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Found 10 papers, 4 papers with code
Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval
Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems.
Ever Evolving Evaluator (EV3): Towards Flexible and Reliable Meta-Optimization for Knowledge Distillation
We introduce EV3, a novel meta-optimization framework designed to efficiently train scalable machine learning models through an intuitive explore-assess-adapt protocol.
Overcoming Prior Misspecification in Online Learning to Rank
The recent literature on online learning to rank (LTR) has established the utility of prior knowledge to Bayesian ranking bandit algorithms.
MergeDTS: A Method for Effective Large-Scale Online Ranker Evaluation
Our main finding is that for large-scale Condorcet ranker evaluation problems, MergeDTS outperforms the state-of-the-art dueling bandit algorithms.
BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback
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.
Online Learning to Rank in Stochastic Click Models
In this work, we propose BatchRank, the first online learning to rank algorithm for a broad class of click models.
Copeland Dueling Bandits
A version of the dueling bandit problem is addressed in which a Condorcet winner may not exist.
Contextual Dueling Bandits
The first of these algorithms achieves particularly low regret, even when data is adversarial, although its time and space requirements are linear in the size of the policy space.
Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem
This paper proposes a new method for the K-armed dueling bandit problem, a variation on the regular K-armed bandit problem that offers only relative feedback about pairs of arms.
Bayesian Optimization in a Billion Dimensions via Random Embeddings
Bayesian optimization techniques have been successfully applied to robotics, planning, sensor placement, recommendation, advertising, intelligent user interfaces and automatic algorithm configuration.
