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Ga Wu
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2020 – today
- 2024
- [c18]Yi Sui, Tongzi Wu, Jesse C. Cresswell, Ga Wu, George Stein, Xiao Shi Huang, Xiaochen Zhang, Maksims Volkovs:
Self-supervised Representation Learning from Random Data Projectors. ICLR 2024 - [i17]Kai Luo, Tianshu Shen, Lan Yao, Ga Wu, Aaron Liblong, István Fehérvári, Ruijian An, Jawad Ahmed, Harshit Mishra, Charu Pujari:
Within-basket Recommendation via Neural Pattern Associator. CoRR abs/2401.16433 (2024) - [i16]Mahtab Sarvmaili, Hassan Sajjad, Ga Wu:
Data-centric Prediction Explanation via Kernelized Stein Discrepancy. CoRR abs/2403.15576 (2024) - [i15]Hammad Rizwan, Domenic Rosati, Ga Wu, Hassan Sajjad:
Resolving Lexical Bias in Edit Scoping with Projector Editor Networks. CoRR abs/2408.10411 (2024) - [i14]Sagar Paresh Shah, Ga Wu, Sean W. Kortschot, Samuel Daviau:
Towards Understanding Human Emotional Fluctuations with Sparse Check-In Data. CoRR abs/2409.06863 (2024) - [i13]Mahtab Sarvmaili, Hassan Sajjad, Ga Wu:
Towards Understanding the Feasibility of Machine Unlearning. CoRR abs/2410.03043 (2024) - 2023
- [j3]Mohamed Reda Bouadjenek
, Scott Sanner
A User-Centric Analysis of Social Media for Stock Market Prediction. ACM Trans. Web 17(2): 9:1-9:22 (2023) - [c17]Ga Wu, Shivam Khare, Li Zhou, Yael Brumer, Jun-Ping Ng, Ruowei Wang:
Large-scale User Preference Tracking via Asynchronous and Asymmetric Updating at Twitter. IEEE Big Data 2023: 315-324 - [i12]Yi Sui, Tongzi Wu, Jesse C. Cresswell
Self-supervised Representation Learning From Random Data Projectors. CoRR abs/2310.07756 (2023) - 2022
- [j2]Ga Wu
Arbitrary conditional inference in variational autoencoders via fast prior network training. Mach. Learn. 111(7): 2537-2559 (2022) - [c16]Ga Wu, Masoud Hashemi, Christopher Srinivasa:
PUMA: Performance Unchanged Model Augmentation for Training Data Removal. AAAI 2022: 8675-8682 - [c15]Tianshu Shen, Zheda Mai, Ga Wu, Scott Sanner:
Distributional Contrastive Embedding for Clarification-based Conversational Critiquing. WWW 2022: 2422-2432 - [i11]Ga Wu, Masoud Hashemi, Christopher Srinivasa:
PUMA: Performance Unchanged Model Augmentation for Training Data Removal. CoRR abs/2203.00846 (2022) - [i10]Giuseppe Castiglione, Gavin Ding, Masoud Hashemi, Christopher Srinivasa, Ga Wu:
Scalable Whitebox Attacks on Tree-based Models. CoRR abs/2204.00103 (2022) - [i9]Giuseppe Castiglione, Ga Wu, Christopher Srinivasa, Simon Prince:
fAux: Testing Individual Fairness via Gradient Alignment. CoRR abs/2210.06288 (2022) - 2021
- [c14]Yi Sui, Ga Wu, Scott Sanner:
Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models. NeurIPS 2021: 23347-23358 - [c13]Hojin Yang, Scott Sanner, Ga Wu, Jin Peng Zhou:
Bayesian Preference Elicitation with Keyphrase-Item Coembeddings for Interactive Recommendation. UMAP 2021: 55-64 - [i8]Yi Sui, Ga Wu, Scott Sanner:
Multi-axis Attentive Prediction for Sparse EventData: An Application to Crime Prediction. CoRR abs/2110.01794 (2021) - 2020
- [j1]Ga Wu, Buser Say, Scott Sanner:
Scalable Planning with Deep Neural Network Learned Transition Models. J. Artif. Intell. Res. 68: 571-606 (2020) - [c12]Zheda Mai, Ga Wu, Kai Luo, Scott Sanner:
Attentive Autoencoders for Multifaceted Preference Learning in One-class Collaborative Filtering. ICDM (Workshops) 2020: 165-172 - [c11]Hanze Li, Scott Sanner, Kai Luo, Ga Wu:
A Ranking Optimization Approach to Latent Linear Critiquing for Conversational Recommender Systems. RecSys 2020: 13-22 - [c10]Kai Luo, Hojin Yang, Ga Wu, Scott Sanner:
Deep Critiquing for VAE-based Recommender Systems. SIGIR 2020: 1269-1278 - [c9]Kai Luo, Scott Sanner, Ga Wu
Latent Linear Critiquing for Conversational Recommender Systems. WWW 2020: 2535-2541 - [i7]Jin Peng Zhou, Ga Wu, Zheda Mai, Scott Sanner:
Noise Contrastive Estimation for Autoencoding-based One-Class Collaborative Filtering. CoRR abs/2008.01246 (2020) - [i6]Zheda Mai, Ga Wu, Kai Luo, Scott Sanner:
Attentive Autoencoders for Multifaceted Preference Learning in One-class Collaborative Filtering. CoRR abs/2010.12803 (2020)
2010 – 2019
- 2019
- [c8]Ga Wu
Deep language-based critiquing for recommender systems. RecSys 2019: 137-145 - [c7]Yakun Wang, Ga Wu, Mohamed Reda Bouadjenek
A Novel Regularizer for Temporally Stable Learning with an Application to Twitter Topic Classification. SDM 2019: 217-225 - [c6]Ga Wu
Noise Contrastive Estimation for One-Class Collaborative Filtering. SIGIR 2019: 135-144 - [c5]Ga Wu
One-Class Collaborative Filtering with the Queryable Variational Autoencoder. SIGIR 2019: 921-924 - [i5]Ga Wu, Buser Say, Scott Sanner:
Scalable Nonlinear Planning with Deep Neural Network Learned Transition Models. CoRR abs/1904.02873 (2019) - 2018
- [c4]Maksims Volkovs, Himanshu Rai, Zhaoyue Cheng, Ga Wu
Two-stage Model for Automatic Playlist Continuation at Scale. RecSys Challenge 2018: 9:1-9:6 - [i4]Ga Wu, Justin Domke, Scott Sanner:
Conditional Inference in Pre-trained Variational Autoencoders via Cross-coding. CoRR abs/1805.07785 (2018) - [i3]Yu Qing Zhou, Ga Wu, Scott Sanner, Putra Manggala:
Aesthetic Features for Personalized Photo Recommendation. CoRR abs/1809.00060 (2018) - [i2]Ga Wu, Maksims Volkovs, Chee Loong Soon, Scott Sanner, Himanshu Rai:
Noise Contrastive Estimation for Scalable Linear Models for One-Class Collaborative Filtering. CoRR abs/1811.00697 (2018) - 2017
- [c3]Buser Say, Ga Wu, Yu Qing Zhou, Scott Sanner:
Nonlinear Hybrid Planning with Deep Net Learned Transition Models and Mixed-Integer Linear Programming. IJCAI 2017: 750-756 - [c2]Ga Wu, Buser Say, Scott Sanner:
Scalable Planning with Tensorflow for Hybrid Nonlinear Domains. NIPS 2017: 6273-6283 - [i1]Ga Wu, Buser Say, Scott Sanner:
Scalable Planning with Tensorflow for Hybrid Nonlinear Domains. CoRR abs/1704.07511 (2017) - 2015
- [c1]Ga Wu, Scott Sanner, Rodrigo F. S. C. Oliveira:
Bayesian Model Averaging Naive Bayes (BMA-NB): Averaging over an Exponential Number of Feature Models in Linear Time. AAAI 2015: 3094-3100
Coauthor Index
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last updated on 2024-11-08 21:28 CET by the dblp team
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