2020
Prior-guided Bayesian Optimization
Artur Souza, Luigi Nardi, Leonardo B. Oliveira, Kunle Olukotun, Marius Lindauer, Frank Hutter
Arxiv Preprint, 2020
2019
Lower Bounds for Locally Private Estimation via Communication Complexity
John Duchi, Ryan Rogers
arXiv preprint arXiv:1902.00582, 2019
Equivariant Transformer Networks
Kai Sheng Tai, Peter Bailis, Gregory Valiant
arXiv preprint arXiv:1901.11399, 2019
Learning from Dialogue after Deployment: Feed Yourself, Chatbot!
Braden Hancock, Antoine Bordes, Pierre-Emmanuel Mazare, Jason Weston
arXiv preprint arXiv:1901.05415, 2019
Medical device surveillance with electronic health records
Alison Callahan, Jason A Fries, Christopher Ré, James I, Huddleston III, Nicholas J Giori, Scott Delp, Nigam H Shah
arXiv Preprint, 2019
Low-Memory Neural Network Training: A Technical Report
Nimit Sohoni, Christopher Aberger, Megan Leszczynski, Jian Zhang, Christopher Ré
arXiv Preprint, 2019
DIFF: A Relational Interface for Large-Scale Data Explanation
Firas Abuzaid, Peter Kraft, Sahaana Suri, Edward Gan, Eric Xu, Atul Shenoy, Asvin Ananthanarayan, John Sheu, Erik Meijer, Xi Wu, Jeff Naughton, Peter Bailis, Matei Zaharia
VLDB 2019
A Kernel Theory of Modern Data Augmentation
Tri Dao, Albert Gu, Alexander J Ratner, Virginia Smith, Christopher De Sa, Christopher Ré
The International Conference on Machine Learning (ICML) 2019 [code]
Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations
Tri Dao, Albert Gu, Matthew Eichhorn, Atri Rudra, Christopher Ré
The International Conference on Machine Learning (ICML) 2019 [blog] [code]
Efficient Multiway Hash Join on Reconfigurable Hardware
Rekha Singhal, Yaqi Zhang, D. Jeffery Ullman, Raghu Proabhakar, Kunle Olukotun
Technology Conference on Performance Evaluation and Benchmarking 2019
Optimizing Data-Intensive Computations in Existing Libraries with Split Annotations
Shoumik Palkar, Matei Zaharia
Symposium on Operating System Principles (SOSP) 2019
PipeDream: Generalized Pipeline Parallelism for DNN Training
Deepak Narayanan, Aaron Harlap, Amar Phanishayee, Vivek Seshadri, Nikhil R. Devanur, Gregory R. Ganger, Phillip B. Gibbons, Matei Zaharia
Symposium on Operating System Principles (SOSP) 2019
Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark
Cody Coleman, Daniel Kang, Deepak Narayanan, Luigi Nardi, Tian Zhao, Jian Zhang, Peter Bailis, Kunle Olukotun, Chris Ré, Matei Zaharia
SIGOPS Oper. Syst. Rev., 2019
Stochastic (Approximate) Proximal Point Methods:
Convergence, Optimality, and Adaptivity
Hilal Asi, John C. Duchi
SIAM Journal on Optimization, 2019
Taurus: An Intelligent Data Plane
Tushar Swamy, Alexander Rucker, Muhammad Shahbaz, Kunle Olukotun
Proceedings of the P4 Workshop 2019 2019
DROP: A Workload-Aware Optimizer for Dimensionality Reduction
Sahaana Suri, Peter Bailis
Proceedings of the 3rd International Workshop on Data Management for End-to-End Machine Learning 2019
