Alex Beutel

ORCID

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 87

Top Venues

Publications

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)
Eric Wallace, Kai Xiao, Reimar Leike, Lilian Weng, Johannes Heidecke, Alex Beutel
The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions. CoRR (2024)
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 (2024)
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)
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)
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 (2023)
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)
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 (2023)
Jindong Gu, Ahmad Beirami, Xuezhi Wang, Alex Beutel, Philip H. S. Torr, Yao Qin
Towards Robust Prompts on Vision-Language Models. CoRR (2023)
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)
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 (2023)
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 (2023)
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 (2023)
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 (2023)
Lucas Monteiro Paes, Ananda Theertha Suresh, Alex Beutel, Flávio P. Calmon, Ahmad Beirami
Multi-Group Fairness Evaluation via Conditional Value-at-Risk Testing. CoRR (2023)
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)
Xiao Ma, Swaroop Mishra, Ahmad Beirami, Alex Beutel, Jilin Chen
Let's Do a Thought Experiment: Using Counterfactuals to Improve Moral Reasoning. CoRR (2023)
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 (2023)
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)
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 (2023)
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 (2023)
Zee Fryer, Vera Axelrod, Ben Packer, Alex Beutel, Jilin Chen, Kellie Webster
Flexible text generation for counterfactual fairness probing. CoRR (2022)
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)
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)
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 (2022)
Esther Rolf, Ben Packer, Alex Beutel, Fernando Diaz
Striving for data-model efficiency: Identifying data externalities on group performance. CoRR (2022)
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 (2022)
Meghana Deodhar, Xiao Ma, Yixin Cai, Alex Koes, Alex Beutel, Jilin Chen
A Human-ML Collaboration Framework for Improving Video Content Reviews. CoRR (2022)
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 (2021)
Yao Qin, Xuezhi Wang, Alex Beutel, Ed H. Chi
Improving Calibration through the Relationship with Adversarial Robustness. NeurIPS (2021)
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)
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 (2021)
Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. CoRR (2021)
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)
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 (2021)
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 (2021)
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)
Ruohan Zhan, Konstantina Christakopoulou, Ya Le, Jayden Ooi, Martin Mladenov, Alex Beutel, Craig Boutilier, Ed Chi, Minmin Chen
Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities. WWW (2021)
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 (2021)
Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. FAccT (2021)
Ananth Balashankar, Alex Beutel, Lakshminarayanan Subramanian
Enhancing Neural Recommender Models through Domain-Specific Concordance. WSDM (2021)
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)
Yao Qin, Xuezhi Wang, Alex Beutel, Ed H. Chi
Improving Uncertainty Estimates through the Relationship with Adversarial Robustness. CoRR (2020)
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 (2020)
Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed Chi
Fairness without Demographics through Adversarially Reweighted Learning. NeurIPS (2020)
Tianlu Wang, Xuezhi Wang, Yao Qin, Ben Packer, Kang Li, Jilin Chen, Alex Beutel, Ed Chi
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation. CoRR (2020)
Weinan Zhang, Xiangyu Zhao, Li Zhao, Dawei Yin, Grace Hui Yang, Alex Beutel
Deep Reinforcement Learning for Information Retrieval: Fundamentals and Advances. SIGIR (2020)
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 (2020)
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 (2020)
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 (2020)
Tianlu Wang, Xuezhi Wang, Yao Qin, Ben Packer, Kang Li, Jilin Chen, Alex Beutel, Ed Chi
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation. EMNLP (1) (2020)
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)
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)
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 (2019)
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)
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)
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 (2019)
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 (2019)
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 (2019)
