Double-Bit Quantization for Hashing. - Weihao Kong; Wu-Jun Li (AAAI, 2012)

Isotropic Hashing. - Weihao Kong; Wu-Jun Li (NIPS, 2012)

Manhattan hashing for large-scale image retrieval. - Weihao Kong; Wu-Jun Li; Minyi Guo (SIGIR, 2012)

Optimal Allocation for Chunked-Reward Advertising. - Weihao Kong; Jian Li 015; Tie-Yan Liu; Tao Qin 001 (WINE, 2013)

Revenue Optimization for Group-Buying Websites - Weihao Kong; Jian Li 015; Tao Qin 001; Tie-Yan Liu (CoRR, 2013)

MobiIO: Push the limit of indoor/outdoor detection through human's mobility traces. - Hongwei Jia; Shuai Su; Weihao Kong; Haiyong Luo; Guoqiang Shang (IPIN, 2014)

Spectrum Estimation from Samples. - Weihao Kong; Gregory Valiant (CoRR, 2016)

Learning Populations of Parameters. - Kevin Tian; Weihao Kong; Gregory Valiant (NIPS, 2017)

Optimally Learning Populations of Parameters. - Kevin Tian; Weihao Kong; Gregory Valiant (CoRR, 2017)

Recovering Structured Probability Matrices. - Qingqing Huang 001; Sham M. Kakade; Weihao Kong; Gregory Valiant (ITCS, 2018)

Approximating the Spectrum of a Graph. - David Cohen-Steiner; Weihao Kong; Christian Sohler; Gregory Valiant (KDD, 2018)

Estimating Learnability in the Sublinear Data Regime. - Weihao Kong; Gregory Valiant (NeurIPS, 2018)

Maximum Likelihood Estimation for Learning Populations of Parameters. - Ramya Korlakai Vinayak; Weihao Kong; Gregory Valiant; Sham M. Kakade (ICML, 2019)

Efficient Algorithms and Lower Bounds for Robust Linear Regression. - Ilias Diakonikolas; Weihao Kong; Alistair Stewart (SODA, 2019)

Optimal Estimation of Change in a Population of Parameters. - Ramya Korlakai Vinayak; Weihao Kong; Sham M. Kakade (CoRR, 2019)

The surprising power of little data. - Weihao Kong (2019)

Sublinear Optimal Policy Value Estimation in Contextual Bandits. - Weihao Kong; Emma Brunskill; Gregory Valiant (AISTATS, 2020)

Meta-learning for Mixed Linear Regression. - Weihao Kong; Raghav Somani; Zhao Song 002; Sham M. Kakade; Sewoong Oh (ICML, 2020)

Robust Meta-learning for Mixed Linear Regression with Small Batches. - Weihao Kong; Raghav Somani; Sham M. Kakade; Sewoong Oh (NeurIPS, 2020)

Online Model Selection for Reinforcement Learning with Function Approximation. - Jonathan N. Lee; Aldo Pacchiano; Vidya Muthukumar; Weihao Kong; Emma Brunskill (AISTATS, 2021)

Defense against backdoor attacks via robust covariance estimation. - Jonathan Hayase; Weihao Kong; Raghav Somani; Sewoong Oh (ICML, 2021)

A 1.8-GS/s 6-Bit Two-Step SAR ADC in 65-nm CMOS. - Xiangyu Meng 003; Weihao Kong; Haifeng Yang; Yecong Li; Xuan Li (ISCAS, 2021)

Robust and differentially private mean estimation. - Xiyang Liu; Weihao Kong; Sham M. Kakade; Sewoong Oh (NeurIPS, 2021)

SPECTRE: Defending Against Backdoor Attacks Using Robust Statistics. - Jonathan Hayase; Weihao Kong; Raghav Somani; Sewoong Oh (CoRR, 2021)

Fisher-Pitman permutation tests based on nonparametric Poisson mixtures with application to single cell genomics. - Zhen Miao; Weihao Kong; Ramya Korlakai Vinayak; Wei Sun; Fang Han (CoRR, 2021)