CrossTrainer: Practical Domain Adaptation with Loss Reweighting
Justin Chen, Edward Gan, Kexin Rong, Sahaana Suri, Peter Bailis
Proceedings of the 3rd International Workshop on Data Management for End-to-End Machine Learning 2019
Elastic RSS: Co-Scheduling Packets and Cores Using Programmable NICs
Alexander Rucker, Tushar Swamy, Muhammad Shahbaz, Kunle Olukotun
Proceedings of the 3rd Asia-Pacific Workshop on Networking 2019 2019
Rehashing Kernel Evaluation in High Dimensions
Paris Siminelakis, Kexin Rong, Peter Bailis, Moses Charikar, Philip Levis
Proceedings of the 36th International Conference on Machine Learning 2019
Beyond Data and Model Parallelism for Deep Neural Networks
Zhihao Jia, Matei Zaharia, Alex Aiken
Proceedings of the 2nd Conference on Systems and Machine Learning 2019
Optimizing DNN Computation with Relaxed Graph Substitutions
Zhihao Jia, James Thomas, Todd Warzawski, Mingyu Gao, Matei Zaharia, Alex Aiken
Proceedings of the 2nd Conference on Systems and Machine Learning 2019
Modeling simple structures and geometry for better stochastic optimization algorithms
Hilal Asi, John C. Duchi
Proceedings of the 22nd International Conference, n Artificial Intelligence and Statistics 2019
Serving Recurrent Neural Networks Efficiently with a Spatial Accelerator
Tian Zhao, Yaqi Zhang, Kunle Olukotun
Proceedings of the 2 nd SysML Conference 2019
Hyperbolic Graph Convolutional Neural Networks
Ines Chami, Rex Ying, Christopher Ré, Jure Leskovec
NeurIPS 2019
On the Downstream Performance of Compressed Word Embeddings
Avner May, Jian Zhang, Tri Dao, Christopher Ré
NeurIPS 2019 Spotlight. [code]
Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices
Vincent S Chen, Sen Wu, Zhenzhen Weng, Alexander Ratner, Christopher Ré
NeurIPS 2019
Multi-Resolution Weak Supervision for Sequential Data
Frederic Sala, Paroma Varma, Shiori Sagawa, Jason A Fries, Daniel Y Fu, Saelig Khattar, Ashwini Ramamoorthy, Ke Xiao, Kayvon Fatahalian, James R Priest, Christopher Ré
NeurIPS 2019
A machine-compiled database of genome-wide association studies
Volodymyr Kuleshov, Jialin Ding, Christopher Vo, Braden Hancock, Alexander Ratner, Yang Li, Christopher Ré, Serafim Batzoglou, Michael Snyder
Nature Communications, 2019 [code]
Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences
Jason A Fries, Paroma Varma, Vincent S Chen, Ke Xiao, Heliodoro Tejeda, Priyanka Saha, Jared Dunnmon, Henry Chubb, Shiraz Maskatia, Madalina Fiterau, others
Nature Communications, 2019
Unlabeled Data Improves Adversarial Robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, John Duchi
NIPS 2019
Doubly Weak Supervision of Deep Learning Models for Head CT
Khaled Saab, Jared Dunnmon, Roger Goldman, Alexander Ratner, Hersh Sagreiya, Christopher Ré, Daniel Rubin
Medical Image Computing and Computer Assisted Intervention 2019 Oral Spotlight.
