Yining Wang

IEEE Trans. Inf. Theory, January, 2024

2023

Robust Dynamic Pricing with Demand Learning in the Presence of Outlier Customers.

[BibT_eX]

[DOI]

Oper. Res., July, 2023

Active Learning for Contextual Search with Binary Feedback.

[BibT_eX]

[DOI]

Quanquan Liu

Manag. Sci., April, 2023

Differential Privacy in Personalized Pricing with Nonparametric Demand Models.

[BibT_eX]

[DOI]

Sentao Miao

Oper. Res., March, 2023

Utility Fairness in Contextual Dynamic Pricing with Demand Learning.

[BibT_eX]

[DOI]

David Simchi-Levi

CoRR, 2023

2022

Constant Regret Resolving Heuristics for Price-Based Revenue Management.

[BibT_eX]

[DOI]

He Wang

Oper. Res., November, 2022

Dynamic Pricing and Inventory Control with Fixed Ordering Cost and Incomplete Demand Information.

[BibT_eX]

[DOI]

Manag. Sci., 2022

Privacy-Preserving Dynamic Personalized Pricing with Demand Learning.

[BibT_eX]

[DOI]

David Simchi-Levi

Manag. Sci., 2022

Fairness-aware Network Revenue Management with Demand Learning.

[BibT_eX]

[DOI]

CoRR, 2022

2021

Near-optimal discrete optimization for experimental design: a regret minimization approach.

[BibT_eX]

[DOI]

Math. Program., 2021

Optimal Policy for Dynamic Assortment Planning Under Multinomial Logit Models.

[BibT_eX]

[DOI]

Math. Oper. Res., 2021

Multimodal Dynamic Pricing.

[BibT_eX]

[DOI]

Boxiao Chen

David Simchi-Levi

Manag. Sci., 2021

Near-Linear Time Local Polynomial Nonparametric Estimation with Box Kernels.

[BibT_eX]

[DOI]

Yi Wu

INFORMS J. Comput., 2021

Optimism in Reinforcement Learning with Generalized Linear Function Approximation.

[BibT_eX]

[DOI]

Proceedings of the 9th International Conference on Learning Representations, 2021

Smooth Bandit Optimization: Generalization to Holder Space.

[BibT_eX]

[DOI]

Yusha Liu

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Dynamic Assortment Optimization with Changing Contextual Information.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2020

Technical Note - Data-Based Dynamic Pricing and Inventory Control with Censored Demand and Limited Price Changes.

[BibT_eX]

[DOI]

Boxiao Chen

Xiuli Chao

Oper. Res., 2020

Uncertainty Quantification for Demand Prediction in Contextual Dynamic Pricing.

[BibT_eX]

[DOI]

CoRR, 2020

2019

Selective Data Acquisition in Learning and Decision Making Problems.

[BibT_eX]

[DOI]

PhD thesis, 2019

A Theoretical Analysis of Noisy Sparse Subspace Clustering on Dimensionality-Reduced Data.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Theory, 2019

Optimization of Smooth Functions With Noisy Observations: Local Minimax Rates.

[BibT_eX]

[DOI]

IEEE Trans. Inf. Theory, 2019

Rate optimal estimation and confidence intervals for high-dimensional regression with missing covariates.

[BibT_eX]

[DOI]

Jialei Wang

J. Multivar. Anal., 2019

Technical Note - Nonstationary Stochastic Optimization Under Lp, q-Variation Measures.

[BibT_eX]

[DOI]

Oper. Res., 2019

Robust Dynamic Assortment Optimization in the Presence of Outlier Customers.

[BibT_eX]

[DOI]

Akshay Krishnamurthy

CoRR, 2019

2018

Spectral Learning for Supervised Topic Models.

[BibT_eX]

[DOI]

Yong Ren

Jun Zhu

IEEE Trans. Pattern Anal. Mach. Intell., 2018

A note on a tight lower bound for capacitated MNL-bandit assortment selection models.

[BibT_eX]

[DOI]

Oper. Res. Lett., 2018

Efficient Load Sampling for Worst-Case Structural Analysis Under Force Location Uncertainty.

[BibT_eX]

[DOI]

CoRR, 2018

Dynamic Assortment Selection under the Nested Logit Models.

[BibT_eX]

[DOI]

CoRR, 2018

Robust Nonparametric Regression under Huber's ε-contamination Model.

[BibT_eX]

[DOI]

Pradeep Ravikumar

CoRR, 2018

How Many Samples are Needed to Learn a Convolutional Neural Network?

[BibT_eX]

[DOI]

Xiyu Zhai

Ruslan Salakhutdinov

CoRR, 2018

Near-Linear Time Local Polynomial Nonparametric Estimation.

[BibT_eX]

[DOI]

Yi Wu

CoRR, 2018

Near-Optimal Policies for Dynamic Multinomial Logit Assortment Selection Models.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

How Many Samples are Needed to Estimate a Convolutional Neural Network?

[BibT_eX]

[DOI]

Xiyu Zhai

Ruslan Salakhutdinov

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Linear Quantization by Effective-Resistance Sampling.

[BibT_eX]

[DOI]

Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

Stochastic Zeroth-order Optimization in High Dimensions.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models.

[BibT_eX]

[DOI]

Adams Wei Yu

J. Mach. Learn. Res., 2017

Provably Correct Algorithms for Matrix Column Subset Selection with Selectively Sampled Data.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2017

A Note on Tight Lower Bound for MNL-Bandit Assortment Selection Models.

[BibT_eX]

[DOI]

CoRR, 2017

Non-stationary Stochastic Optimization with Local Spatial and Temporal Changes.

[BibT_eX]

[DOI]

CoRR, 2017

On the Power of Truncated SVD for General High-rank Matrix Estimation Problems.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Near-Optimal Design of Experiments via Regret Minimization.

[BibT_eX]

[DOI]

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Minimax Subsampling for Estimation and Prediction in Low-Dimensional Linear Regression.

[BibT_eX]

[DOI]

CoRR, 2016

Data Poisoning Attacks on Factorization-Based Collaborative Filtering.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

An Improved Gap-Dependency Analysis of the Noisy Power Method.

[BibT_eX]

[DOI]

Proceedings of the 29th Conference on Learning Theory, 2016

Graph Connectivity in Noisy Sparse Subspace Clustering.

[BibT_eX]

[DOI]

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Noise-Adaptive Margin-Based Active Learning and Lower Bounds under Tsybakov Noise Condition.

[BibT_eX]

[DOI]

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Clustering Consistent Sparse Subspace Clustering.

[BibT_eX]

[DOI]

CoRR, 2015

Provably Correct Active Sampling Algorithms for Matrix Column Subset Selection with Missing Data.

[BibT_eX]

[DOI]

CoRR, 2015

Differentially private subspace clustering.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data.

[BibT_eX]

[DOI]

Proceedings of the 32nd International Conference on Machine Learning, 2015

An empirical comparison of sampling techniques for matrix column subset selection.

[BibT_eX]

[DOI]

Proceedings of the 53rd Annual Allerton Conference on Communication, 2015

Column Subset Selection with Missing Data via Active Sampling.

[BibT_eX]

[DOI]

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Noise-adaptive Margin-based Active Learning for Multi-dimensional Data.

[BibT_eX]

[DOI]