Search Results for author: Haolun Wu

Found 19 papers, 6 papers with code

Logits are All We Need to Adapt Closed Models

1 code implementation3 Feb 2025 Gaurush Hiranandani, Haolun Wu, Subhojyoti Mukherjee, Sanmi Koyejo

In this paper, we propose a token-level probability reweighting framework that, given access to logits and a small amount of task-specific data, can effectively steer black-box LLMs toward application-specific content generation.

Prompt Engineering

Retrieval-Augmented Generation for Natural Language Processing: A Survey

no code implementations18 Jul 2024 Shangyu Wu, Ying Xiong, Yufei Cui, Haolun Wu, Can Chen, Ye Yuan, Lianming Huang, Xue Liu, Tei-Wei Kuo, Nan Guan, Chun Jason Xue

Large language models (LLMs) have demonstrated great success in various fields, benefiting from their huge amount of parameters that store knowledge.

Hallucination RAG +2

Design Editing for Offline Model-based Optimization

no code implementations22 May 2024 Ye Yuan, Youyuan Zhang, Can Chen, Haolun Wu, Zixuan Li, Jianmo Li, James J. Clark, Xue Liu

Offline model-based optimization (MBO) aims to maximize a black-box objective function using only an offline dataset of designs and scores.

Denoising model

Diffusion-based Contrastive Learning for Sequential Recommendation

1 code implementation15 May 2024 Ziqiang Cui, Haolun Wu, Bowei He, Ji Cheng, Chen Ma

Most existing approaches generate augmented views of the same user sequence through random augmentation and subsequently maximize their agreement in the representation space.

Contrastive Learning Sequential Recommendation

Towards Group-aware Search Success

no code implementations26 Apr 2024 Haolun Wu, Bhaskar Mitra, Nick Craswell

Traditional measures of search success often overlook the varying information needs of different demographic groups.

Learning to Extract Structured Entities Using Language Models

1 code implementation6 Feb 2024 Haolun Wu, Ye Yuan, Liana Mikaelyan, Alexander Meulemans, Xue Liu, James Hensman, Bhaskar Mitra

Recent advances in machine learning have significantly impacted the field of information extraction, with Language Models (LMs) playing a pivotal role in extracting structured information from unstructured text.

Triplet

Less or More From Teacher: Exploiting Trilateral Geometry For Knowledge Distillation

no code implementations22 Dec 2023 Chengming Hu, Haolun Wu, Xuan Li, Chen Ma, Xi Chen, Jun Yan, Boyu Wang, Xue Liu

A simple neural network then learns the implicit mapping from the intra- and inter-sample relations to an adaptive, sample-wise knowledge fusion ratio in a bilevel-optimization manner.

Bilevel Optimization Click-Through Rate Prediction +3

Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval

no code implementations31 Oct 2023 Haolun Wu, Ofer Meshi, Masrour Zoghi, Fernando Diaz, Xue Liu, Craig Boutilier, Maryam Karimzadehgan

Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems.

Diversity GPR +3

Teacher-Student Architecture for Knowledge Distillation: A Survey

no code implementations8 Aug 2023 Chengming Hu, Xuan Li, Dan Liu, Haolun Wu, Xi Chen, Ju Wang, Xue Liu

Recently, Teacher-Student architectures have been effectively and widely embraced on various knowledge distillation (KD) objectives, including knowledge compression, knowledge expansion, knowledge adaptation, and knowledge enhancement.

Knowledge Distillation regression +1

Result Diversification in Search and Recommendation: A Survey

1 code implementation29 Dec 2022 Haolun Wu, Yansen Zhang, Chen Ma, Fuyuan Lyu, Bowei He, Bhaskar Mitra, Xue Liu

Diversifying return results is an important research topic in retrieval systems in order to satisfy both the various interests of customers and the equal market exposure of providers.

Diversity Retrieval +1

Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation

no code implementations11 Nov 2022 Haolun Wu, Yingxue Zhang, Chen Ma, Wei Guo, Ruiming Tang, Xue Liu, Mark Coates

To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and item representations.

Decision Making Recommendation Systems +2

Coarse-to-Fine Knowledge-Enhanced Multi-Interest Learning Framework for Multi-Behavior Recommendation

no code implementations3 Aug 2022 Chang Meng, Ziqi Zhao, Wei Guo, Yingxue Zhang, Haolun Wu, Chen Gao, Dong Li, Xiu Li, Ruiming Tang

More specifically, we propose a novel Coarse-to-fine Knowledge-enhanced Multi-interest Learning (CKML) framework to learn shared and behavior-specific interests for different behaviors.

Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation

1 code implementation2 Aug 2022 Haolun Wu, Chen Ma, Yingxue Zhang, Xue Liu, Ruiming Tang, Mark Coates

In order to effectively utilize such information, most research adopts the pairwise ranking method on constructed training triplets (user, positive item, negative item) and aims to distinguish between positive items and negative items for each user.

Bilevel Optimization Graph Neural Network +1

Joint Multisided Exposure Fairness for Recommendation

1 code implementation29 Apr 2022 Haolun Wu, Bhaskar Mitra, Chen Ma, Fernando Diaz, Xue Liu

Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or groups of items to individual users of the system.

Exposure Fairness Information Retrieval +2

Multi-FR: A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendation

no code implementations6 May 2021 Haolun Wu, Chen Ma, Bhaskar Mitra, Fernando Diaz, Xue Liu

To address these limitations, we propose a multi-objective optimization framework for fairness-aware recommendation, Multi-FR, that adaptively balances accuracy and fairness for various stakeholders with Pareto optimality guarantee.

Fairness Recommendation Systems

Knowledge-Enhanced Top-K Recommendation in Poincaré Ball

no code implementations13 Jan 2021 Chen Ma, Liheng Ma, Yingxue Zhang, Haolun Wu, Xue Liu, Mark Coates

To effectively make use of the knowledge graph, we propose a recommendation model in the hyperbolic space, which facilitates the learning of the hierarchical structure of knowledge graphs.

Knowledge Graphs Recommendation Systems

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