Lower bounds for finding stationary points
II: First order methods
Yair Carmon, John C Duchi, Oliver Hinder, Sidford,
Aaron
Mathematical Programming, Series A, 2019
Lower bounds for finding stationary points
I
Yair Carmon, John C Duchi, Oliver Hinder, Sidford,
Aaron
Mathematical Programming, Series A, 2019
Improving Sample Complexity with Observational Supervision
Khaled Saab, Jared Dunnmon, Alexander Ratner, Daniel Rubin, Christopher Ré
2019
Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively Sensed Data
Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant
International Conference on Machine Learning 2019
Equivariant Transformer Networks
Kai Sheng Tai, Peter Bailis, Gregory Valiant
International Conference on Machine Learning 2019
LIT: Learned Intermediate Representation Training for Model Compression
Animesh Koratana, Daniel Kang, Peter Bailis, Matei Zaharia
International Conference on Machine Learning 2019
Learning Dependency Structures for Weak Supervision Models
Paroma Varma, Frederic Sala, Ann He, Alexander Ratner, Christopher Ré
International Conference on Machine Learning 2019 [blog]
Learning Mixed-Curvature Representations in Products of Model Spaces
Albert Gu, Frederic Sala, Beliz Gunel, Christopher Ré
International Conference on Learning Representations (ICLR 2019) 2019
TensorFlow to Cloud FPGAs: Tradeoffs for Accelerating Deep Neural Networks
Stefan Hadjis, Kunle Olukotun
In Proceedings of the 29th International Conference on Field Programmable Logic and Applications, FPL’19 2019
Practical Design Space Exploration
L. Nardi, D. Koeplinger, K. Olukotun
IEEE International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), 2019
To Index or Not to Index: Optimizing Exact Maximum Inner Product Search
Firas Abuzaid, Geet Sethi, Peter Bailis, Matei Zaharia
ICDE 2019
Scene Graph Prediction with Limited Labels
Vincent S Chen, Paroma Varma, Ranjay Krishna, Michael Bernstein, Christopher Ré, Li Fei-Fei
ICCV 2019
The Role of Massively Multi-Task and Weak Supervision in Software 2.0
Alex Ratner, Braden Hancock, Christopher Ré
Conference on Innovative Data Systems Research (CIDR 2019) 2019
Selection Via Proxy: Efficient Data Selection For Deep Learning
Cody Coleman and
Christopher Yeh and
Stephen Mussmann and
Baharan Mirzasoleiman and
Peter Bailis and
Percy Liang and
Jure Leskovec and
Matei Zaharia
CoRR, 2019
Lower Bounds for Locally Private Estimation via Communication Complexity
John C. Duchi, Ryan Rogers
COLT 2019
A Rank-1 Sketch for Matrix Multiplicative Weights
Yair Carmon, John Duchi, Aaron Sidford, Kevin Tian
COLT 2019
Challenges and Opportunities in DNN-Based Video Analytics: A Demonstration of the BlazeIt Video Query Engine
Daniel Kang, Peter Bailis, Matei Zaharia
CIDR 2019
Learning from Dialogue after Deployment: Feed Yourself, Chatbot!
Braden Hancock, Antoine Bordes, Pierre-Emmanuel Mazaré, Jason Weston
Association for Computational Linguistics (ACL) 2019
Snorkel DryBell: A Case Study in Deploying Weak Supervision at Industrial Scale
Stephen H. Bach, Daniel Rodriguez, Yintao Liu, Chong Luo, Haidong Shao, Cassandra Xia, Souvik Sen, Alexander Ratner, Braden Hancock, Houman Alborzi, Rahul Kuchhal, Christopher Ré, Rob Malkin
Arxiv 2019
Asymptotic Optimality in Stochastic Optimization
John C. Duchi, Feng Ruan
AOS, 2019
Low-Precision Random Fourier Features for Memory-Constrained Kernel Approximation
Jian Zhang, Avner May, Tri Dao, Christopher Ré
AISTATS 2019, 2019
Automating the generation of hardware component knowledge bases
Luke Hsiao, Sen Wu, Nicholas Chiang, Christopher Ré, Philip Levis
ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems 2019
