29 citations · 66 across the 11 of their papers we have counts for
11 papers
Stabilized Higher-Order Influence Functions: Statistical Theory of a Class of Bilinear Forms
Na Liu, Chang Li, Yujia Gu +1
Higher-order influence functions, introduced in a series of articles (Robins et al., 2008, 2009a; van der Vaart, 2014; Robins et al., 2016, 2023; Liu et al., 2017), are a unified f…
First-Order Geometry, Spectral Compression, and Structural Compatibility under Bounded Computation
Changkai Li
Optimization under structural constraints is typically analyzed through projection or penalty methods, obscuring the geometric mechanism by which constraints shape admissible dynam…
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…
Optimizing Ranking Systems Online as Bandits
Chang Li
Ranking system is the core part of modern retrieval and recommender systems, where the goal is to rank candidate items given user contexts. Optimizing ranking systems online means…
Federated Unbiased Learning to Rank
Chang Li, Hua Ouyang
Unbiased Learning to Rank (ULTR) studies the problem of learning a ranking function based on biased user interactions. In this framework, ULTR algorithms have to rely on a large am…
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…
Online Learning to Rank with List-level Feedback for Image Filtering
Chang Li, Artem Grotov, Ilya Markov +1
Online learning to rank (OLTR) via implicit feedback has been extensively studied for document retrieval in cases where the feedback is available at the level of individual items.…
MergeDTS: A Method for Effective Large-Scale Online Ranker Evaluation
Chang Li, Ilya Markov, Maarten de Rijke +1
Online ranker evaluation is one of the key challenges in information retrieval. While the preferences of rankers can be inferred by interleaving methods, the problem of how to effe…
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…