Yining Wang's homepage: publications

(*alphabetic author order, **equal contribution)

The headings refer to the completion dates; the publication dates are given at the end of the citations.

2024

Stochastic Zeroth-Order Optimization under Strongly Convexity and Lipschitz Hessian: Minimax Sample Complexity.
By Qian Yu, Yining Wang, Baihe Huang, Qi Lei and Jason D. Lee.
In NeurIPS 2024, Vancouver, Canada.
A Re-solving Heuristic for Dynamic Assortment Optimization with Knapsack Constraints.
By Xi Chen*, Mo Liu*, Yining Wang* and Yuan Zhou*.
On the Optimal Regret of Locally Private Linear Contextual Bandit.
By Jiachun Li*, David Simchi-Levi* and Yining Wang*.
Demand Balancing in Primal-Dual Optimization for Blind Network Revenue Management.
By Sentao Miao* and Yining Wang*.

2023

Utility Fairness in Contextual Dynamic Pricing with Demand Learning.
By Xi Chen*, David Simchi-Levi* and Yining Wang*.
Management Science, accepted for publication.
Dynamic Learning Policy for Multi-Warehouse Multi-Store Systems with Censored Demands.
By Sentao Miao*, Yining Wang* and Renbo Zhao*.
Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms.
By Qian Yu, Yining Wang, Baihe Huang, Qi Lei, Jason D. Lee.
In NeurIPS 2023, New Orleans, USA.
Estimation of High-Dimensional Contextual Pricing Models with Nonparametric Price Confounders.
By Yining Wang and Quanquan Liu.
Capacity and Pricing Management with Demand Learning.
By Jian Chen*, Zechao Li*, Anyan Qi* and Yining Wang*.
Optimal Sample Complexity Bounds for Non-convex Optimization under Kurdyka-Lojasiewicz Condition.
By Qian Yu, Yining Wang, Baihe Huang, Qi Lei and Jason D. Lee.
In AISTATS 2023, Valencia, Spain.

2022

On Adaptivity in Non-stationary Stochastic Optimization With Bandit Feedback.
By Yining Wang. [journal link]
Operations Research (tech. note), accepted for publication.
Network Revenue Management with Demand Learning and Fair Resource Consumption Balancing.
By Xi Chen*, Jiameng Lyu*, Yining Wang* and Yuan Zhou*.
Production and Operations Management, accepted for publication.
Pricing and Positioning of Horizontally Differentiated Products with Incomplete Demand Information.
By Arnoud V. den Boer*, Boxiao Chen* and Yining Wang*.
Operations Research, accepted for publication.
Nearly Dimension-Independent Sparse Linear Bandit over Small Action Spaces via Best Subset Selection.
By Yi Chen**, Yining Wang**, Ethan X. Fang, Zhaoran Wang and Runze Li. [journal link]
Journal of the American Statistical Association, 119(545):246-258, 2022.

2021

Network Revenue Management with Nonparametric Demand Learning: sqrt{T}-regret and Polynomial Dimension Dependency.
By Sentao Miao* and Yining Wang*. [slides]
- Presented at the Supply Chain Management SIG Meeting, MSOM Conference 2022, Munich Germany.
Active Learning for Contextual Search with Binary Feedbacks.
By Xi Chen*, Quanquan Liu* and Yining Wang*. [journal link]
Management Science, 69(4):2165-2181, 2023.
Dynamic Pricing with Fairness Constraints.
By Maxime Cohen*, Sentao Miao* and Yining Wang*.
Differential Privacy in Personalized Pricing with Nonparametric Demand Models.
By Xi Chen*, Sentao Miao* and Yining Wang*. [journal link] [slides]
Operations Research, 71(2):581-602, 2023.
A General Framework for Resource Constrained Revenue Management with Demand Learning and Large Action Space.
By Sentao Miao*, Yining Wang* and Jiawei Zhang*.
Adversarial Combinatorial Bandits with General Non-linear Reward Functions.
By Xi Chen*, Yanjun Han* and Yining Wang*.
In ICML 2021, virtual conference.

