Search Results for author:
Found 8 papers, 2 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.
IMO$^3$: Interactive Multi-Objective Off-Policy Optimization
This problem has been studied extensively in the setting of known objective functions.
CORe: Capitalizing On Rewards in Bandit Exploration
We propose a bandit algorithm that explores purely by randomizing its past observations.
Separate and Attend in Personal Email Search
In personal email search, user queries often impose different requirements on different aspects of the retrieved emails.
Domain Adaptation for Enterprise Email Search
To address this data challenge, in this paper we propose a domain adaptation approach that fine-tunes the global model to each individual enterprise.
Multi-Task Learning for Email Search Ranking with Auxiliary Query Clustering
In this paper, we study how to obtain query type in an unsupervised fashion and how to incorporate this information into query-dependent ranking models.
