Ga Wu

According to our database, Ga Wu authored at least 31 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Data-centric Prediction Explanation via Kernelized Stein Discrepancy.

Mahtab Sarvmaili, Hassan Sajjad, Ga Wu

CoRR, 2024

Within-basket Recommendation via Neural Pattern Associator.

Kai Luo, Tianshu Shen, Lan Yao, Ga Wu, Aaron Liblong, István Fehérvári, Ruijian An, Jawad Ahmed, Harshit Mishra, Charu Pujari

CoRR, 2024

2023

A User-Centric Analysis of Social Media for Stock Market Prediction.

Mohamed Reda Bouadjenek, Scott Sanner, Ga Wu

ACM Trans. Web, May, 2023

Self-supervised Representation Learning From Random Data Projectors.

Yi Sui, Tongzi Wu, Jesse C. Cresswell, Ga Wu, George Stein, Xiao Shi Huang, Xiaochen Zhang, Maksims Volkovs

CoRR, 2023

Large-scale User Preference Tracking via Asynchronous and Asymmetric Updating at Twitter.

Ga Wu, Shivam Khare, Li Zhou, Yael Brumer, Jun-Ping Ng, Ruowei Wang

Proceedings of the IEEE International Conference on Big Data, 2023

2022

Arbitrary conditional inference in variational autoencoders via fast prior network training.

Ga Wu, Justin Domke, Scott Sanner

Mach. Learn., 2022

fAux: Testing Individual Fairness via Gradient Alignment.

Giuseppe Castiglione, Ga Wu, Christopher Srinivasa, Simon Prince

CoRR, 2022

Scalable Whitebox Attacks on Tree-based Models.

Giuseppe Castiglione, Gavin Ding, Masoud Hashemi, Christopher Srinivasa, Ga Wu

CoRR, 2022

Distributional Contrastive Embedding for Clarification-based Conversational Critiquing.

Tianshu Shen, Zheda Mai, Ga Wu, Scott Sanner

Proceedings of the WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25, 2022

PUMA: Performance Unchanged Model Augmentation for Training Data Removal.

Ga Wu, Masoud Hashemi, Christopher Srinivasa

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Multi-axis Attentive Prediction for Sparse EventData: An Application to Crime Prediction.

Yi Sui, Ga Wu, Scott Sanner

CoRR, 2021

Bayesian Preference Elicitation with Keyphrase-Item Coembeddings for Interactive Recommendation.

Hojin Yang, Scott Sanner, Ga Wu, Jin Peng Zhou

Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, 2021

Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models.

Yi Sui, Ga Wu, Scott Sanner

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Scalable Planning with Deep Neural Network Learned Transition Models.

Ga Wu, Buser Say, Scott Sanner

J. Artif. Intell. Res., 2020

Noise Contrastive Estimation for Autoencoding-based One-Class Collaborative Filtering.

Jin Peng Zhou, Ga Wu, Zheda Mai, Scott Sanner

CoRR, 2020

Latent Linear Critiquing for Conversational Recommender Systems.

Kai Luo, Scott Sanner, Ga Wu, Hanze Li, Hojin Yang

Proceedings of the WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020, 2020

Deep Critiquing for VAE-based Recommender Systems.

Kai Luo, Hojin Yang, Ga Wu, Scott Sanner

Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, 2020

A Ranking Optimization Approach to Latent Linear Critiquing for Conversational Recommender Systems.

Hanze Li, Scott Sanner, Kai Luo, Ga Wu

Proceedings of the RecSys 2020: Fourteenth ACM Conference on Recommender Systems, 2020

Attentive Autoencoders for Multifaceted Preference Learning in One-class Collaborative Filtering.

Zheda Mai, Ga Wu, Kai Luo, Scott Sanner

Proceedings of the 20th International Conference on Data Mining Workshops, 2020

2019

Scalable Nonlinear Planning with Deep Neural Network Learned Transition Models.

Ga Wu, Buser Say, Scott Sanner

CoRR, 2019

Noise Contrastive Estimation for One-Class Collaborative Filtering.

Ga Wu, Maksims Volkovs, Chee Loong Soon, Scott Sanner, Himanshu Rai

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019

One-Class Collaborative Filtering with the Queryable Variational Autoencoder.

Ga Wu, Mohamed Reda Bouadjenek, Scott Sanner

Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019

A Novel Regularizer for Temporally Stable Learning with an Application to Twitter Topic Classification.

Yakun Wang, Ga Wu, Mohamed Reda Bouadjenek, Scott Sanner, Sen Su, Zhongbao Zhang

Proceedings of the 2019 SIAM International Conference on Data Mining, 2019

Deep language-based critiquing for recommender systems.

Ga Wu, Kai Luo, Scott Sanner, Harold Soh

Proceedings of the 13th ACM Conference on Recommender Systems, 2019

2018

Noise Contrastive Estimation for Scalable Linear Models for One-Class Collaborative Filtering.

Ga Wu, Maksims Volkovs, Chee Loong Soon, Scott Sanner, Himanshu Rai

CoRR, 2018

Aesthetic Features for Personalized Photo Recommendation.

Yu Qing Zhou, Ga Wu, Scott Sanner, Putra Manggala

CoRR, 2018

Conditional Inference in Pre-trained Variational Autoencoders via Cross-coding.

Ga Wu, Justin Domke, Scott Sanner

CoRR, 2018

Two-stage Model for Automatic Playlist Continuation at Scale.

Maksims Volkovs, Himanshu Rai, Zhaoyue Cheng, Ga Wu, Yichao Lu, Scott Sanner

Proceedings of the ACM Recommender Systems Challenge, 2018

2017

Scalable Planning with Tensorflow for Hybrid Nonlinear Domains.

Ga Wu, Buser Say, Scott Sanner

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Nonlinear Hybrid Planning with Deep Net Learned Transition Models and Mixed-Integer Linear Programming.

Buser Say, Ga Wu, Yu Qing Zhou, Scott Sanner

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

2015

Bayesian Model Averaging Naive Bayes (BMA-NB): Averaging over an Exponential Number of Feature Models in Linear Time.

Ga Wu, Scott Sanner, Rodrigo F. S. C. Oliveira

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015