Timezone: »
There has been a surge of recent interest in graph representation learning (GRL). GRL methods have generally fallen into three main categories, based on the availability of labeled data. The first, network embedding, focuses on learning unsupervised representations of relational structure. The second, graph regularized neural networks, leverages graphs to augment neural network losses with a regularization objective for semi-supervised learning. The third, graph neural networks, aims to learn differentiable functions over discrete topologies with arbitrary structure. However, despite the popularity of these areas there has been surprisingly little work on unifying the three paradigms. Here, we aim to bridge the gap between network embedding, graph regularization and graph neural networks. We propose a comprehensive taxonomy of GRL methods, aiming to unify several disparate bodies of work. Specifically, we propose the GraphEDM framework, which generalizes popular algorithms for semi-supervised learning (e.g. GraphSage, GCN, GAT), and unsupervised learning (e.g. DeepWalk, node2vec) of graph representations into a single consistent approach. To illustrate the generality of GraphEDM, we fit over thirty existing methods into this framework. We believe that this unifying view both provides a solid foundation for understanding the intuition behind these methods, and enables future research in the area.
Author Information
Ines Chami (Stanford University)
Sami Abu-El-Haija (Google Research)
Bryan Perozzi (Google Research)
Christopher Ré (Stanford)
Kevin Murphy (Google)
More from the Same Authors
-
2021 : Personalized Benchmarking with the Ludwig Benchmarking Toolkit »
Avanika Narayan · Piero Molino · Karan Goel · Willie Neiswanger · Christopher Ré -
2021 : SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation »
Arjun Desai · Andrew Schmidt · Elka Rubin · Christopher Sandino · Marianne Black · Valentina Mazzoli · Kathryn Stevens · Robert Boutin · Christopher Ré · Garry Gold · Brian Hargreaves · Akshay Chaudhari -
2021 : Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning »
Zachary Nado · Neil Band · Mark Collier · Josip Djolonga · Mike Dusenberry · Sebastian Farquhar · Qixuan Feng · Angelos Filos · Marton Havasi · Rodolphe Jenatton · Ghassen Jerfel · Jeremiah Liu · Zelda Mariet · Jeremy Nixon · Shreyas Padhy · Jie Ren · Tim G. J. Rudner · Yeming Wen · Florian Wenzel · Kevin Murphy · D. Sculley · Balaji Lakshminarayanan · Jasper Snoek · Yarin Gal · Dustin Tran -
2021 : Combining Recurrent, Convolutional, and Continuous-Time Models with Structured Learnable Linear State-Space Layers »
Isys Johnson · Albert Gu · Karan Goel · Khaled Saab · Tri Dao · Atri Rudra · Christopher Ré -
2022 : Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learning »
Zeel Bharatkumar Patel · Nipun Batra · Kevin Murphy -
2022 : GraphWorld: Fake Graphs BringReal Insights for GNNs »
John Palowitch · Anton Tsitsulin · Bryan Perozzi · Brandon Mayer -
2022 : Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank »
Alessandro Epasto · Vahab Mirrokni · Bryan Perozzi · Anton Tsitsulin · Peilin Zhong -
2023 : Does In-Context Operator Learning Generalize to Domain-Shifted Settings? »
Jerry Liu · N. Benjamin Erichson · Kush Bhatia · Michael Mahoney · Christopher Ré -
2023 : Scalable Deep Potentials as Implicit Hierarchical Semi-Separable Operators »
