1 citations · 4 across the 12 of their papers we have counts for
12 papers
Achieving First-Order Statistical Improvements in Data-Driven Optimization: From No-Free-Lunch to Amplified Decision Perturbation
Henry Lam, Tianyu Wang
Recent proliferation of data-optimization integration has led to a range of methods that aim to improve the statistical performance of data-driven optimization decisions. However,…
Billions-Scale Forecast Reconciliation
Tianyu Wang, Matthew C. Johnson, Steven Klee +1
The problem of combining multiple forecasts of related quantities that obey expected equality and additivity constraints, often referred to a hierarchical forecast reconciliation,…
A Proof of Talagrand's Creating Large Sets Conjecture
Xuan Fang, Tianyu Wang
Talagrand conjectured that if a family of sets over is of large measure, then constant times of unions of sets in will cover a…
Batched Bandits with Heavy-Tailed Rewards
Yunwen Guo, Yunlun Shu, Gongyi Zhuo +1
The batched multi-armed bandit (MAB) problem, where rewards are collected in batches, is pivotal in applications like clinical trials. While prior work assumes light-tailed reward…
An Experimental Approach for Running-Time Estimation of Multi-objective Evolutionary Algorithms in Numerical Optimization
Han Huang, Tianyu Wang, Chaoda Peng +2
Multi-objective evolutionary algorithms (MOEAs) have become essential tools for solving multi-objective optimization problems (MOPs), making their running time analysis crucial for…
Revisit First-order Methods for Geodesically Convex Optimization
Yunlu Shu, Jiaxin Jiang, Lei Shi +1
In a seminal work of Zhang and Sra, gradient descent methods for geodesically convex optimization were comprehensively studied. In particular, Zhang and Sra derived a comparison in…
Is Cross-Validation the Gold Standard to Evaluate Model Performance?
Garud Iyengar, Henry Lam, Tianyu Wang
Cross-Validation (CV) is the default choice for evaluating the performance of machine learning models. Despite its wide usage, their statistical benefits have remained half-underst…
Contextual Optimization under Covariate Shift: A Robust Approach by Intersecting Wasserstein Balls
Tianyu Wang, Ningyuan Chen, Chun Wang
In contextual optimization, a decision-maker leverages contextual information, often referred to as covariates, to better resolve uncertainty and make informed decisions. In this p…
Rethinking Distribution Shifts: Empirical Analysis and Modeling for Tabular Data
Tianyu Wang, Jiashuo Liu, Peng Cui +1
Different distribution shifts require different interventions, and algorithms must be grounded in the specific shifts they address. However, methodological development for robust a…
Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization
Garud Iyengar, Henry Lam, Tianyu Wang
In data-driven optimization, the sample performance of the obtained decision typically incurs an optimistic bias against the true performance, a phenomenon commonly known as the Op…
Hedging Complexity in Generalization via a Parametric Distributionally Robust Optimization Framework
Garud Iyengar, Henry Lam, Tianyu Wang
Empirical risk minimization (ERM) and distributionally robust optimization (DRO) are popular approaches for solving stochastic optimization problems that appear in operations manag…
Distributionally Robust Prescriptive Analytics with Wasserstein Distance
Tianyu Wang, Ningyuan Chen, Chun Wang
In prescriptive analytics, the decision-maker observes historical samples of , where is the uncertain problem parameter and is the concurrent covariate, without kno…