2020

Optimal Policies for Dynamic Pricing and Inventory Control with Nonparametric Censored Demands.
By Beryl Boxiao Chen*, Yining Wang* and Yuan Zhou*. [slides] [journal link]
Management Science, accepted for publication.
Smooth Bandit Optimization: Generalization to Holder Space.
By Yusha Liu, Yining Wang and Aarti Singh.
In AISTATS 2021, virtual conference.
Privacy-Preserving Dynamic Personalized Pricing with Demand Learning.
By Xi Chen*, David Simchi-Levi* and Yining Wang*. [journal link] [slides]
Management Science, 68(7):4878-4898, 2022.
Constant Regret Re-solving Heuristics for Price-based Revenue Management.
By Yining Wang and He Wang. [journal link]
Operations Research, 70(6):3538-3557, 2022.
Robust Dynamic Pricing with Demand Learning in the Presence of Outlier Customers.
By Xi Chen* and Yining Wang*. [journal link]
Operations Research, 71(4):1362-1396, 2023.
Dynamic Pricing and Inventory Control with Fixed Ordering Cost and Incomplete Demand Information.
By Beryl Boxiao Chen*, David Simchi-Levi*, Yining Wang* and Yuan Zhou*. [journal link] [slides]
Management Science, 68(8):5684-5703, 2022.
Uncertainty Quantification for Demand Prediction in Contextual Dynamic Pricing.
By Yining Wang, Xi Chen, Xiangyu Chang and Dongdong Ge. [journal link]
Production and Operations Management, 30(6):1703-1717, 2021.
Data-Based Dynamic Pricing and Inventory Control with Censored Demand and Limited Price Changes.
By Beryl Boxiao Chen, Xiuli Chao and Yining Wang. [journal link]
Operations Research (tech. note), 68(5):1445-1456, 2020.

2019

Multi-modal Dynamic Pricing.
By Yining Wang, Beryl Boxiao Chen and David Simchi-Levi. [journal link]
Management Science, 67(10):6136-6152, 2021.
Optimism in Reinforcement Learning with Generalized Linear Function Approximation.
By Yining Wang, Ruosong Wang, Simon S. Du and Akshay Krishnamurthy.
In ICLR 2021, virtual conference.
Robust Dynamic Assortment Optimization in the Presence of Outlier Customers.
By Xi Chen*, Akshay Krishnamurthy* and Yining Wang*. [slides] [journal link]
Operations Research, accepted for publication.
Sqrt{n}-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank.
By Kefan Dong*, Jian Peng*, Yining Wang* and Yuan Zhou*.
- A 4-page extended abstract to appear in COLT 2020, Graz, Austria.
Tight Regret Bounds for Infinite-armed Linear Contextual Bandits.
By Yingkai Li*, Yining Wang* and Yuan Zhou*.
In AISTATS 2021, virtual conference.
Selective Data Acquisition in Learning and Decision Making Problems.
By Yining Wang.
PhD thesis, Machine Learning Department, Carnegie Mellon University.
Nearly Minimax-Optimal Regret for Linearly Parameterized Bandits.
By Yingkai Li*, Yining Wang* and Yuan Zhou*.
IEEE Transactions on Information Theory, accepted for publication.
- A 2-page extended abstract to appear in COLT 2019, Phoenix, USA.
On Asymptotically Tight Tail Bounds for Sums of Geometric and Exponential Random Variables.
By Yaonan Jin*, Yingkai Li*, Yining Wang* and Yuan Zhou*.

