 (2019) - [i7]Mingda Qiao, Gregory Valiant:
A Theory of Selective Prediction. CoRR abs/1902.04256 (2019) - 2018
- [c6]Mingda Qiao:
Do Outliers Ruin Collaboration? ICML 2018: 4177-4184 - [c5]Mingda Qiao, Gregory Valiant:
Learning Discrete Distributions from Untrusted Batches. ITCS 2018: 47:1-47:20 - [i6]Mingda Qiao:
Do Outliers Ruin Collaboration? CoRR abs/1805.04720 (2018) - 2017
- [c4]Lijie Chen, Jian Li, Mingda Qiao:
Nearly Instance Optimal Sample Complexity Bounds for Top-k Arm Selection. AISTATS 2017: 101-110 - [c3]Lijie Chen, Anupam Gupta, Jian Li, Mingda Qiao, Ruosong Wang:
Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration. COLT 2017: 482-534 - [c2]Lijie Chen, Jian Li, Mingda Qiao:
Towards Instance Optimal Bounds for Best Arm Identification. COLT 2017: 535-592 - [c1]Avrim Blum, Nika Haghtalab, Ariel D. Procaccia, Mingda Qiao:
Collaborative PAC Learning. NIPS 2017: 2392-2401 - [i5]Lijie Chen, Jian Li, Mingda Qiao:
Nearly Instance Optimal Sample Complexity Bounds for Top-k Arm Selection. CoRR abs/1702.03605 (2017) - [i4]Haotian Jiang, Jian Li, Mingda Qiao:
Practical Algorithms for Best-K Identification in Multi-Armed Bandits. CoRR abs/1705.06894 (2017) - [i3]Lijie Chen, Anupam Gupta, Jian Li, Mingda Qiao, Ruosong Wang:
Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration. CoRR abs/1706.01081 (2017) - [i2]Mingda Qiao, Gregory Valiant:
Learning Discrete Distributions from Untrusted Batches. CoRR abs/1711.08113 (2017) - 2016
- [i1]Lijie Chen, Jian Li, Mingda Qiao:
Towards Instance Optimal Bounds for Best Arm Identification. CoRR abs/1608.06031 (2016)
Coauthor Index
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