Multi-Frequency Multi-Amplitude Superposition Modulation Method With Phase Shift Optimization for Single Inverter of Wireless Power Transfer System. - Jie Wu 007; Lizhong Bie; Weihao Kong; Pengfei Gao; Yanfeng Wang (IEEE Trans. Circuits Syst. I Regul. Pap., 2021)

Differential privacy and robust statistics in high dimensions. - Xiyang Liu; Weihao Kong; Sewoong Oh (COLT, 2022)

Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model. - Pranjal Awasthi; Abhimanyu Das; Weihao Kong; Rajat Sen (NeurIPS, 2022)

DP-PCA: Statistically Optimal and Differentially Private PCA. - Xiyang Liu; Weihao Kong; Prateek Jain 002; Sewoong Oh (NeurIPS, 2022)

A Top-Down Approach to Hierarchically Coherent Probabilistic Forecasting. - Abhimanyu Das; Weihao Kong; Biswajit Paria; Rajat Sen (CoRR, 2022)

Trimmed Maximum Likelihood Estimation for Robust Learning in Generalized Linear Models. - Weihao Kong; Rajat Sen; Pranjal Awasthi; Abhimanyu Das (CoRR, 2022)

Efficient List-Decodable Regression using Batches. - Abhimanyu Das; Ayush Jain 001; Weihao Kong; Rajat Sen (ICML, 2023)

Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency. - Xiyang Liu; Prateek Jain 002; Weihao Kong; Sewoong Oh; Arun Sai Suggala (NeurIPS, 2023)

Auxiliary Information Enhanced Span-Based Model for Nested Named Entity Recognition. - Yiming Sun; Chenyang Li; Weihao Kong (NLPCC (1), 2023)

Blackbox optimization of unimodal functions. - Ashok Cutkosky; Abhimanyu Das; Weihao Kong; Chansoo Lee; Rajat Sen (UAI, 2023)

Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting. - Abhimanyu Das; Weihao Kong; Biswajit Paria; Rajat Sen (UAI, 2023)

Near Optimal Private and Robust Linear Regression. - Xiyang Liu; Prateek Jain 002; Weihao Kong; Sewoong Oh; Arun Sai Suggala (CoRR, 2023)

Estimating Optimal Policy Value in General Linear Contextual Bandits. - Jonathan N. Lee; Weihao Kong; Aldo Pacchiano; Vidya Muthukumar; Emma Brunskill (CoRR, 2023)

Long-term Forecasting with TiDE: Time-series Dense Encoder. - Abhimanyu Das; Weihao Kong; Andrew Leach; Shaan Mathur; Rajat Sen; Rose Yu (Trans. Mach. Learn. Res., 2023)

Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares Extended Abstract. - Gavin Brown 003; Jonathan Hayase; Samuel B. Hopkins 001; Weihao Kong; Xiyang Liu; Sewoong Oh; Juan C. Perdomo; Adam Smith 006 (COLT, 2024)

Transformers can optimally learn regression mixture models. - Reese Pathak; Rajat Sen; Weihao Kong; Abhimanyu Das (ICLR, 2024)

A decoder-only foundation model for time-series forecasting. - Abhimanyu Das; Weihao Kong; Rajat Sen; Yichen Zhou (ICML, 2024)

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

Cambricon-D: Full-Network Differential Acceleration for Diffusion Models. - Weihao Kong; Yifan Hao 001; Qi Guo 001; Yongwei Zhao; Xinkai Song; Xiaqing Li; Mo Zou; Zidong Du; Rui Zhang 040; Chang Liu 021; Yuanbo Wen; Pengwei Jin; et al. (ISCA, 2024)

Linear Regression using Heterogeneous Data Batches. - Ayush Jain 001; Rajat Sen; Weihao Kong; Abhimanyu Das; Alon Orlitsky (NeurIPS, 2024)

Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares. - Gavin Brown 003; Jonathan Hayase; Samuel B. Hopkins 001; Weihao Kong; Xiyang Liu; Sewoong Oh; Juan C. Perdomo; Adam Smith 006 (CoRR, 2024)

Estimating Optimal Policy Value in Linear Contextual Bandits Beyond Gaussianity. - Jonathan Lee 002; Weihao Kong; Aldo Pacchiano; Vidya Muthukumar; Emma Brunskill (Trans. Mach. Learn. Res., 2024)