1 citations · 1 across the 3 of their papers we have counts for
9 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,…
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…
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…
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…
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…