2 citations · 5 across the 13 of their papers we have counts for
9 papers · 1 filter
Fast A/B/n Testing: Exact Multi-Policy Comparison via Tree-Coupled Feedback Sharing
Yuxiao Wen
Online platforms increasingly compare many adaptive decision policies---ranking systems, recommendation algorithms, pricing rules, and language-model agents---while each reward-bea…
Learning to Bid with Unknown Private Values in Budget-Constrained First-Price Auctions
Zihao Hu, Yuxiao Wen, Yuan Yao +2
We study the operational problem of automated bidding in repeated first-price auctions under budget and return-on-spend (RoS) constraints. In this setting, an auto-bidder must tran…
Optimal Arm Elimination Algorithms for Combinatorial Bandits
Yuxiao Wen, Yanjun Han, Zhengyuan Zhou
Combinatorial bandits extend the classical bandit framework to settings where the learner selects multiple arms in each round, motivated by applications such as online recommendati…
Calibrated Recommendations with Contextual Bandits
Diego Feijer, Himan Abdollahpouri, Sanket Gupta +7
Spotify's Home page features a variety of content types, including music, podcasts, and audiobooks. However, historical data is heavily skewed toward music, making it challenging t…
Adversarial Combinatorial Semi-bandits with Graph Feedback
Yuxiao Wen
In combinatorial semi-bandits, a learner repeatedly selects from a combinatorial decision set of arms, receives the realized sum of rewards, and observes the rewards of the individ…
Joint Value Estimation and Bidding in Repeated First-Price Auctions
Yuxiao Wen, Yanjun Han, Zhengyuan Zhou
We study regret minimization in repeated first-price auctions (FPAs), where a bidder observes only the realized outcome after each auction -- win or loss. This setup reflects pract…
Stochastic contextual bandits with graph feedback: from independence number to MAS number
Yuxiao Wen, Yanjun Han, Zhengyuan Zhou
We consider contextual bandits with graph feedback, a class of interactive learning problems with richer structures than vanilla contextual bandits, where taking an action reveals…
Which Frequencies do CNNs Need? Emergent Bottleneck Structure in Feature Learning
Yuxiao Wen, Arthur Jacot
We describe the emergence of a Convolution Bottleneck (CBN) structure in CNNs, where the network uses its first few layers to transform the input representation into a representati…
Active operator inference for learning low-dimensional dynamical-system models from noisy data
Wayne Isaac Tan Uy, Yuepeng Wang, Yuxiao Wen +1
Noise poses a challenge for learning dynamical-system models because already small variations can distort the dynamics described by trajectory data. This work builds on operator in…