2018

Dynamic Assortment Optimization with Changing Contextual Information.
By Xi Chen*, Yining Wang* and Yuan Zhou*. [journal link]
Journal of Machine Learning Research, 21(216):1-44, 2020.
- Finalist in 2019 INFORMS JFIG Best Paper Competition.
Dynamic Assortment Selection under the Nested Logit Models.
By Xi Chen*, Chao Shi*, Yining Wang* and Yuan Zhou*. [journal link]
Production and Operations Management, 30(1):85-102, 2021.
Robust Nonparametric Regression under Huber's epsilon-contamination Model.
By Simon Du, Yining Wang, Sivaraman Balakrishnan, Pradeep Ravikumar and Aarti Singh.
How Many Samples are Needed to Estimate a Convolutional or Recurrent Neural Network?
By Simon Du**, Yining Wang**, Xiyu Zhai, Sivaraman Balakrishnan, Ruslan Salakhutdinov and Aarti Singh.
- Preliminary version with results on CNN only appeared in NeurIPS 2018, Montreal, Canada.
Optimal Policy for Dynamic Assortment Planning Under Multinomial Logit Models.
By Xi Chen*, Yining Wang* and Yuan Zhou*. [journal link]
Mathematics of Operations Research, 46(4):1639-1657, 2021.
- Preliminary version with weaker regret bounds in NeurIPS 2018, Montreal, Canada.
Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates.
By Yining Wang, Sivaraman Balakrishnan and Aarti Singh. [slides] [journal link]
IEEE Transactions on Information Theory, 65(11):7350-7366, 2019.
- Preliminary version in NeurIPS 2018, Montreal, Canada.
Near-Linear Time Local Polynomial Nonparametric Estimation with Box Kernels.
By Yining Wang, Yi Wu and Simon Du. [journal link]
INFORMS Journal on Computing, accepted for publication.

2017

Convergence Rates of Latent Topic Models Under Relaxed Identifiability Conditions.
By Yining Wang. [journal link]
Electronic Journal of Statistics, 13(1):37-66, 2019.
Stochastic Zeroth-order Optimization in High Dimensions.
By Yining Wang, Simon Du, Sivaraman Balakrishnan and Aarti Singh. [slides]
In AISTATS 2018 (oral), Playa Blanca, Spain.
A Note on a Tight Lower Bound for MNL-Bandit Assortment Selection Models.
By Xi Chen* and Yining Wang*. [journal link]
Operations Research Letters, 46(5):534-537, 2018.
Non-stationary Stochastic Optimization with Local Spatial and Temporal Changes.
By Xi Chen*, Yining Wang* and Yu-Xiang Wang*. [slides] [journal link]
Operations Research (tech. note), 67(6):1752-1765, 2020.
Near-Optimal Discrete Optimization for Experimental Design: A Regret Minimization Approach.
By Zeyuan Allen-Zhu*, Yuanzhi Li*, Aarti Singh* and Yining Wang*. [code] [slides] [journal link]
Mathematical Programming (Series A), 186:439-478, 2021.
- An easier-to-read version, with proof details hidden and more examples.
- Preliminary version in ICML 2017, Sydney, Australia.
- A two-page abstract presented at the DSML Workshop in NIPS 2017, Long Beach, USA.
- Application to a structural design problem, presented at ASME IDETC-DAC 2018, Quebec City, Canada.
Sequence Modeling via Segmentations.
By Chong Wang, Yining Wang, Po-Sen Huang, Abdelrahman Mohamed, Dengyong Zhou and Li Deng.
In ICML 2017, Sydney, Australia.
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems.
By Simon Du, Yining Wang and Aarti Singh.
In NIPS 2017, Long Beach, USA.
Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates.
By Yining Wang, Jialei Wang, Sivaraman Balakrishnan and Aarti Singh. [journal link]
Journal of Multivariate Analysis, 174:104526, 2019.

2016

Data Poisoning Attacks on Factorization-Based Collaborative Filtering.
By Bo Li**, Yining Wang**, Aarti Singh and Yevgeniy Vorobeychik.
In NIPS 2016, Barcelona, Spain.
Online and Differentially Private Tensor Decomposition.
By Yining Wang and Anima Anandkumar.
In NIPS 2016, Barcelona, Spain.
An Improved Gap-Dependency Analysis of the Noisy Power Method.
By Maria-Florina Balcan*, Simon Du*, Yining Wang* and Adams Wei Yu*.
In COLT 2016, New York, USA.
On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models.
By Yining Wang, Adams Wei Yu and Aarti Singh. [code] [slides] [journal link]
Journal of Machine Learning Research, 18(143):1-41, 2017.
- A short abstract appeared at the DEML Workshop in ICML 2016, New York, USA.
- An extension to linear quantization appeared in ICASSP 2018, Calgary, Canada. [slides]