Michael Poli · Michael Poli · Stefano Massaroli · Stefano Massaroli · Christopher Ré · Christopher Ré · Stefano Ermon · Stefano Ermon -
2023 : TART: A plug-and-play Transformer module for task-agnostic reasoning »
Kush Bhatia · Avanika Narayan · Christopher De Sa · Christopher Ré -
2023 : TART: A plug-and-play Transformer module for task-agnostic reasoning »
Kush Bhatia · Avanika Narayan · Christopher De Sa · Christopher Ré -
2023 : Context-Aware Meta-Learning »
Christopher Fifty · Dennis Duan · Ronald Junkins · Ehsan Amid · Jure Leskovec · Christopher Ré · Sebastian Thrun -
2023 : Measuring and Improving Recall in Convolutional Language Models »
Evan Sabri Eyuboglu · Simran Arora · Aman Timalsina · Isys Johnson · Michael Poli · James Zou · Atri Rudra · Christopher Ré -
2023 : The Graph Lottery Ticket Hypothesis: Finding Sparse, Informative Graph Structure »
Anton Tsitsulin · Bryan Perozzi -
2023 : Talk like a Graph: Encoding Graphs for Large Language Models »
Bahare Fatemi · Jonathan Halcrow · Bryan Perozzi -
2023 : [Paper-Oral 9] Improving Linear Attention via Softmax Mimicry »
Michael Zhang · Kush Bhatia · Hermann Kumbong · Christopher Ré -
2023 : [Paper-Oral 4] FlashFFTConv: Efficient Convolutions for Long Sequences with Tensor Cores »
Dan Fu · Hermann Kumbong · Eric Nguyen · Christopher Ré -
2023 Poster: Graph Clustering with Graph Neural Networks »
Anton Tsitsulin · John Palowitch · Bryan Perozzi · Emmanuel Müller -
2023 Poster: HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution »
Eric Nguyen · Michael Poli · Marjan Faizi · Armin Thomas · Michael Wornow · Callum Birch-Sykes · Stefano Massaroli · Aman Patel · Clayton Rabideau · Yoshua Bengio · Stefano Ermon · Christopher Ré · Stephen Baccus -
2023 Poster: A case for reframing automated medical image classification as segmentation »
Sarah Hooper · Mayee Chen · Khaled Saab · Kush Bhatia · Curtis Langlotz · Christopher Ré -
2023 Poster: TART: A plug-and-play Transformer module for task-agnostic reasoning »
Kush Bhatia · Avanika Narayan · Christopher De Sa · Christopher Ré -
2023 Poster: Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions »
Stefano Massaroli · Michael Poli · Dan Fu · Hermann Kumbong · Rom Parnichkun · David Romero · Aman Timalsina · Quinn McIntyre · Beidi Chen · Atri Rudra · Ce Zhang · Christopher Ré · Stefano Ermon · Yoshua Bengio -
2023 Poster: Skill-it! A data-driven skills framework for understanding and training language models »
Mayee Chen · Nicholas Roberts · Kush Bhatia · Jue WANG · Ce Zhang · Frederic Sala · Christopher Ré -
2023 Poster: Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture »
Dan Fu · Simran Arora · Jessica Grogan · Isys Johnson · Evan Sabri Eyuboglu · Armin Thomas · Benjamin Spector · Michael Poli · Atri Rudra · Christopher Ré -
2023 Poster: Embroid: Unsupervised Prediction Smoothing Can Improve Few-Shot Classification »
Neel Guha · Mayee Chen · Kush Bhatia · Azalia Mirhoseini · Frederic Sala · Christopher Ré -
2023 Poster: Learning Large Graph Property Prediction via Graph Segment Training »
Kaidi Cao · Mangpo Phothilimtha · Sami Abu-El-Haija · Dustin Zelle · Yanqi Zhou · Charith Mendis · Jure Leskovec · Bryan Perozzi -
2023 Oral: Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture »
Dan Fu · Simran Arora · Jessica Grogan · Isys Johnson · Evan Sabri Eyuboglu · Armin Thomas · Benjamin Spector · Michael Poli · Atri Rudra · Christopher Ré -
2023 Poster: H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models »
