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 (AAAI, 2015)

Nonlinear Hybrid Planning with Deep Net Learned Transition Models and Mixed-Integer Linear Programming. - Buser Say; Ga Wu; Yu Qing Zhou; Scott Sanner (IJCAI, 2017)

Scalable Planning with Tensorflow for Hybrid Nonlinear Domains. - Ga Wu; Buser Say; Scott Sanner (NIPS, 2017)

Two-stage Model for Automatic Playlist Continuation at Scale. - Maksims Volkovs; Himanshu Rai; Zhaoyue Cheng; Ga Wu; Yichao Lu; Scott Sanner (RecSys Challenge, 2018)

Conditional Inference in Pre-trained Variational Autoencoders via Cross-coding. - Ga Wu; Justin Domke; Scott Sanner (CoRR, 2018)

Aesthetic Features for Personalized Photo Recommendation. - Yu Qing Zhou; Ga Wu; Scott Sanner; Putra Manggala (CoRR, 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)

Deep language-based critiquing for recommender systems. - Ga Wu; Kai Luo; Scott Sanner; Harold Soh (RecSys, 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 (SDM, 2019)

One-Class Collaborative Filtering with the Queryable Variational Autoencoder. - Ga Wu; Mohamed Reda Bouadjenek; Scott Sanner (SIGIR, 2019)

Noise Contrastive Estimation for One-Class Collaborative Filtering. - Ga Wu; Maksims Volkovs; Chee Loong Soon; Scott Sanner; Himanshu Rai (SIGIR, 2019)

Scalable Nonlinear Planning with Deep Neural Network Learned Transition Models. - Ga Wu; Buser Say; Scott Sanner (CoRR, 2019)

Attentive Autoencoders for Multifaceted Preference Learning in One-class Collaborative Filtering. - Zheda Mai; Ga Wu; Kai Luo; Scott Sanner (ICDM (Workshops), 2020)

A Ranking Optimization Approach to Latent Linear Critiquing for Conversational Recommender Systems. - Hanze Li; Scott Sanner; Kai Luo; Ga Wu (RecSys, 2020)

Deep Critiquing for VAE-based Recommender Systems. - Kai Luo; Hojin Yang; Ga Wu; Scott Sanner (SIGIR, 2020)

Latent Linear Critiquing for Conversational Recommender Systems. - Kai Luo; Scott Sanner; Ga Wu; Hanze Li; Hojin Yang (WWW, 2020)

Noise Contrastive Estimation for Autoencoding-based One-Class Collaborative Filtering. - Jin Peng Zhou; Ga Wu; Zheda Mai; Scott Sanner (CoRR, 2020)

Scalable Planning with Deep Neural Network Learned Transition Models. - Ga Wu; Buser Say; Scott Sanner (J. Artif. Intell. Res., 2020)

Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models. - Yi Sui 005; Ga Wu; Scott Sanner (NeurIPS, 2021)

Bayesian Preference Elicitation with Keyphrase-Item Coembeddings for Interactive Recommendation. - Hojin Yang; Scott Sanner; Ga Wu; Jin Peng Zhou (UMAP, 2021)

Multi-axis Attentive Prediction for Sparse EventData: An Application to Crime Prediction. - Yi Sui 005; Ga Wu; Scott Sanner (CoRR, 2021)

PUMA: Performance Unchanged Model Augmentation for Training Data Removal. - Ga Wu; Masoud Hashemi; Christopher Srinivasa (AAAI, 2022)

Distributional Contrastive Embedding for Clarification-based Conversational Critiquing. - Tianshu Shen; Zheda Mai; Ga Wu; Scott Sanner (WWW, 2022)

Scalable Whitebox Attacks on Tree-based Models. - Giuseppe Castiglione; Gavin Ding; Masoud Hashemi; Christopher Srinivasa; Ga Wu (CoRR, 2022)

fAux: Testing Individual Fairness via Gradient Alignment. - Giuseppe Castiglione; Ga Wu; Christopher Srinivasa; Simon Prince (CoRR, 2022)

Arbitrary conditional inference in variational autoencoders via fast prior network training. - Ga Wu; Justin Domke; Scott Sanner (Mach. Learn., 2022)

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 (IEEE Big Data, 2023)

A User-Centric Analysis of Social Media for Stock Market Prediction. - Mohamed Reda Bouadjenek; Scott Sanner; Ga Wu (ACM Trans. Web, 2023)

Self-supervised Representation Learning from Random Data Projectors. - Yi Sui 001; Tongzi Wu; Jesse C. Cresswell; Ga Wu; George Stein; Xiao Shi Huang; Xiaochen Zhang; Maksims Volkovs (ICLR, 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)

Resolving Lexical Bias in Edit Scoping with Projector Editor Networks. - Hammad Rizwan; Domenic Rosati; Ga Wu; Hassan Sajjad 001 (CoRR, 2024)

Towards Understanding Human Emotional Fluctuations with Sparse Check-In Data. - Sagar Paresh Shah; Ga Wu; Sean W. Kortschot; Samuel Daviau (CoRR, 2024)

Towards Understanding the Feasibility of Machine Unlearning. - Mahtab Sarvmaili; Hassan Sajjad 001; Ga Wu (CoRR, 2024)

Data-centric Prediction Explanation via Kernelized Stein Discrepancy. - Mahtab Sarvmaili; Hassan Sajjad 001; Ga Wu (ICLR, 2025)

Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality. - Naihe Feng; Yi Sui 001; Shiyi Hou; Jesse C. Cresswell; Ga Wu (SIGIR, 2025)