Training Complex Models with Multi-Task Weak Supervision
Alex Ratner, Braden Hancock, Jared Dunnmon, Frederic Sala, Shreyash Pandey, Christopher Ré
AAAI Conference on Artificial Intelligence (AAAI-19) 2019
Scalable Interconnects for Reconfigurable Spatial Architectures
Yaqi Zhang, Alexandar Rucker, Matthew Villim, Raghu Prabhakar, Kunle Olukotun
2019 ACM/IEEE 46th Annual International Symposium on Computer Architecture (ISCA) 2019
2018
Protection Against Reconstruction and Its Applications in Private Federated Learning
Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, Ryan Rogers
arXiv preprint arXiv:1812.00984, 2018
Splitability Annotations: Optimizing Black-Box Function Composition in Existing Libraries
Shoumik Palkar, Matei Zaharia
arXiv preprint arXiv:1810.12297, 2018
Stochastic (Approximate) Proximal Point Methods: Convergence, Optimality, and Adaptivity
Hilal Asi, John C Duchi
arXiv preprint arXiv:1810.05633, 2018
The right complexity measure in locally private estimation: It is not the Fisher information
John C Duchi, Feng Ruan
arXiv preprint arXiv:1806.05756, 2018
A Formal Framework For Probabilistic Unclean Databases
Christopher De Sa, Ihab F Ilyas, Benny Kimelfeld, Christopher Re, Theodoros Rekatsinas
arXiv Preprint, 2018
Evaluating End-to-end Optimization for Data Analytics Applications in Weld
Shoumik Palkar, James Thomas, Deepak Narayanan, Pratiksha Thaker, Rahul Palamuttam, Parimajan Negi, Anil Shanbhag, Malte Schwarzkopf, Holger Pirk, Saman Amarasinghe, Samuel Madden, Matei Zaharia
VLDB, 2018 [blog]
Filter Before You Parse: Faster Analytics on Raw Data with Sparser
Shoumik Palkar, Firas Abuzaid, Peter Bailis, Matei Zaharia
VLDB, 2018 [blog]
Locality-sensitive Hashing for Earthquake Detection: A Case Study of Scaling Data-driven Science
Kexin Rong, Clara Yoon, Karianne Bergen, Hashem Elezabi, Peter Bailis, Philip Levis, Gregory Beroza
VLDB, 2018 [blog]
Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries
Edward Gan, Jialin Ding, Kai Sheng Tai, Vatsal Sharan, Peter Bailis
VLDB, 2018 [blog]
Snorkel: Rapid Training Data Creation with Weak Supervision
Alexander Ratner, Stephen Bach, Henry Ehrenberg, Jason Fries, Sen Wu, Christopher Ré
VLDB 2018 [blog]
Efficient Mergeable Quantile Sketches using Moments
Edward Gan, Jialin Ding, Peter Bailis
SysML 2018 Poster.
PipeDream: Pipeline Parallelism for DNN Training
Aaron Harlap, Deepak Narayanan, Amar Phanishayee, Vivek Seshadri, Gregory Ganger, Phillip Gibbons
SysML 2018 Poster.
Accelerating Model Search with Model Batching
Deepak Narayanan, Keshav Santhanam, Matei Zaharia
SysML 2018 Poster.
BlazeIt: An Optimizing Query Engine for Video at Scale
Daniel Kang, Peter Bailis, Matei Zaharia
SysML 2018 Poster.
A Two-pronged Progress in Structured Dense Matrix Vector Multiplication
Christopher De Sa, Albert Cu, Rohan Puttagunta, Christopher Ré, Atri Rudra
SODA 2018
Sketching Linear Classifiers on Data Streams
Kai Sheng Tai, Vatsal Sharan, Peter Bailis, Gregory Valiant
SIGMOD 2018 [blog]
Fonduer: Knowledge Base Construction from Richly Formatted Data
Sen Wu, Luke Hsiao, Xiao Cheng, Braden Hancock, Theodoros Rekatsinas, Philip Levis, Christopher Ré
SIGMOD 2018 [blog]
Assessment of convolutional neural networks for automated classification of chest radiographs
Jared A Dunnmon, Darvin Yi, Curtis P Langlotz, Christopher Ré, Daniel L Rubin, Matthew P Lungren
Radiology, 2018
Snuba: automating weak supervision to label training data
Paroma Varma, Christopher Ré
Proceedings of the VLDB Endowment, 2018
Snorkel MeTaL: Weak Supervision for Multi-Task Learning
Alex Ratner, Braden Hancock, Jared Dunnmon, Roger Goldman, Christopher Ré