Zhenyu Zhang · Ying Sheng · Tianyi Zhou · Tianlong Chen · Lianmin Zheng · Ruisi Cai · Zhao Song · Yuandong Tian · Christopher Ré · Clark Barrett · Zhangyang "Atlas" Wang · Beidi Chen -
2023 Poster: TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs »
Mangpo Phothilimtha · Sami Abu-El-Haija · Kaidi Cao · Bahare Fatemi · Michael Burrows · Charith Mendis · Bryan Perozzi -
2023 Poster: LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models »
Neel Guha · Julian Nyarko · Daniel Ho · Christopher Ré · Adam Chilton · Aditya K · Alex Chohlas-Wood · Austin Peters · Brandon Waldon · Daniel Rockmore · Diego Zambrano · Dmitry Talisman · Enam Hoque · Faiz Surani · Frank Fagan · Galit Sarfaty · Gregory Dickinson · Haggai Porat · Jason Hegland · Jessica Wu · Joe Nudell · Joel Niklaus · John Nay · Jonathan Choi · Kevin Tobia · Margaret Hagan · Megan Ma · Michael Livermore · Nikon Rasumov-Rahe · Nils Holzenberger · Noam Kolt · Peter Henderson · Sean Rehaag · Sharad Goel · Shang Gao · Spencer Williams · Sunny Gandhi · Tom Zur · Varun Iyer · Zehua Li -
2023 Expo Talk Panel: Graph Learning Meets Artificial Intelligence »
Bryan Perozzi -
2022 Spotlight: Machine Learning on Graphs: A Model and Comprehensive Taxonomy »
Ines Chami · Sami Abu-El-Haija · Bryan Perozzi · Christopher Ré · Kevin Murphy -
2022 Poster: On the Parameterization and Initialization of Diagonal State Space Models »
Albert Gu · Karan Goel · Ankit Gupta · Christopher Ré -
2022 Poster: Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank »
Alessandro Epasto · Vahab Mirrokni · Bryan Perozzi · Anton Tsitsulin · Peilin Zhong -
2022 Poster: Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks »
Minji Yoon · John Palowitch · Dustin Zelle · Ziniu Hu · Ruslan Salakhutdinov · Bryan Perozzi -
2022 Poster: Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging Data »
Armin Thomas · Christopher Ré · Russell Poldrack -
2022 Poster: HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions »
Lingjiao Chen · Zhihua Jin · Evan Sabri Eyuboglu · Christopher Ré · Matei Zaharia · James Zou -
2022 Poster: FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness »
Tri Dao · Dan Fu · Stefano Ermon · Atri Rudra · Christopher Ré -
2022 Poster: Contrastive Adapters for Foundation Model Group Robustness »
Michael Zhang · Christopher Ré -
2022 Poster: Decentralized Training of Foundation Models in Heterogeneous Environments »
Binhang Yuan · Yongjun He · Jared Davis · Tianyi Zhang · Tri Dao · Beidi Chen · Percy Liang · Christopher Ré · Ce Zhang -
2022 Poster: Transform Once: Efficient Operator Learning in Frequency Domain »
Michael Poli · Stefano Massaroli · Federico Berto · Jinkyoo Park · Tri Dao · Christopher Ré · Stefano Ermon -
2022 Poster: S4ND: Modeling Images and Videos as Multidimensional Signals with State Spaces »
Eric Nguyen · Karan Goel · Albert Gu · Gordon Downs · Preey Shah · Tri Dao · Stephen Baccus · Christopher Ré -
2022 Poster: Fine-tuning Language Models over Slow Networks using Activation Quantization with Guarantees »
Jue WANG · Binhang Yuan · Luka Rimanic · Yongjun He · Tri Dao · Beidi Chen · Christopher Ré · Ce Zhang -
2022 : TF-GNN Basics (Hands on) »
Sami A Abu-El-Haija -
2022 : GNN Basics »
Sami A Abu-El-Haija -
2022 Expo Workshop: Graph Neural Networks in Tensorflow: A Practical Guide »
Bryan Perozzi · Sami A Abu-El-Haija · Neslihan Bulut · Brandon Mayer -
2022 : Welcome »
Bryan Perozzi -
