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Weihao 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) - [c29]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 - [c28]Reese Pathak, Rajat Sen, Weihao Kong, Abhimanyu Das:
Transformers can optimally learn regression mixture models. ICLR 2024 - [c27]Weihao Kong, Mingda Qiao, Rajat Sen:
A Combinatorial Approach to Robust PCA. ITCS 2024: 70:1-70:22 - [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 2024-08-10 00:31 CEST by the dblp team
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