Proceedings of the Second Workshop on Data Management for End-To-End Machine Learning, 2018
High-Accuracy Low-Precision Training
Christopher R Aberger, Christopher De Sa, Megan Leszczynski, Alana Marzoev, Kunle Olukotun, Christopher Ré, Jian Zhang
Preprint, 2018 [blog]
Spatial: A Language and Compiler for Application Accelerators
David Koeplinger, Matthew Feldman, Raghu Prabhakar, Yaqi Zhang, Stefan Hadjis, Ruben Fiszel, Tian Zhao, Luigi Nardi, Ardavan Pedram, Christos Kozyrakis, Kunle Olukotun
PLDI 2018
Accelerating Model Search with Model Batching
Deepak Narayanan, Keshav Santhanam, Amar Phanishayee, Matei Zaharia
NeurIPS MLSys Workshop 2018
Model assertions for debugging machine learning
Daniel Kang, Deepti Raghavan, Peter Bailis, Matei Zaharia
NeurIPS MLSys Workshop 2018
Exploring the Use of Learning Algorithms for Efficient Performance Profiling
Shoumik Palkar, Sahaana, Suri, Peter Bailis, Matei Zaharia
ML for Systems Workshop at NeurIPS 2018, 2018
Systematic Protein Prioritization for Targeted Proteomics Studies through Literature Mining
Kun-Hsing Yu, TSUNG-LU LEE, Chi-Shiang Wang, Yu-Ju Chen, Christopher Ré, Samuel C Kou, Jung-Hsien Chiang, Isaac S Kohane, Michael Snyder
Journal of Proteome Research, 2018
Efficient Octree-Based Volumetric SLAM Supporting Signed-Distance and Occupancy Mapping
Emanuele Vespa, Nikolay Nikolov, Marius Grimm, Luigi Nardi, Paul HJ Kelly, Stefan Leutenegger
IEEE Robotics and Automation Letters, 2018
SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM
Bruno Bodin, Harry Wagstaff, Sajad Saeedi, Luigi Nardi, Emanuele Vespa, John H Mayer, Andy Nisbet, Mikel Luján, Steve Furber, Andrew J Davison, Paul H.J. Kelly, Michael O’Boyle
ICRA 2018
Representation Tradeoffs for Hyperbolic Embeddings
Frederic Sala, Chris De Sa, Albert Gu, Christopher Ré
ICML, 2018
Learning Invariance with Compact Transforms
Anna Thomas, Albert Gu, Tri Dao, Atri Rudra, Christopher Re
ICLR Workshop Track, 2018
LevelHeaded: A Unified Engine for Business Intelligence and Linear Algebra Querying
Christopher R. Aberger, Andrew Lamb, Kunle Olukotun, Christopher Ré
ICDE 2018
Exploring the Utility of Developer Exhaust
Jian Zhang, Max Lam, Stephanie Wang, Paroma Varma, Luigi Nardi, Kunle Olukotun, Christopher Re
DEEM’18, 2018
Title Generation for Web Tables
Braden Hancock, Hongrae Lee, Cong Yu
Arxiv Preprint, 2018
Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems
Yair Carmon, John C Duchi
Advances in Neural Information Processing Systems 2018
Accelerated stochastic power iteration
Christopher De Sa, Bryan He, Ioannis Mitliagkas, Christopher Ré, Peng Xu
AI Stats, 2018 [blog]
Training Classifiers with Natural Language Explanations
Braden Hancock, Paroma Varma, Stephanie Wang, Martin Bringmann, Percy Liang, Christopher Ré
ACL, 2018
2017
YellowFin and the Art of Momentum Tuning
Jian Zhang, Ioannis Mitliagkas, Christopher Ré
{AutoML Workshop at ICML} 2017 [blog] [code]
AMELIE accelerates Mendelian patient diagnosis directly from the primary literature
Johannes Birgmeier, Maximilian Haeussler, Cole A Deisseroth, Karthik A Jagadeesh, Alexander J Ratner, Harendra Guturu, Aaron M Wenger, Peter D Stenson, David N Cooper, Christopher Re, others
bioRxiv, 2017
ASAP: Prioritizing Attention via Time Series Smoothing
Kexin Rong, Peter Bailis
VLDB 2017 [demo] [blog] [talk] [slides] [code]
NoScope: Optimizing Neural Network Queries over Video at Scale
Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, Matei Zaharia
VLDB 2017 [blog] [slides] [code]
HoloClean: Holistic Data Repairs with Probabilistic Inference
Theodoros Rekatsinas, Xu Chu, Ihab F Ilyas, Christopher Ré
VLDB 2017 [blog]