2015

Fast and Guaranteed Tensor Decomposition via Sketching.
By Yining Wang, Hsiao-Yu Fish Tung, Alex Smola and Anima Anandkumar. [code]
In NIPS 2015 (spotlight), Montreal, Canada.
- The theoretical analysis in this paper is sub-optimal. Please refer to our new paper for a cleaner and improved analysis of orthogonal tensor decomposition via noisy power method.
Differentially Private Subspace Clustering.
By Yining Wang, Yu-Xiang Wang and Aarti Singh.
In NIPS 2015, Montreal, Canada.
Graph Connectivity in Noisy Sparse Subspace Clustering.
By Yining Wang, Yu-Xiang Wang and Aarti Singh.
In AISTATS 2016, Cadiz, Spain.
A Theoretical Analysis of Noisy Sparse Subspace Clustering on Dimensionality-Reduced Data.
By Yining Wang, Yu-Xiang Wang and Aarti Singh. [journal link]
IEEE Transactions on Information Theory, 65(2):685-706, 2019.
- Preliminary version in ICML 2015, Lille, France.
DP-space: Bayesian Nonparametric Subspace Clustering with Small-variance Asymptotics.
By Yining Wang and Jun Zhu. [code]
In ICML 2015, Lille, France.
Provably Correct Active Sampling Algorithms for Matrix Column Subset Selection with Missing Data.
By Yining Wang and Aarti Singh. [journal link]
Journal of Machine Learning Research, 18(156):1-42, 2018.
- Preliminary version in AISTATS 2015, San Diego, USA.
- A follow-up note with empirical comparisons in Allerton 2015, Monticello, USA. [slides]

2014

Noise-adaptive Margin-based Active Learning and Lower Bounds under Tsybakov Noise Condition.
By Yining Wang and Aarti Singh.
In AAAI 2016 (oral), Phoenix, USA. [slides]
Direct Learning to Rank And Rerank.
By Cynthia Rudin and Yining Wang.
In AISTATS 2018, Playa Blanca, Spain.
- Finalist in 2017 INFORMS QSR Section Best Paper Competition.
Spectral Learning for Supervised Topic Models.
By Yong Ren**, Yining Wang** and Jun Zhu. [code]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(3):726-739, 2018.
- Preliminary version in NIPS 2014, Montreal, Canada.
Small-variance Asymptotics for Dirichlet Process Mixtures of SVMs.
By Yining Wang and Jun Zhu. [slides] [code]
In AAAI 2014 (oral), Quebec City, Canada.
Harvesting Motion Patterns in Still Images from the Internet.
By Jiajun Wu, Yining Wang, Zhulin Li and Zhuowen Tu.
In CogSci 2014, Quebec City, Canada.

2013 and before

A Theoretical Analysis of NDCG Type Ranking Measures.
By Yining Wang, Liwei Wang, Yuanzhi Li, Di He, Wei Chen and Tie-Yan Liu.
In COLT 2013, Princeton, USA.
Building Large Collections of Chinese and English Medical Terms from Semi-Structured and Encyclopedia Websites.
By Yan Xu, Yining Wang, Jian-Tao Sun, Jianwen Zhang, Junichi Tsujii and Eric Chang.
PLoS One, 8(7):e67526, 2013.
Joint segmentation and named entity recognition using dual decomposition in Chinese discharge summaries.
By Yan Xu, Yining Wang, Tianren Liu, Jiahua Liu, Yubo Fan, Yi Qian, Junichi Tsujii and Eric Chang.
Journal of the American Medical Informatics Association, 21:e84-e92, 2014.
An end-to-end system to identify temporal relation in discharge summaries: 2012 i2b2 challenge.
By Yan Xu, Yining Wang, Tianren Liu, Junichi Tsujii and Eric Chang.
Journal of the American Medical Informatics Association, 20:849-858, 2013.
FMTCP: A Fountain Code-Based Multipath Transmission Control Protocol.
By Yong Cui, Lian Wang, Xin Wang, Hongyi Wang and Yining Wang.
IEEE/ACM Transactions on Networking, 23(2):465-478, 2015.