Search Results for author: Da Xu

Found 22 papers, 5 papers with code

Pretrained Embeddings for E-commerce Machine Learning: When it Fails and Why?

no code implementations9 Apr 2023 Da Xu, Bo Yang

The use of pretrained embeddings has become widespread in modern e-commerce machine learning (ML) systems.

Causal Structure Learning with Recommendation System

no code implementations19 Oct 2022 Shuyuan Xu, Da Xu, Evren Korpeoglu, Sushant Kumar, Stephen Guo, Kannan Achan, Yongfeng Zhang

Discovering the causal mechanism from RS feedback data is both novel and challenging, since RS itself is a source of intervention that can influence both the users' exposure and their willingness to interact.

Decision Making Recommendation Systems

Tutorial: Modern Theoretical Tools for Understanding and Designing Next-generation Information Retrieval System

no code implementations26 Mar 2022 Da Xu, Chuanwei Ruan

Time is now to bring the community a systematic tutorial on how we successfully adapt those tools and make significant progress in understanding, designing, and eventually productionize impactful IR systems.

Causal Inference Decision Making +3

On the Advances and Challenges of Adaptive Online Testing

no code implementations15 Mar 2022 Da Xu, Bo Yang

In recent years, the interest in developing adaptive solutions for online testing has grown significantly in the industry.

Towards Robust Off-policy Learning for Runtime Uncertainty

no code implementations27 Feb 2022 Da Xu, Yuting Ye, Chuanwei Ruan, Bo Yang

Off-policy learning plays a pivotal role in optimizing and evaluating policies prior to the online deployment.

Clutter Edges Detection Algorithms for Structured Clutter Covariance Matrices

no code implementations3 Feb 2022 Tianqi Wang, Da Xu, Chengpeng Hao, Pia Addabbo, Danilo Orlando

This letter deals with the problem of clutter edge detection and localization in training data.

Edge Detection

Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives

no code implementations23 Oct 2021 Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

The recent work by Rendle et al. (2020), based on empirical observations, argues that matrix-factorization collaborative filtering (MCF) compares favorably to neural collaborative filtering (NCF), and conjectures the dot product's superiority over the feed-forward neural network as similarity function.

Collaborative Filtering Transductive Learning

Towards the D-Optimal Online Experiment Design for Recommender Selection

1 code implementation23 Oct 2021 Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

Selecting the optimal recommender via online exploration-exploitation is catching increasing attention where the traditional A/B testing can be slow and costly, and offline evaluations are prone to the bias of history data.

Multi-Armed Bandits

An Efficient Group-based Search Engine Marketing System for E-Commerce

no code implementations24 Jun 2021 Cheng Jie, Da Xu, Zigeng Wang, Lu Wang, Wei Shen

With the increasing scale of search engine marketing, designing an efficient bidding system is becoming paramount for the success of e-commerce companies.

Marketing

A Temporal Kernel Approach for Deep Learning with Continuous-time Information

2 code implementations ICLR 2021 Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

Sequential deep learning models such as RNN, causal CNN and attention mechanism do not readily consume continuous-time information.

Density Estimation

Understanding the role of importance weighting for deep learning

no code implementations ICLR 2021 Da Xu, Yuting Ye, Chuanwei Ruan

The recent paper by Byrd & Lipton (2019), based on empirical observations, raises a major concern on the impact of importance weighting for the over-parameterized deep learning models.

Learning Theory

Theoretical Understandings of Product Embedding for E-commerce Machine Learning

no code implementations24 Feb 2021 Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

The generalization performance in the downstream machine learning task is controlled by the alignment between the embeddings and the product relatedness measure.

BIG-bench Machine Learning Dimensionality Reduction +2

Adversarial Counterfactual Learning and Evaluation for Recommender System

1 code implementation NeurIPS 2020 Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

The feedback data of recommender systems are often subject to what was exposed to the users; however, most learning and evaluation methods do not account for the underlying exposure mechanism.

Causal Inference counterfactual +1

Sparse Symmetric Tensor Regression for Functional Connectivity Analysis

no code implementations28 Oct 2020 Da Xu

Tensor regression models, such as CP regression and Tucker regression, have many successful applications in neuroimaging analysis where the covariates are of ultrahigh dimensionality and possess complex spatial structures.

regression

6 nm super-resolution optical transmission and scattering spectroscopic imaging of carbon nanotubes using a nanometer-scale white light source

no code implementations8 Jun 2020 Xuezhi Ma, Qiushi Liu, Ning Yu, Da Xu, Sanggon Kim, Zebin Liu, Kaili Jiang, Bryan M. Wong, Ruoxue Yan, Ming Liu

Optical hyperspectral imaging based on absorption and scattering of photons at the visible and adjacent frequencies denotes one of the most informative and inclusive characterization methods in material research.

Super-Resolution Optics Materials Science

Inductive Representation Learning on Temporal Graphs

4 code implementations ICLR 2020 Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

Moreover, node and topological features can be temporal as well, whose patterns the node embeddings should also capture.

Graph Attention Graph Embedding +3

Self-attention with Functional Time Representation Learning

2 code implementations NeurIPS 2019 Da Xu, Chuanwei Ruan, Sushant Kumar, Evren Korpeoglu, Kannan Achan

To bridge the gap between modelling time-independent and time-dependent event sequence, we introduce a functional feature map that embeds time span into high-dimensional spaces.

Representation Learning Translation

Product Knowledge Graph Embedding for E-commerce

no code implementations28 Nov 2019 Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, Kannan Achan

In this paper, we propose a new product knowledge graph (PKG) embedding approach for learning the intrinsic product relations as product knowledge for e-commerce.

Knowledge Graph Embedding Marketing +2

Knowledge-aware Complementary Product Representation Learning

no code implementations16 Mar 2019 Da Xu, Chuanwei Ruan, Jason Cho, Evren Korpeoglu, Sushant Kumar, Kannan Achan

Standard usage of representation learning emphasizes on only one set of embedding, which is problematic for modelling such properties of complementariness.

Multi-Task Learning Recommendation Systems +1

Generative Graph Convolutional Network for Growing Graphs

no code implementations6 Mar 2019 Da Xu, Chuanwei Ruan, Kamiya Motwani, Evren Korpeoglu, Sushant Kumar, Kannan Achan

Here we propose a unified generative graph convolutional network that learns node representations for all nodes adaptively in a generative model framework, by sampling graph generation sequences constructed from observed graph data.

Graph Generation Graph Reconstruction +1

Cannot find the paper you are looking for? You can Submit a new open access paper.