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2020 – today
- 2024
[j8]Aradhana Sinha, Ananth Balashankar, Ahmad Beirami, Thi Avrahami, Jilin Chen, Alex Beutel:
Break it, Imitate it, Fix it: Robustness by Generating Human-Like Attacks. Trans. Mach. Learn. Res. 2024 (2024)- [c57]Hansa Srinivasan
, Candice Schumann, Aradhana Sinha, David Madras, Gbolahan Oluwafemi Olanubi, Alex Beutel, Susanna Ricco, Jilin Chen:
Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images. FAccT 2024: 797-821 - [c56]Sidharth Mudgal, Jong Lee, Harish Ganapathy, YaGuang Li, Tao Wang, Yanping Huang, Zhifeng Chen, Heng-Tze Cheng, Michael Collins, Trevor Strohman, Jilin Chen, Alex Beutel, Ahmad Beirami:
Controlled Decoding from Language Models. ICML 2024 - [i46]Hansa Srinivasan, Candice Schumann, Aradhana Sinha, David Madras, Gbolahan Oluwafemi Olanubi, Alex Beutel, Susanna Ricco, Jilin Chen:
Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images. CoRR abs/2401.14322 (2024) - [i45]Eric Wallace, Kai Xiao, Reimar Leike, Lilian Weng, Johannes Heidecke, Alex Beutel:
The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions. CoRR abs/2404.13208 (2024) - 2023
- [c55]Ananth Balashankar, Xuezhi Wang, Yao Qin, Ben Packer, Nithum Thain, Ed H. Chi, Jilin Chen, Alex Beutel:
Improving Classifier Robustness through Active Generative Counterfactual Data Augmentation. EMNLP (Findings) 2023: 127-139 - [c54]Preethi Lahoti, Nicholas Blumm, Xiao Ma, Raghavendra Kotikalapudi, Sahitya Potluri, Qijun Tan, Hansa Srinivasan, Ben Packer, Ahmad Beirami, Alex Beutel, Jilin Chen:
Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting. EMNLP 2023: 10383-10405 - [c53]Zhouxing Shi, Nicholas Carlini, Ananth Balashankar, Ludwig Schmidt, Cho-Jui Hsieh, Alex Beutel, Yao Qin:
Effective Robustness against Natural Distribution Shifts for Models with Different Training Data. NeurIPS 2023 - [c52]Yueqi Wang, Yoni Halpern, Shuo Chang, Jingchen Feng, Elaine Ya Le, Longfei Li, Xujian Liang, Min-Cheng Huang, Shane Li, Alex Beutel, Yaping Zhang, Shuchao Bi:
Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders. RecSys 2023: 1049-1053 - [c51]Yao Qin, Xuezhi Wang, Balaji Lakshminarayanan, Ed H. Chi, Alex Beutel:
What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel. SaTML 2023: 365-376 - [i44]Zhouxing Shi, Nicholas Carlini, Ananth Balashankar, Ludwig Schmidt, Cho-Jui Hsieh, Alex Beutel, Yao Qin:
Effective Robustness against Natural Distribution Shifts for Models with Different Training Data. CoRR abs/2302.01381 (2023) - [i43]Yao Qin, Xuezhi Wang, Balaji Lakshminarayanan, Ed H. Chi, Alex Beutel:
What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel. CoRR abs/2302.11188 (2023) - [i42]Jindong Gu, Ahmad Beirami, Xuezhi Wang, Alex Beutel, Philip H. S. Torr, Yao Qin:
Towards Robust Prompts on Vision-Language Models. CoRR abs/2304.08479 (2023) - [i41]Ananth Balashankar, Xuezhi Wang, Yao Qin, Ben Packer, Nithum Thain, Jilin Chen, Ed H. Chi, Alex Beutel:
Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals. CoRR abs/2305.13535 (2023) - [i40]Xiao Ma, Swaroop Mishra, Ahmad Beirami, Alex Beutel, Jilin Chen:
Let's Do a Thought Experiment: Using Counterfactuals to Improve Moral Reasoning. CoRR abs/2306.14308 (2023) - [i39]James Atwood, Tina Tian, Ben Packer, Meghana Deodhar, Jilin Chen, Alex Beutel, Flavien Prost, Ahmad Beirami:
Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classification. CoRR abs/2307.05728 (2023) - [i38]Yueqi Wang, Yoni Halpern, Shuo Chang, Jingchen Feng, Elaine Ya Le, Longfei Li, Xujian Liang, Min-Cheng Huang, Shane Li, Alex Beutel, Yaping Zhang, Shuchao Bi:
Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders. CoRR abs/2308.12256 (2023) - [i37]Preethi Lahoti, Nicholas Blumm, Xiao Ma, Raghavendra Kotikalapudi, Sahitya Potluri, Qijun Tan, Hansa Srinivasan, Ben Packer, Ahmad Beirami, Alex Beutel, Jilin Chen:
Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting. CoRR abs/2310.16523 (2023) - [i36]Aradhana Sinha, Ananth Balashankar, Ahmad Beirami, Thi Avrahami, Jilin Chen, Alex Beutel:
Break it, Imitate it, Fix it: Robustness by Generating Human-Like Attacks. CoRR abs/2310.16955 (2023) - [i35]Ananth Balashankar, Xiao Ma, Aradhana Sinha, Ahmad Beirami, Yao Qin, Jilin Chen, Alex Beutel:
Improving Few-shot Generalization of Safety Classifiers via Data Augmented Parameter-Efficient Fine-Tuning. CoRR abs/2310.16959 (2023) - [i34]Sidharth Mudgal, Jong Lee, Harish Ganapathy, YaGuang Li, Tao Wang, Yanping Huang, Zhifeng Chen, Heng-Tze Cheng, Michael Collins, Trevor Strohman, Jilin Chen, Alex Beutel, Ahmad Beirami:
Controlled Decoding from Language Models. CoRR abs/2310.17022 (2023) - [i33]Lucas Monteiro Paes, Ananda Theertha Suresh, Alex Beutel, Flávio P. Calmon, Ahmad Beirami:
Multi-Group Fairness Evaluation via Conditional Value-at-Risk Testing. CoRR abs/2312.03867 (2023) - 2022
- [j7]Alexander D'Amour, Katherine A. Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yi-An Ma, Cory Y. McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley:
Underspecification Presents Challenges for Credibility in Modern Machine Learning. J. Mach. Learn. Res. 23: 226:1-226:61 (2022) - [c50]Meghana Deodhar, Xiao Ma, Yixin Cai, Alex Koes, Alex Beutel, Jilin Chen:
A human-ML collaboration framework for improving video content reviews. CIKM Workshops 2022 - [c49]Yao Qin, Chiyuan Zhang, Ting Chen, Balaji Lakshminarayanan, Alex Beutel, Xuezhi Wang:
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation. NeurIPS 2022 - [i32]Zee Fryer, Vera Axelrod, Ben Packer, Alex Beutel, Jilin Chen, Kellie Webster:
Flexible text generation for counterfactual fairness probing. CoRR abs/2206.13757 (2022) - [i31]Flavien Prost, Ben Packer, Jilin Chen, Li Wei, Pierre Kremp, Nick Blumm, Susan Wang, Tulsee Doshi, Tonia Osadebe, Lukasz Heldt, Ed H. Chi, Alex Beutel:
Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations. CoRR abs/2210.07755 (2022) - [i30]Meghana Deodhar, Xiao Ma, Yixin Cai, Alex Koes, Alex Beutel, Jilin Chen:
A Human-ML Collaboration Framework for Improving Video Content Reviews. CoRR abs/2210.09500 (2022) - [i29]Esther Rolf, Ben Packer, Alex Beutel, Fernando Diaz:
Striving for data-model efficiency: Identifying data externalities on group performance. CoRR abs/2211.06348 (2022) - 2021
- [c48]Flavien Prost, Pranjal Awasthi, Nick Blumm, Aditee Kumthekar, Trevor Potter, Li Wei, Xuezhi Wang, Ed H. Chi, Jilin Chen, Alex Beutel:
Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective. AIES 2021: 873-883 - [c47]Ananth Balashankar, Xuezhi Wang, Ben Packer, Nithum Thain, Ed H. Chi, Alex Beutel:
Can We Improve Model Robustness through Secondary Attribute Counterfactuals? EMNLP (1) 2021: 4701-4712 - [c46]Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang:
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. FAccT 2021: 206-214 - [c45]Yuyan Wang, Xuezhi Wang, Alex Beutel, Flavien Prost, Jilin Chen, Ed H. Chi:
Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning. KDD 2021: 1748-1757 - [c44]Yao Qin, Xuezhi Wang, Alex Beutel, Ed H. Chi:
Improving Calibration through the Relationship with Adversarial Robustness. NeurIPS 2021: 14358-14369 - [c43]Xuezhi Wang, Nithum Thain, Anu Sinha, Flavien Prost, Ed H. Chi, Jilin Chen, Alex Beutel:
Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems. WSDM 2021: 436-444 - [c42]Ananth Balashankar, Alex Beutel, Lakshminarayanan Subramanian:
Enhancing Neural Recommender Models through Domain-Specific Concordance. WSDM 2021: 1002-1010 - [c41]Ruohan Zhan, Konstantina Christakopoulou, Ya Le, Jayden Ooi, Martin Mladenov, Alex Beutel, Craig Boutilier, Ed H. Chi, Minmin Chen:
Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities. WWW 2021: 3872-3883 - [i28]Sirui Yao, Yoni Halpern, Nithum Thain, Xuezhi Wang, Kang Lee, Flavien Prost, Ed H. Chi, Jilin Chen, Alex Beutel:
Measuring Recommender System Effects with Simulated Users. CoRR abs/2101.04526 (2021) - [i27]Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang:
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. CoRR abs/2102.08410 (2021) - [i26]Ruohan Zhan, Konstantina Christakopoulou, Ya Le, Jayden Ooi, Martin Mladenov, Alex Beutel, Craig Boutilier, Ed H. Chi, Minmin Chen:
Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities. CoRR abs/2105.02377 (2021) - [i25]Flavien Prost, Pranjal Awasthi, Nick Blumm, Aditee Kumthekar, Trevor Potter, Li Wei, Xuezhi Wang, Ed H. Chi, Jilin Chen, Alex Beutel:
Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective. CoRR abs/2105.09985 (2021) - [i24]Yuyan Wang, Xuezhi Wang, Alex Beutel, Flavien Prost, Jilin Chen, Ed H. Chi:
Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning. CoRR abs/2106.02705 (2021) - [i23]Yao Qin, Chiyuan Zhang, Ting Chen, Balaji Lakshminarayanan, Alex Beutel, Xuezhi Wang:
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation. CoRR abs/2110.07858 (2021) - 2020
- [c40]Tianlu Wang, Xuezhi Wang, Yao Qin, Ben Packer, Kang Li, Jilin Chen, Alex Beutel, Ed H. Chi:
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation. EMNLP (1) 2020: 5141-5146 - [c39]Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed H. Chi:
Fairness without Demographics through Adversarially Reweighted Learning. NeurIPS 2020 - [c38]Weinan Zhang, Xiangyu Zhao, Li Zhao, Dawei Yin, Grace Hui Yang, Alex Beutel:
Deep Reinforcement Learning for Information Retrieval: Fundamentals and Advances. SIGIR 2020: 2468-2471 - [i22]Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed H. Chi:
Fairness without Demographics through Adversarially Reweighted Learning. CoRR abs/2006.13114 (2020) - [i21]Yao Qin, Xuezhi Wang, Alex Beutel, Ed H. Chi:
Improving Uncertainty Estimates through the Relationship with Adversarial Robustness. CoRR abs/2006.16375 (2020) - [i20]Tianlu Wang, Xuezhi Wang, Yao Qin, Ben Packer, Kang Li, Jilin Chen, Alex Beutel, Ed H. Chi:
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation. CoRR abs/2010.02338 (2020) - [i19]Kellie Webster, Xuezhi Wang, Ian Tenney, Alex Beutel, Emily Pitler, Ellie Pavlick, Jilin Chen, Slav Petrov:
Measuring and Reducing Gendered Correlations in Pre-trained Models. CoRR abs/2010.06032 (2020) - [i18]Alexander D'Amour, Katherine A. Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yi-An Ma, Cory Y. McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley:
Underspecification Presents Challenges for Credibility in Modern Machine Learning. CoRR abs/2011.03395 (2020) - [i17]Hussam Abu-Libdeh, Deniz Altinbüken, Alex Beutel, Ed H. Chi, Lyric Doshi, Tim Kraska, Xiaozhou Li, Andy Ly, Christopher Olston:
Learned Indexes for a Google-scale Disk-based Database. CoRR abs/2012.12501 (2020)
2010 – 2019
- 2019
- [j6]Alexandra Olteanu, Jean Garcia-Gathright, Maarten de Rijke, Michael D. Ekstrand, Adam Roegiest, Aldo Lipani, Alex Beutel, Ana Lucic, Ana-Andreea Stoica, Anubrata Das, Asia Biega, Bart Voorn, Claudia Hauff, Damiano Spina, David D. Lewis, Douglas W. Oard, Emine Yilmaz, Faegheh Hasibi, Gabriella Kazai, Graham McDonald, Hinda Haned, Iadh Ounis, Ilse van der Linden, Joris Baan, Kamuela N. Lau, Krisztian Balog, Mahmoud F. Sayed, Maria Panteli, Mark Sanderson, Matthew Lease, Preethi Lahoti, Toshihiro Kamishima:
FACTS-IR: fairness, accountability, confidentiality, transparency, and safety in information retrieval. SIGIR Forum 53(2): 20-43 (2019) - [c37]Sahaj Garg, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed H. Chi, Alex Beutel:
Counterfactual Fairness in Text Classification through Robustness. AIES 2019: 219-226 - [c36]Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Allison Woodruff, Christine Luu, Pierre Kreitmann, Jonathan Bischof, Ed H. Chi:
Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements. AIES 2019: 453-459 - [c35]Tim Kraska, Mohammad Alizadeh, Alex Beutel, Ed H. Chi, Ani Kristo, Guillaume Leclerc, Samuel Madden, Hongzi Mao, Vikram Nathan:
SageDB: A Learned Database System. CIDR 2019 - [c34]Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, Cristos Goodrow:
Fairness in Recommendation Ranking through Pairwise Comparisons. KDD 2019: 2212-2220 - [c33]Minmin Chen, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, Ed H. Chi:
Top-K Off-Policy Correction for a REINFORCE Recommender System. WSDM 2019: 456-464 - [c32]Jiaxi Tang, Francois Belletti, Sagar Jain, Minmin Chen, Alex Beutel, Can Xu, Ed H. Chi:
Towards Neural Mixture Recommender for Long Range Dependent User Sequences. WWW 2019: 1782-1793 - [i16]Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Allison Woodruff, Christine Luu, Pierre Kreitmann, Jonathan Bischof, Ed H. Chi:
Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements. CoRR abs/1901.04562 (2019) - [i15]Jiaxi Tang, Francois Belletti, Sagar Jain, Minmin Chen, Alex Beutel, Can Xu, Ed H. Chi:
Towards Neural Mixture Recommender for Long Range Dependent User Sequences. CoRR abs/1902.08588 (2019) - [i14]Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, Cristos Goodrow:
Fairness in Recommendation Ranking through Pairwise Comparisons. CoRR abs/1903.00780 (2019) - [i13]Candice Schumann, Xuezhi Wang, Alex Beutel, Jilin Chen, Hai Qian, Ed H. Chi:
Transfer of Machine Learning Fairness across Domains. CoRR abs/1906.09688 (2019) - [i12]Flavien Prost, Hai Qian, Qiuwen Chen, Ed H. Chi, Jilin Chen, Alex Beutel:
Toward a better trade-off between performance and fairness with kernel-based distribution matching. CoRR abs/1910.11779 (2019) - [i11]Xuezhi Wang, Nithum Thain, Anu Sinha, Ed H. Chi, Jilin Chen, Alex Beutel:
Practical Compositional Fairness: Understanding Fairness in Multi-Task ML Systems. CoRR abs/1911.01916 (2019) - 2018
- [c31]Francois Belletti, Alex Beutel, Sagar Jain, Ed Huai-hsin Chi:
Factorized Recurrent Neural Architectures for Longer Range Dependence. AISTATS 2018: 1522-1530 - [c30]Konstantina Christakopoulou, Alex Beutel, Rui Li, Sagar Jain, Ed H. Chi:
Q&R: A Two-Stage Approach toward Interactive Recommendation. KDD 2018: 139-148 - [c29]Qian Zhao, Jilin Chen, Minmin Chen, Sagar Jain, Alex Beutel, Francois Belletti, Ed H. Chi:
Categorical-attributes-based item classification for recommender systems. RecSys 2018: 320-328 - [c28]Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, Neoklis Polyzotis:
The Case for Learned Index Structures. SIGMOD Conference 2018: 489-504 - [c27]Alex Beutel, Paul Covington, Sagar Jain, Can Xu, Jia Li, Vince Gatto, Ed H. Chi:
Latent Cross: Making Use of Context in Recurrent Recommender Systems. WSDM 2018: 46-54 - [r2]Evangelos E. Papalexakis, Alex Beutel, Peter Steenkiste:
Network Anomaly Detection Using Co-clustering. Encyclopedia of Social Network Analysis and Mining. 2nd Ed. 2018 - [i10]Sahaj Garg, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed H. Chi, Alex Beutel:
Counterfactual Fairness in Text Classification through Robustness. CoRR abs/1809.10610 (2018) - [i9]Minmin Chen, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, Ed H. Chi:
Top-K Off-Policy Correction for a REINFORCE Recommender System. CoRR abs/1812.02353 (2018) - 2017
- [j5]Bryan Hooi, Kijung Shin, Hyun Ah Song, Alex Beutel, Neil Shah, Christos Faloutsos:
Graph-Based Fraud Detection in the Face of Camouflage. ACM Trans. Knowl. Discov. Data 11(4): 44:1-44:26 (2017) - [c26]Neil Shah, Hemank Lamba, Alex Beutel, Christos Faloutsos:
The Many Faces of Link Fraud. ICDM 2017: 1069-1074 - [c25]Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J. Smola:
Joint Training of Ratings and Reviews with Recurrent Recommender Networks. ICLR (Workshop) 2017 - [c24]Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J. Smola, How Jing:
Recurrent Recommender Networks. WSDM 2017: 495-503 - [c23]Alex Beutel, Ed Huai-hsin Chi, Zhiyuan Cheng, Hubert Pham, John R. Anderson:
Beyond Globally Optimal: Focused Learning for Improved Recommendations. WWW 2017: 203-212 - [i8]Neil Shah, Hemank Lamba, Alex Beutel, Christos Faloutsos:
OEC: Open-Ended Classification for Future-Proof Link-Fraud Detection. CoRR abs/1704.01420 (2017) - [i7]Alex Beutel, Jilin Chen, Zhe Zhao, Ed H. Chi:
Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations. CoRR abs/1707.00075 (2017) - [i6]Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, Neoklis Polyzotis:
The Case for Learned Index Structures. CoRR abs/1712.01208 (2017) - 2016
- [j4]Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos, Shiqiang Yang:
Inferring lockstep behavior from connectivity pattern in large graphs. Knowl. Inf. Syst. 48(2): 399-428 (2016) - [j3]Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos, Shiqiang Yang:
Catching Synchronized Behaviors in Large Networks: A Graph Mining Approach. ACM Trans. Knowl. Discov. Data 10(4): 35:1-35:27 (2016) - [j2]Meng Jiang, Alex Beutel, Peng Cui, Bryan Hooi, Shiqiang Yang, Christos Faloutsos:
Spotting Suspicious Behaviors in Multimodal Data: A General Metric and Algorithms. IEEE Trans. Knowl. Data Eng. 28(8): 2187-2200 (2016) - [j1]Pankaj K. Agarwal, Alex Beutel, Thomas Mølhave:
TerraNNI: Natural Neighbor Interpolation on 2D and 3D Grids Using a GPU. ACM Trans. Spatial Algorithms Syst. 2(2): 7:1-7:31 (2016) - [c22]Neil Shah, Alex Beutel, Bryan Hooi, Leman Akoglu, Stephan Günnemann, Disha Makhija, Mohit Kumar, Christos Faloutsos:
EdgeCentric: Anomaly Detection in Edge-Attributed Networks. ICDM Workshops 2016: 327-334 - [c21]Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, Christos Faloutsos:
FRAUDAR: Bounding Graph Fraud in the Face of Camouflage. KDD 2016: 895-904 - [c20]Bryan Hooi, Neil Shah, Alex Beutel, Stephan Günnemann, Leman Akoglu, Mohit Kumar, Disha Makhija, Christos Faloutsos:
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection. SDM 2016: 495-503 - [c19]Chao-Yuan Wu, Alex Beutel, Amr Ahmed, Alexander J. Smola:
Explaining Reviews and Ratings with PACO: Poisson Additive Co-Clustering. WWW (Companion Volume) 2016: 127-128 - 2015
- [c18]Alex Beutel, Leman Akoglu, Christos Faloutsos:
Fraud Detection through Graph-Based User Behavior Modeling. CCS 2015: 1696-1697 - [c17]Meng Jiang, Alex Beutel, Peng Cui, Bryan Hooi, Shiqiang Yang, Christos Faloutsos:
A General Suspiciousness Metric for Dense Blocks in Multimodal Data. ICDM 2015: 781-786 - [c16]Alex Beutel, Leman Akoglu, Christos Faloutsos:
Graph-Based User Behavior Modeling: From Prediction to Fraud Detection. KDD 2015: 2309-2310 - [c15]Maria Giatsoglou, Despoina Chatzakou, Neil Shah, Alex Beutel, Christos Faloutsos, Athena Vakali:
ND-Sync: Detecting Synchronized Fraud Activities. PAKDD (2) 2015: 201-214 - [c14]Alex Beutel, Amr Ahmed, Alexander J. Smola:
ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly. WWW 2015: 119-129 - [i5]Alex Beutel, Amr Ahmed, Alexander J. Smola:
ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly. CoRR abs/1501.00199 (2015) - [i4]Neil Shah, Alex Beutel, Bryan Hooi, Leman Akoglu, Stephan Günnemann, Disha Makhija, Mohit Kumar, Christos Faloutsos:
EdgeCentric: Anomaly Detection in Edge-Attributed Networks. CoRR abs/1510.05544 (2015) - [i3]Bryan Hooi, Neil Shah, Alex Beutel, Stephan Günnemann, Leman Akoglu, Mohit Kumar, Disha Makhija, Christos Faloutsos:
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection. CoRR abs/1511.06030 (2015) - [i2]Chao-Yuan Wu, Alex Beutel, Amr Ahmed, Alexander J. Smola:
Explaining reviews and ratings with PACO: Poisson Additive Co-Clustering. CoRR abs/1512.01845 (2015) - 2014
- [c13]Abhimanu Kumar, Alex Beutel, Qirong Ho, Eric P. Xing:
Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data. AISTATS 2014: 531-539 - [c12]Neil Shah, Alex Beutel, Brian Gallagher, Christos Faloutsos:
Spotting Suspicious Link Behavior with fBox: An Adversarial Perspective. ICDM 2014: 959-964 - [c11]Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos, Shiqiang Yang:
CatchSync: catching synchronized behavior in large directed graphs. KDD 2014: 941-950 - [c10]Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos, Shiqiang Yang:
Inferring Strange Behavior from Connectivity Pattern in Social Networks. PAKDD (1) 2014: 126-138 - [c9]Alex Beutel, Partha Pratim Talukdar, Abhimanu Kumar, Christos Faloutsos, Evangelos E. Papalexakis, Eric P. Xing:
FlexiFaCT: Scalable Flexible Factorization of Coupled Tensors on Hadoop. SDM 2014: 109-117 - [c8]Alex Beutel, Kenton Murray, Christos Faloutsos, Alexander J. Smola:
CoBaFi: collaborative bayesian filtering. WWW 2014: 97-108 - [c7]Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos, Shiqiang Yang:
Detecting suspicious following behavior in multimillion-node social networks. WWW (Companion Volume) 2014: 305-306 - [r1]Evangelos E. Papalexakis, Alex Beutel, Peter Steenkiste:
Network Anomaly Detection Using Co-clustering. Encyclopedia of Social Network Analysis and Mining 2014: 1054-1068 - [i1]Neil Shah, Alex Beutel, Brian Gallagher, Christos Faloutsos:
Spotting Suspicious Link Behavior with fBox: An Adversarial Perspective. CoRR abs/1410.3915 (2014) - 2013
- [c6]Alex Beutel, Wanhong Xu, Venkatesan Guruswami, Christopher Palow, Christos Faloutsos:
CopyCatch: stopping group attacks by spotting lockstep behavior in social networks. WWW 2013: 119-130 - 2012
- [c5]Evangelos E. Papalexakis, Alex Beutel, Peter Steenkiste:
Network Anomaly Detection Using Co-clustering. ASONAM 2012: 403-410 - [c4]Alex Beutel, B. Aditya Prakash, Roni Rosenfeld, Christos Faloutsos:
Interacting viruses in networks: can both survive? KDD 2012: 426-434 - [c3]B. Aditya Prakash, Alex Beutel, Roni Rosenfeld, Christos Faloutsos:
Winner takes all: competing viruses or ideas on fair-play networks. WWW 2012: 1037-1046 - 2011
- [c2]Alex Beutel, Thomas Mølhave, Pankaj K. Agarwal, Arnold P. Boedihardjo, James A. Shine:
TerraNNI: natural neighbor interpolation on a 3D grid using a GPU. GIS 2011: 64-74 - 2010
- [c1]Alex Beutel, Thomas Mølhave, Pankaj K. Agarwal:
Natural neighbor interpolation based grid DEM construction using a GPU. GIS 2010: 172-181
Coauthor Index
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