2021 Workshop: Bayesian Deep Learning »
Yarin Gal · Yingzhen Li · Sebastian Farquhar · Christos Louizos · Eric Nalisnick · Andrew Gordon Wilson · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2021 Poster: Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers »
Albert Gu · Isys Johnson · Karan Goel · Khaled Saab · Tri Dao · Atri Rudra · Christopher Ré -
2021 Poster: Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data »
Qi Zhu · Natalia Ponomareva · Jiawei Han · Bryan Perozzi -
2021 Poster: Rethinking Neural Operations for Diverse Tasks »
Nicholas Roberts · Mikhail Khodak · Tri Dao · Liam Li · Christopher Ré · Ameet Talwalkar -
2021 Poster: Implicit SVD for Graph Representation Learning »
Sami Abu-El-Haija · Hesham Mostafa · Marcel Nassar · Valentino Crespi · Greg Ver Steeg · Aram Galstyan -
2020 : Focused Breakout Session »
Ines Chami · Joey Bose -
2020 : Panel Discussion »
Joey Bose · Emile Mathieu · Charline Le Lan · Ines Chami -
2020 : Poster Session 1 on Gather.Town »
Joey Bose · Ines Chami -
2020 : Tree Covers: An Alternative to Metric Embeddings »
Roshni Sahoo · Ines Chami · Christopher Ré -
2020 Workshop: Differential Geometry meets Deep Learning (DiffGeo4DL) »
Joey Bose · Emile Mathieu · Charline Le Lan · Ines Chami · Frederic Sala · Christopher De Sa · Maximilian Nickel · Christopher Ré · Will Hamilton -
2020 Poster: HiPPO: Recurrent Memory with Optimal Polynomial Projections »
Albert Gu · Tri Dao · Stefano Ermon · Atri Rudra · Christopher Ré -
2020 Spotlight: HiPPO: Recurrent Memory with Optimal Polynomial Projections »
Albert Gu · Tri Dao · Stefano Ermon · Atri Rudra · Christopher Ré -
2020 Oral: Hogwild!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent »
Benjamin Recht · Christopher Ré · Stephen Wright · Feng Niu -
2020 Poster: From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering »
Ines Chami · Albert Gu · Vaggos Chatziafratis · Christopher Ré -
2020 : GNNs and Graph Embeddings »
Bryan Perozzi -
2020 Expo Workshop: Mining and Learning with Graphs at Scale »
Vahab Mirrokni · Bryan Perozzi · Jakub Lacki · Jonathan Halcrow · Jaqui C Herman -
2019 : Poster Session #2 »
Yunzhu Li · Peter Meltzer · Jianing Sun · Guillaume SALHA · Marin Vlastelica Pogančić · Chia-Cheng Liu · Fabrizio Frasca · Marc-Alexandre Côté · Vikas Verma · Abdulkadir Celikkanat · Pierluca D'Oro · Priyesh Vijayan · Maria Schuld · Petar Veličković · Kshitij Tayal · Yulong Pei · Hao Xu · Lei Chen · Pengyu Cheng · Ines Chami · Dongkwan Kim · Guilherme Gomes · Lukasz Maziarka · Jessica Hoffmann · Ron Levie · Antonia Gogoglou · Shunwang Gong · Federico Monti · Wenlin Wang · Yan Leng · Salvatore Vivona · Daniel Flam-Shepherd · Chester Holtz · Li Zhang · MAHMOUD KHADEMI · I-Chung Hsieh · Aleksandar Stanić · Ziqiao Meng · Yuhang Jiao -
2019 : Poster session »
Sebastian Farquhar · Erik Daxberger · Andreas Look · Matt Benatan · Ruiyi Zhang · Marton Havasi · Fredrik Gustafsson · James A Brofos · Nabeel Seedat · Micha Livne · Ivan Ustyuzhaninov · Adam Cobb · Felix D McGregor · Patrick McClure · Tim R. Davidson · Gaurush Hiranandani · Sanjeev Arora · Masha Itkina · Didrik Nielsen · William Harvey · Matias Valdenegro-Toro · Stefano Peluchetti · Riccardo Moriconi · Tianyu Cui · Vaclav Smidl · Taylan Cemgil · Jack Fitzsimons · He Zhao · · mariana vargas vieyra · Apratim Bhattacharyya · Rahul Sharma · Geoffroy Dubourg-Felonneau · Jonathan Warrell · Slava Voloshynovskiy · Mihaela Rosca · Jiaming Song · Andrew Ross · Homa Fashandi · Ruiqi Gao · Hooshmand Shokri Razaghi · Joshua Chang · Tim Xiao · Vanessa Boehm · Giorgio Giannone · Ranganath Krishnan · Joe Davison · Arsenii Ashukha · Jeremiah Liu · Sicong (Sheldon) Huang · Evgenii Nikishin · Sunho Park · Nilesh Ahuja · Mahesh Subedar · · Artyom Gadetsky · Jhosimar Arias Figueroa · Tim G. J. Rudner · Waseem Aslam · Adrián Csiszárik · John Moberg · Ali Hebbal · Kathrin Grosse · Pekka Marttinen · Bang An · Hlynur Jónsson · Samuel Kessler · Abhishek Kumar · Mikhail Figurnov · Omesh Tickoo · Steindor Saemundsson · Ari Heljakka · Dániel Varga · Niklas Heim · Simone Rossi · Max Laves · Waseem Gharbieh · Nicholas Roberts · Luis Armando Pérez Rey · Matthew Willetts · Prithvijit Chakrabarty · Sumedh Ghaisas · Carl Shneider · Wray Buntine · Kamil Adamczewski · Xavier Gitiaux · Suwen Lin · Hao Fu · Gunnar Rätsch · Aidan Gomez · Erik Bodin · Dinh Phung · Lennart Svensson · Juliano Tusi Amaral Laganá Pinto · Milad Alizadeh · Jianzhun Du · Kevin Murphy · Beatrix Benkő · Shashaank Vattikuti · Jonathan Gordon · Christopher Kanan · Sontje Ihler · Darin Graham · Michael Teng · Louis Kirsch · Tomas Pevny · Taras Holotyak -
2019 Workshop: Bayesian Deep Learning »
Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Eric Nalisnick · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2019 Workshop: KR2ML - Knowledge Representation and Reasoning Meets Machine Learning »
Veronika Thost · Christian Muise · Kartik Talamadupula · Sameer Singh · Christopher Ré -
2019 Poster: On the Downstream Performance of Compressed Word Embeddings »
Avner May · Jian Zhang · Tri Dao · Christopher Ré -
2019 Spotlight: On the Downstream Performance of Compressed Word Embeddings »
Avner May · Jian Zhang · Tri Dao · Christopher Ré -
2019 Poster: Multi-Resolution Weak Supervision for Sequential Data »
Paroma Varma · Frederic Sala · Shiori Sagawa · Jason Fries · Dan Fu · Saelig Khattar · Ashwini Ramamoorthy · Ke Xiao · Kayvon Fatahalian · James Priest · Christopher Ré -
2019 Poster: Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices »
Vincent Chen · Sen Wu · Alexander Ratner · Jen Weng · Christopher Ré -
2019 Poster: Hyperbolic Graph Convolutional Neural Networks »
Ines Chami · Zhitao Ying · Christopher Ré · Jure Leskovec -
2019 Poster: Language as an Abstraction for Hierarchical Deep Reinforcement Learning »
YiDing Jiang · Shixiang (Shane) Gu · Kevin Murphy · Chelsea Finn -
2019 Poster: Unsupervised learning of object structure and dynamics from videos »
Matthias Minderer · Chen Sun · Ruben Villegas · Forrester Cole · Kevin Murphy · Honglak Lee -
2018 Workshop: Relational Representation Learning »
Aditya Grover · Paroma Varma · Frederic Sala · Christopher Ré · Jennifer Neville · Stefano Ermon · Steven Holtzen -
2018 Workshop: Bayesian Deep Learning »
Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Andrew Wilson · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2018 Poster: Watch Your Step: Learning Node Embeddings via Graph Attention »
Sami Abu-El-Haija · Bryan Perozzi · Rami Al-Rfou · Alexander Alemi -
2018 Poster: Learning Compressed Transforms with Low Displacement Rank »
Anna Thomas · Albert Gu · Tri Dao · Atri Rudra · Christopher Ré -
2017 Workshop: Bayesian Deep Learning »
Yarin Gal · José Miguel Hernández-Lobato · Christos Louizos · Andrew Wilson · Andrew Wilson · Diederik Kingma · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2017 Workshop: Learning with Limited Labeled Data: Weak Supervision and Beyond »
