253 citations · 533 across the 17 of their papers we have counts for
17 papers
Multi-Fidelity Multi-Armed Bandits Revisited
Xuchuang Wang, Qingyun Wu, Wei Chen +1
We study the multi-fidelity multi-armed bandit (MF-MAB), an extension of the canonical multi-armed bandit (MAB) problem. MF-MAB allows each arm to be pulled with different costs (f…
Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective
Zeyu Zhang, Yi Su, Hui Yuan +5
Off-policy Learning to Rank (LTR) aims to optimize a ranker from data collected by a deployed logging policy. However, existing off-policy learning to rank methods often make stron…
MathChat: Converse to Tackle Challenging Math Problems with LLM Agents
Yiran Wu, Feiran Jia, Shaokun Zhang +7
Employing Large Language Models (LLMs) to address mathematical problems is an intriguing research endeavor, considering the abundance of math problems expressed in natural language…
HyperTime: Hyperparameter Optimization for Combating Temporal Distribution Shifts
Shaokun Zhang, Yiran Wu, Zhonghua Zheng +2
In this work, we propose a hyperparameter optimization method named \emph{HyperTime} to find hyperparameters robust to potential temporal distribution shifts in the unseen test dat…
Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization
Kaixuan Huang, Yu Wu, Xuezhou Zhang +4
Online influence maximization aims to maximize the influence spread of a content in a social network with unknown network model by selecting a few seed nodes. Recent studies follow…
FairAutoML: Embracing Unfairness Mitigation in AutoML
Qingyun Wu, Chi Wang
In this work, we propose an Automated Machine Learning (AutoML) system to search for models not only with good prediction accuracy but also fair. We first investigate the necessity…
ChaCha for Online AutoML
Qingyun Wu, Chi Wang, John Langford +2
We propose the ChaCha (Champion-Challengers) algorithm for making an online choice of hyperparameters in online learning settings. ChaCha handles the process of determining a champ…
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution
Chuanhao Li, Qingyun Wu, Hongning Wang
Collaborative bandit learning, i.e., bandit algorithms that utilize collaborative filtering techniques to improve sample efficiency in online interactive recommendation, has attrac…
Unifying Clustered and Non-stationary Bandits
Chuanhao Li, Qingyun Wu, Hongning Wang
Non-stationary bandits and online clustering of bandits lift the restrictive assumptions in contextual bandits and provide solutions to many important real-world scenarios. Though…
Fast Distributed Bandits for Online Recommendation Systems
Kanak Mahadik, Qingyun Wu, Shuai Li +1
Contextual bandit algorithms are commonly used in recommender systems, where content popularity can change rapidly. These algorithms continuously learn latent mappings between user…
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users
Shijun Li, Wenqiang Lei, Qingyun Wu +3
Static recommendation methods like collaborative filtering suffer from the inherent limitation of performing real-time personalization for cold-start users. Online recommendation,…
Frugal Optimization for Cost-related Hyperparameters
Qingyun Wu, Chi Wang, Silu Huang
The increasing demand for democratizing machine learning algorithms calls for hyperparameter optimization (HPO) solutions at low cost. Many machine learning algorithms have hyperpa…
Estimation-Action-Reflection: Towards Deep Interaction Between Conversational and Recommender Systems
Wenqiang Lei, Xiangnan He, Yisong Miao +4
Recommender systems are embracing conversational technologies to obtain user preferences dynamically, and to overcome inherent limitations of their static models. A successful Conv…
FLAML: A Fast and Lightweight AutoML Library
Chi Wang, Qingyun Wu, Markus Weimer +1
We study the problem of using low computational cost to automate the choices of learners and hyperparameters for an ad-hoc training dataset and error metric, by conducting trials o…
Factorization Bandits for Online Influence Maximization
Qingyun Wu, Zhige Li, Huazheng Wang +2
We study the problem of online influence maximization in social networks. In this problem, a learner aims to identify the set of "best influencers" in a network by interacting with…
Variance Reduction in Gradient Exploration for Online Learning to Rank
Huazheng Wang, Sonwoo Kim, Eric McCord-Snook +2
Online Learning to Rank (OL2R) algorithms learn from implicit user feedback on the fly. The key of such algorithms is an unbiased estimation of gradients, which is often (trivially…
Learning Contextual Bandits in a Non-stationary Environment
Qingyun Wu, Naveen Iyer, Hongning Wang
Multi-armed bandit algorithms have become a reference solution for handling the explore/exploit dilemma in recommender systems, and many other important real-world problems, such a…