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ORCIDWeihao Kong
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2020 – today
- 2024
[j3]Jonathan Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill:
Estimating Optimal Policy Value in Linear Contextual Bandits Beyond Gaussianity. Trans. Mach. Learn. Res. 2024 (2024)- [c31]Gavin Brown, Jonathan Hayase, Samuel B. Hopkins, Weihao Kong, Xiyang Liu, Sewoong Oh, Juan C. Perdomo, Adam Smith:
Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares Extended Abstract. COLT 2024: 750-751 - [c30]Reese Pathak, Rajat Sen, Weihao Kong, Abhimanyu Das:
Transformers can optimally learn regression mixture models. ICLR 2024 - [c29]Abhimanyu Das, Weihao Kong, Rajat Sen, Yichen Zhou:
A decoder-only foundation model for time-series forecasting. ICML 2024 - [c28]Weihao Kong, Mingda Qiao, Rajat Sen:
A Combinatorial Approach to Robust PCA. ITCS 2024: 70:1-70:22 - [c27]Weihao Kong, Yifan Hao, Qi Guo, Yongwei Zhao, Xinkai Song, Xiaqing Li, Mo Zou, Zidong Du, Rui Zhang, Chang Liu, Yuanbo Wen, Pengwei Jin, Xing Hu, Wei Li, Zhiwei Xu, Tianshi Chen:
Cambricon-D: Full-Network Differential Acceleration for Diffusion Models. ISCA 2024: 903-914 - [i29]Gavin Brown, Jonathan Hayase, Samuel B. Hopkins, Weihao Kong, Xiyang Liu, Sewoong Oh, Juan C. Perdomo, Adam Smith:
Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares. CoRR abs/2404.15409 (2024) - 2023
- [j2]Abhimanyu Das, Weihao Kong, Andrew Leach, Shaan Mathur, Rajat Sen, Rose Yu:
Long-term Forecasting with TiDE: Time-series Dense Encoder. Trans. Mach. Learn. Res. 2023 (2023) - [c26]Abhimanyu Das, Ayush Jain, Weihao Kong, Rajat Sen:
Efficient List-Decodable Regression using Batches. ICML 2023: 7025-7065 - [c25]Xiyang Liu, Prateek Jain, Weihao Kong, Sewoong Oh, Arun Sai Suggala:
Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency. NeurIPS 2023 - [c24]Yiming Sun, Chenyang Li, Weihao Kong:
Auxiliary Information Enhanced Span-Based Model for Nested Named Entity Recognition. NLPCC (1) 2023: 209-221 - [c23]Ashok Cutkosky, Abhimanyu Das, Weihao Kong, Chansoo Lee, Rajat Sen:
Blackbox optimization of unimodal functions. UAI 2023: 476-484 - [c22]Abhimanyu Das, Weihao Kong, Biswajit Paria, Rajat Sen:
Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting. UAI 2023: 518-528 - [i28]Xiyang Liu, Prateek Jain, Weihao Kong, Sewoong Oh, Arun Sai Suggala:
Near Optimal Private and Robust Linear Regression. CoRR abs/2301.13273 (2023) - [i27]Jonathan N. Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill:
Estimating Optimal Policy Value in General Linear Contextual Bandits. CoRR abs/2302.09451 (2023) - [i26]Abhimanyu Das, Weihao Kong, Andrew Leach, Shaan Mathur, Rajat Sen, Rose Yu:
Long-term Forecasting with TiDE: Time-series Dense Encoder. CoRR abs/2304.08424 (2023) - [i25]Ayush Jain, Rajat Sen, Weihao Kong, Abhimanyu Das, Alon Orlitsky:
Linear Regression using Heterogeneous Data Batches. CoRR abs/2309.01973 (2023) - [i24]Abhimanyu Das, Weihao Kong, Rajat Sen, Yichen Zhou:
A decoder-only foundation model for time-series forecasting. CoRR abs/2310.10688 (2023) - [i23]Reese Pathak, Rajat Sen, Weihao Kong, Abhimanyu Das:
Transformers can optimally learn regression mixture models. CoRR abs/2311.08362 (2023) - [i22]Weihao Kong, Mingda Qiao, Rajat Sen:
A Combinatorial Approach to Robust PCA. CoRR abs/2311.16416 (2023) - 2022
- [c21]Xiyang Liu, Weihao Kong, Sewoong Oh:
Differential privacy and robust statistics in high dimensions. COLT 2022: 1167-1246 - [c20]Pranjal Awasthi, Abhimanyu Das, Weihao Kong, Rajat Sen:
Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model. NeurIPS 2022 - [c19]Xiyang Liu, Weihao Kong, Prateek Jain, Sewoong Oh:
DP-PCA: Statistically Optimal and Differentially Private PCA. NeurIPS 2022 - [i21]Abhimanyu Das, Weihao Kong, Biswajit Paria, Rajat Sen:
A Top-Down Approach to Hierarchically Coherent Probabilistic Forecasting. CoRR abs/2204.10414 (2022) - [i20]Xiyang Liu, Weihao Kong, Prateek Jain, Sewoong Oh:
DP-PCA: Statistically Optimal and Differentially Private PCA. CoRR abs/2205.13709 (2022) - [i19]Weihao Kong, Rajat Sen, Pranjal Awasthi, Abhimanyu Das:
Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models. CoRR abs/2206.04777 (2022) - [i18]Abhimanyu Das, Ayush Jain, Weihao Kong, Rajat Sen:
Efficient List-Decodable Regression using Batches. CoRR abs/2211.12743 (2022) - 2021
- [j1]Jie Wu
, Lizhong Bie, Weihao Kong, Pengfei Gao, Yanfeng Wang:
Multi-Frequency Multi-Amplitude Superposition Modulation Method With Phase Shift Optimization for Single Inverter of Wireless Power Transfer System. IEEE Trans. Circuits Syst. I Regul. Pap. 68(5): 2271-2279 (2021) - [c18]Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill:
Online Model Selection for Reinforcement Learning with Function Approximation. AISTATS 2021: 3340-3348 - [c17]Jonathan Hayase, Weihao Kong, Raghav Somani, Sewoong Oh:
Defense against backdoor attacks via robust covariance estimation. ICML 2021: 4129-4139 - [c16]Xiangyu Meng, Weihao Kong, Haifeng Yang, Yecong Li, Xuan Li:
A 1.8-GS/s 6-Bit Two-Step SAR ADC in 65-nm CMOS. ISCAS 2021: 1-4 - [c15]Xiyang Liu, Weihao Kong, Sham M. Kakade, Sewoong Oh:
Robust and differentially private mean estimation. NeurIPS 2021: 3887-3901 - [i17]Xiyang Liu, Weihao Kong, Sham M. Kakade, Sewoong Oh:
Robust and Differentially Private Mean Estimation. CoRR abs/2102.09159 (2021) - [i16]Jonathan Hayase, Weihao Kong, Raghav Somani, Sewoong Oh:
SPECTRE: Defending Against Backdoor Attacks Using Robust Statistics. CoRR abs/2104.11315 (2021) - [i15]Zhen Miao, Weihao Kong, Ramya Korlakai Vinayak, Wei Sun, Fang Han:
Fisher-Pitman permutation tests based on nonparametric Poisson mixtures with application to single cell genomics. CoRR abs/2106.03022 (2021) - [i14]Xiyang Liu, Weihao Kong, Sewoong Oh:
Differential privacy and robust statistics in high dimensions. CoRR abs/2111.06578 (2021) - 2020
- [c14]Weihao Kong, Emma Brunskill, Gregory Valiant:
Sublinear Optimal Policy Value Estimation in Contextual Bandits. AISTATS 2020: 4377-4387 - [c13]Weihao Kong, Raghav Somani, Zhao Song, Sham M. Kakade, Sewoong Oh:
Meta-learning for Mixed Linear Regression. ICML 2020: 5394-5404 - [c12]Weihao Kong, Raghav Somani, Sham M. Kakade, Sewoong Oh:
Robust Meta-learning for Mixed Linear Regression with Small Batches. NeurIPS 2020 - [i13]Weihao Kong, Raghav Somani, Zhao Song, Sham M. Kakade, Sewoong Oh:
Meta-learning for mixed linear regression. CoRR abs/2002.08936 (2020) - [i12]Weihao Kong, Raghav Somani, Sham M. Kakade, Sewoong Oh:
Robust Meta-learning for Mixed Linear Regression with Small Batches. CoRR abs/2006.09702 (2020) - [i11]Jonathan N. Lee, Aldo Pacchiano, Vidya Muthukumar, Weihao Kong, Emma Brunskill:
Online Model Selection for Reinforcement Learning with Function Approximation. CoRR abs/2011.09750 (2020)
2010 – 2019
- 2019
- [b1]Weihao Kong:
The surprising power of little data. Stanford University, USA, 2019 - [c11]Ramya Korlakai Vinayak, Weihao Kong, Gregory Valiant, Sham M. Kakade:
Maximum Likelihood Estimation for Learning Populations of Parameters. ICML 2019: 6448-6457 - [c10]Ilias Diakonikolas, Weihao Kong, Alistair Stewart:
Efficient Algorithms and Lower Bounds for Robust Linear Regression. SODA 2019: 2745-2754 - [i10]Ramya Korlakai Vinayak, Weihao Kong, Gregory Valiant, Sham M. Kakade:
Maximum Likelihood Estimation for Learning Populations of Parameters. CoRR abs/1902.04553 (2019) - [i9]Ramya Korlakai Vinayak, Weihao Kong, Sham M. Kakade:
Optimal Estimation of Change in a Population of Parameters. CoRR abs/1911.12568 (2019) - [i8]Weihao Kong, Gregory Valiant, Emma Brunskill:
Sublinear Optimal Policy Value Estimation in Contextual Bandits. CoRR abs/1912.06111 (2019) - 2018
- [c9]Qingqing Huang, Sham M. Kakade, Weihao Kong, Gregory Valiant:
Recovering Structured Probability Matrices. ITCS 2018: 46:1-46:14 - [c8]David Cohen-Steiner, Weihao Kong, Christian Sohler, Gregory Valiant:
Approximating the Spectrum of a Graph. KDD 2018: 1263-1271 - [c7]Weihao Kong, Gregory Valiant:
Estimating Learnability in the Sublinear Data Regime. NeurIPS 2018: 5460-5469 - [i7]Weihao Kong, Gregory Valiant:
Estimating Learnability in the Sublinear Data Regime. CoRR abs/1805.01626 (2018) - [i6]Ilias Diakonikolas, Weihao Kong, Alistair Stewart:
Efficient Algorithms and Lower Bounds for Robust Linear Regression. CoRR abs/1806.00040 (2018) - 2017
- [c6]Kevin Tian, Weihao Kong, Gregory Valiant:
Learning Populations of Parameters. NIPS 2017: 5778-5787 - [i5]Kevin Tian, Weihao Kong, Gregory Valiant:
Optimally Learning Populations of Parameters. CoRR abs/1709.02707 (2017) - [i4]David Cohen-Steiner, Weihao Kong, Christian Sohler, Gregory Valiant:
Approximating the Spectrum of a Graph. CoRR abs/1712.01725 (2017) - 2016
- [i3]Weihao Kong, Gregory Valiant:
Spectrum Estimation from Samples. CoRR abs/1602.00061 (2016) - [i2]Qingqing Huang, Sham M. Kakade, Weihao Kong, Gregory Valiant:
Recovering Structured Probability Matrices. CoRR abs/1602.06586 (2016) - 2014
- [c5]Hongwei Jia, Shuai Su, Weihao Kong, Haiyong Luo, Guoqiang Shang:
MobiIO: Push the limit of indoor/outdoor detection through human's mobility traces. IPIN 2014: 197-202 - 2013
- [c4]Weihao Kong, Jian Li, Tie-Yan Liu, Tao Qin:
Optimal Allocation for Chunked-Reward Advertising. WINE 2013: 291-304 - [i1]Weihao Kong, Jian Li, Tao Qin, Tie-Yan Liu:
Revenue Optimization for Group-Buying Websites. CoRR abs/1305.5946 (2013) - 2012
- [c3]Weihao Kong, Wu-Jun Li:
Double-Bit Quantization for Hashing. AAAI 2012: 634-640 - [c2]Weihao Kong, Wu-Jun Li:
Isotropic Hashing. NIPS 2012: 1655-1663 - [c1]Weihao Kong, Wu-Jun Li, Minyi Guo:
Manhattan hashing for large-scale image retrieval. SIGIR 2012: 45-54
Coauthor Index
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last updated on 2024-09-04 00:30 CEST by the dblp team
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