29 citations · 66 across the 11 of their papers we have counts for
5 papers · 1 filter
Fibration Policy Optimization
Chang Li, Tshihao Tsu, Yaren Zhang +2
Large language models are increasingly trained as heterogeneous systems spanning multiple domains, expert partitions, and agentic pipelines, yet prevalent proximal objectives opera…
Cascading Hybrid Bandits: Online Learning to Rank for Relevance and Diversity
Chang Li, Haoyun Feng, Maarten de Rijke
Relevance ranking and result diversification are two core areas in modern recommender systems. Relevance ranking aims at building a ranked list sorted in decreasing order of item r…
Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model
Chang Li, Maarten de Rijke
Non-stationarity appears in many online applications such as web search and advertising. In this paper, we study the online learning to rank problem in a non-stationary environment…
Incremental Sparse Bayesian Ordinal Regression
Chang Li, Maarten de Rijke
Ordinal Regression (OR) aims to model the ordering information between different data categories, which is a crucial topic in multi-label learning. An important class of approaches…
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback
Chang Li, Branislav Kveton, Tor Lattimore +4
In this paper, we study the problem of safe online learning to re-rank, where user feedback is used to improve the quality of displayed lists. Learning to rank has traditionally be…