Candice Schumann, Xuezhi Wang, Alex Beutel, Jilin Chen, Hai Qian, Ed H. Chi
Transfer of Machine Learning Fairness across Domains. CoRR (2019)
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) (2019)
Xuezhi Wang, Nithum Thain, Anu Sinha, Ed H. Chi, Jilin Chen, Alex Beutel
Practical Compositional Fairness: Understanding Fairness in Multi-Task ML Systems. CoRR (2019)
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)
Sahaj Garg, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed H. Chi, Alex Beutel
Counterfactual Fairness in Text Classification through Robustness. AIES (2019)
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)
Sahaj Garg, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed H. Chi, Alex Beutel
Counterfactual Fairness in Text Classification through Robustness. CoRR (2018)
Minmin Chen, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, Ed H. Chi
Top-K Off-Policy Correction for a REINFORCE Recommender System. CoRR (2018)
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)
Konstantina Christakopoulou, Alex Beutel, Rui Li, Sagar Jain, Ed H. Chi
Q&R: A Two-Stage Approach toward Interactive Recommendation. KDD (2018)
Francois Belletti, Alex Beutel, Sagar Jain, Ed Huai-hsin Chi
Factorized Recurrent Neural Architectures for Longer Range Dependence. AISTATS (2018)
Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, Neoklis Polyzotis
The Case for Learned Index Structures. SIGMOD Conference (2018)
Alex Beutel, Ed Huai-hsin Chi, Zhiyuan Cheng, Hubert Pham, John R. Anderson
Beyond Globally Optimal: Focused Learning for Improved Recommendations. WWW (2017)
Neil Shah, Hemank Lamba, Alex Beutel, Christos Faloutsos
OEC: Open-Ended Classification for Future-Proof Link-Fraud Detection. CoRR (2017)
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) (2017)
Neil Shah, Hemank Lamba, Alex Beutel, Christos Faloutsos
The Many Faces of Link Fraud. ICDM (2017)
Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J. Smola
Joint Training of Ratings and Reviews with Recurrent Recommender Networks. ICLR (Workshop) (2017)
Alex Beutel, Jilin Chen, Zhe Zhao, Ed H. Chi
Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations. CoRR (2017)
Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J. Smola, How Jing
Recurrent Recommender Networks. WSDM (2017)
Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, Neoklis Polyzotis
The Case for Learned Index Structures. CoRR (2017)
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) (2016)
Chao-Yuan Wu, Alex Beutel, Amr Ahmed, Alexander J. Smola
Explaining Reviews and Ratings with PACO: Poisson Additive Co-Clustering. WWW (Companion Volume) (2016)
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)
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) (2016)
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) (2016)
Meng Jiang, Peng Cui, Alex Beutel, Christos Faloutsos, Shiqiang Yang
Inferring lockstep behavior from connectivity pattern in large graphs. Knowl. Inf. Syst. 48 (2) (2016)
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)
Bryan Hooi, Hyun Ah Song, Alex Beutel, Neil Shah, Kijung Shin, Christos Faloutsos
FRAUDAR: Bounding Graph Fraud in the Face of Camouflage. KDD (2016)
Alex Beutel, Amr Ahmed, Alexander J. Smola
ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly. WWW (2015)
Maria Giatsoglou, Despoina Chatzakou, Neil Shah, Alex Beutel, Christos Faloutsos, Athena Vakali
ND-Sync: Detecting Synchronized Fraud Activities. PAKDD (2) (2015)
Alex Beutel, Amr Ahmed, Alexander J. Smola
ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly. CoRR (2015)
Alex Beutel, Leman Akoglu, Christos Faloutsos
Graph-Based User Behavior Modeling: From Prediction to Fraud Detection. KDD (2015)
Chao-Yuan Wu, Alex Beutel, Amr Ahmed, Alexander J. Smola
Explaining reviews and ratings with PACO: Poisson Additive Co-Clustering. CoRR (2015)
Bryan Hooi, Neil Shah, Alex Beutel, Stephan Günnemann, Leman Akoglu, Mohit Kumar, Disha Makhija, Christos Faloutsos
BIRDNEST: Bayesian Inference for Ratings-Fraud Detection. CoRR (2015)
Neil Shah, Alex Beutel, Bryan Hooi, Leman Akoglu, Stephan Günnemann, Disha Makhija, Mohit Kumar, Christos Faloutsos
EdgeCentric: Anomaly Detection in Edge-Attributed Networks. CoRR (2015)
Meng Jiang, Alex Beutel, Peng Cui, Bryan Hooi, Shiqiang Yang, Christos Faloutsos
A General Suspiciousness Metric for Dense Blocks in Multimodal Data. ICDM (2015)
Alex Beutel, Leman Akoglu, Christos Faloutsos
Fraud Detection through Graph-Based User Behavior Modeling. CCS (2015)
Abhimanu Kumar, Alex Beutel, Qirong Ho, Eric P. Xing
Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data. AISTATS (2014)
Alex Beutel, Kenton Murray, Christos Faloutsos, Alexander J. Smola
CoBaFi: collaborative bayesian filtering. WWW (2014)
Neil Shah, Alex Beutel, Brian Gallagher, Christos Faloutsos
Spotting Suspicious Link Behavior with fBox: An Adversarial Perspective. ICDM (2014)
Neil Shah, Alex Beutel, Brian Gallagher, Christos Faloutsos
Spotting Suspicious Link Behavior with fBox: An Adversarial Perspective. CoRR (2014)

Alex Beutel.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Alex Beutel