Isabelle Augenstein · Stephen Bach · Eugene Belilovsky · Matthew Blaschko · Christoph Lampert · Edouard Oyallon · Emmanouil Antonios Platanios · Alexander Ratner · Christopher Ré -
2017 Workshop: ML Systems Workshop @ NIPS 2017 »
Aparna Lakshmiratan · Sarah Bird · Siddhartha Sen · Christopher Ré · Li Erran Li · Joseph Gonzalez · Daniel Crankshaw -
2017 Demonstration: Babble Labble: Learning from Natural Language Explanations »
Braden Hancock · Paroma Varma · Percy Liang · Christopher Ré · Stephanie Wang -
2017 Poster: Learning to Compose Domain-Specific Transformations for Data Augmentation »
Alexander Ratner · Henry Ehrenberg · Zeshan Hussain · Jared Dunnmon · Christopher Ré -
2017 Poster: Gaussian Quadrature for Kernel Features »
Tri Dao · Christopher M De Sa · Christopher Ré -
2017 Spotlight: Gaussian Quadrature for Kernel Features »
Tri Dao · Christopher M De Sa · Christopher Ré -
2017 Poster: Inferring Generative Model Structure with Static Analysis »
Paroma Varma · Bryan He · Payal Bajaj · Nishith Khandwala · Imon Banerjee · Daniel Rubin · Christopher Ré -
2016 : Invited Talk: You've been using asynchrony wrong your whole life! (Chris Re, Stanford) »
Christopher Ré -
2016 Workshop: Bayesian Deep Learning »
Yarin Gal · Christos Louizos · Zoubin Ghahramani · Kevin Murphy · Max Welling -
2016 Workshop: Advances in Approximate Bayesian Inference »
Tamara Broderick · Stephan Mandt · James McInerney · Dustin Tran · David Blei · Kevin Murphy · Andrew Gelman · Michael I Jordan -
2016 Poster: Cyclades: Conflict-free Asynchronous Machine Learning »
Xinghao Pan · Maximilian Lam · Stephen Tu · Dimitris Papailiopoulos · Ce Zhang · Michael Jordan · Kannan Ramchandran · Christopher Ré · Benjamin Recht -
2016 Poster: Sub-sampled Newton Methods with Non-uniform Sampling »
Peng Xu · Jiyan Yang · Farbod Roosta-Khorasani · Christopher Ré · Michael Mahoney -
2015 Poster: Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care »
Sorathan Chaturapruek · John Duchi · Christopher Ré -
2015 Poster: Bayesian dark knowledge »
Anoop Korattikara Balan · Vivek Rathod · Kevin Murphy · Max Welling -
2015 Poster: Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width »
Christopher M De Sa · Ce Zhang · Kunle Olukotun · Christopher Ré -
2015 Spotlight: Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width »
Christopher M De Sa · Ce Zhang · Kunle Olukotun · Christopher Ré · Christopher Ré -
2015 Poster: Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms »
Christopher M De Sa · Ce Zhang · Kunle Olukotun · Christopher Ré · Christopher Ré -
2014 Workshop: 4th Workshop on Automated Knowledge Base Construction (AKBC) »
Sameer Singh · Fabian M Suchanek · Sebastian Riedel · Partha Pratim Talukdar · Kevin Murphy · Christopher Ré · William Cohen · Tom Mitchell · Andrew McCallum · Jason Weston · Ramanathan Guha · Boyan Onyshkevych · Hoifung Poon · Oren Etzioni · Ari Kobren · Arvind Neelakantan · Peter Clark -
2014 Poster: Parallel Feature Selection Inspired by Group Testing »
Yingbo Zhou · Utkarsh Porwal · Ce Zhang · Hung Q Ngo · XuanLong Nguyen · Christopher Ré · Venu Govindaraju -
2013 Workshop: Big Learning : Advances in Algorithms and Data Management »
Xinghao Pan · Haijie Gu · Joseph Gonzalez · Sameer Singh · Yucheng Low · Joseph Hellerstein · Derek G Murray · Raghu Ramakrishnan · Michael Jordan · Christopher Ré