Mind the Gap: Bridging Multi-Domain Workloads with EmptyHeaded
Christopher R. Aberger, Andrew Lamb, Kunle Olukotun, Christopher Ré
VLDB 2017 (Demo)
DAWNBench: An End-to-End Deep Learning Benchmark and Competition
Cody Coleman, Deepak Narayanan, Daniel Kang, Tian Zhao, Jian Zhang, Luigi Nardi, Peter Bailis, Kunle Olukotun, Chris Ré, Matei Zaharia
SOSP MLSys Workshop, 2017
Stadium: A Distributed Metadata-Private Messaging System
Nirvan Tyagi, Yossi Gilad, Derek Leung, Matei Zaharia, Nickolai Zeldovich
SOSP 2017
SLiMFast: Guaranteed Results for Data Fusion and Source Reliability
Manas Joglekar, Theodoros Rekatsinas, Hector Garcia-Molina, Aditya Parameswaran, Christopher Ré
SIGMOD, 2017 [blog]
Scalable Kernel Density Classification via Threshold-Based Pruning
{Edward Gan, Peter Bailis}
SIGMOD 2017 [talk] [slides] [code]
Demonstration: MacroBase, A Fast Data Analysis Engine
{Peter Bailis, Edward Gan, and Kexin Rong, Sahaana Suri}
SIGMOD 2017 (Demo)
MacroBase: Prioritizing Attention in Fast Data
{Peter Bailis, Edward Gan, Samuel Madden, Deepak Narayanan, Kexin Rong, Sahaana Suri}
SIGMOD 2017 Awarded “Best of SIGMOD 2017”. [code]
Snorkel: Fast Training Set Generation for Information Extraction
Alexander J. Ratner, Stephen H. Bach, Henry R. Ehrenberg, Christopher Ré
SIGMOD 2017 (Demo) [code] [coverage]
Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data
Thorsten Kurth, Jian Zhang, Nadathur Satish, Ioannis Mitliagkas, Evan Racah, Mostofa Ali Patwary, Tareq Malas, Narayanan Sundaram, Wahid Bhimji, Mikhail Smorkalov, Jack Deslippe, Mikhail Shiryaev, Srinivas Sridharan, Prabhat, Pradeep Dubey
SC 2017
ShortFuse: Biomedical Time Series Representations in the Presence of Structured Information
Madalina Fiterau, Suvrat Bhooshan, Jason Fries, Charles Bournhonesque, Jennifer Hicks, Eni Halilaj, Christopher Ré, Scott Delp
Proceedings of the Machine Learning in Healthcare Conference 2017 [slides]
Flipper: A Systematic Approach to Debugging Training Sets
Paroma Varma, Dan Iter, Christopher De Sa, Christopher Ré
Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics 2017
DIY Hosting for Online Privacy
Shoumik Palkar, Matei Zaharia
Proceedings of the 16th ACM Workshop on Hot Topics in Networks 2017
A Relational Framework for Classifier Engineering
Benny Kimelfeld, Christopher Ré
PODS 2017 Best of PODS 2017.
Splinter: Practical Private Queries on Public Data
Frank Wang, Catherine Yun, Shafi Goldwasser, Vinod Vaikuntanathan, Matei Zaharia
NSDI 2017 [coverage]
Cross-Modal Data Programming for Medical Images
Nishith Khandwala, Alexander Ratner, Jared Dunnmon, Roger Goldman, Matt Lungren, Daniel Rubiun, Christopher Ré
NIPS ML4H Workshop 2017
Automatic Training Set Generation for Aortic Valve Classification
Vincent Chen, Paroma Varma, Madalina Fiterau, Seung-Pyo Lee, James Priest, Christopher Ré
NIPS ML4H Workshop 2017
Generating Training Labels for Cardiac Phase-Contrast MRI Images
Vincent Chen, Paroma Varma, Madalina Fiterau, Seung-Pyo Lee, James Priest, Christopher Ré
NIPS ML4H Workshop 2017
Babble Labble: Learning from Natural Language Explanations
Braden Hancock, Stephanie Wang, Paroma Varma, Percy Liang, Christopher Ré
NIPS Demonstration 2017 [blog]
Inferring Generative Model Structure with Static Analysis
Paroma Varma, Bryan He, Payal Bajaj, Imon Banerjee, Nishith Khandwala, Daniel L. Rubin Rubin, Christopher Ré
NIPS 2017
Learning to Compose Domain-Specific Transformations for Data Augmentation
Alexander J. Ratner, Henry R. Ehrenberg, Zeshan, Hussain, Jared Dunmon, Christopher Ré
NIPS 2017 [blog] [code]
Gaussian Quadrature for Kernel Features
Tri Dao, Chris De Sa, Christopher Ré
NIPS 2017 Spotlight.
DAWNBench: An End-to-End Deep Learning Benchmark and Competition
Cody Coleman, Deepak Narayanan, Daniel Kang, Tian Zhao, Jian Zhang, Luigi Nardi, Peter Bailis, Kunle Olukotun, Chris R'e, Matei Zaharia
ML System Workshops at NIPS 2017
Learning the structure of generative models without labeled data
Stephen H Bach, Bryan He, Alexander Ratner, Christopher Ré
International Conference on Machine Learning 2017 [blog]
Understanding and optimizing asynchronous low-precision stochastic gradient descent
Christopher De Sa, Matthew Feldman, Christopher Ré, Kunle Olukotun
ISCA 2017
Plasticine: A Reconfigurable Architecture For Parallel Patterns
David Koeplinger, Raghu Prabhakar, Yaqi Zhang, Matt Feldman, Tian Zhao, Stefan Hadjis, Christos Kozyrakis, Kunle Olukotun
ISCA 2017
Learning the Structure of Generative Models without Labeled Data
Bryan He, Christopher M De Sa, Ioannis Mitliagkas, Christopher Ré
ICML 2017
GYM: A multiround join algorithm in mapreduce
Foto Afrati, Manas Joglekar, Christopher Ré, Semih Salihoglu, Jeffrey D Ullman
ICDT, 2017
Association of Omics Features with Histopathology Patterns in Lung Adenocarcinoma
Kun-Hsing Yu, Gerald J Berry, Daniel L Rubin, Christopher Ré, Russ B Altman, Michael Snyder
Cell systems, 2017
Snorkel: A System for Lightweight Extraction.
Alexander Ratner, Stephen H Bach, Henry R Ehrenberg, Jason Alan Fries, Sen Wu, Christopher Ré
CIDR 2017
Prioritizing Attention in Fast Data: Principles and Promise
Peter {Bailis, Edward Gan, Kexin Rong, Sahaana} Suri
CIDR 2017
Weld: A Common Runtime for High Performance Data Analytics
Shoumik Palkar, James J Thomas, Anil Shanbhag, Deepak Narayanan, Holger Pirk, Malte Schwarzkopf, Saman Amarasinghe, Matei Zaharia
CIDR 2017 [code]
There and Back Again: A General Approach to Learning Sparse Models
Vatsal Sharan, Kai Sheng Tai, Peter Bailis, Gregory Valiant
Arxiv Preprint, 2017
Report from the third workshop on Algorithms and Systems for MapReduce and Beyond (BeyondMR’16)
Foto N Afrati, Jan Hidders, Christopher Ré, Jacek Sroka, Jeffrey Ullman
ACM SIGMOD Record, 2017
2016
Emptyheaded: A relational engine for graph processing
Christopher R Aberger, Susan Tu, Kunle Olukotun, Christopher Ré
SIGMOD 2016 Awarded “Best of SIGMOD 2016”. [slides]
Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features
Kun-Hsing Yu, Ce Zhang, Gerald J Berry, Russ B Altman, Christopher Ré, Daniel L Rubin, Michael Snyder
Nature Communications, 2016 [coverage]
Data Programming: Creating Large Training Sets, Quickly
Alexander J. Ratner, Christopher M. De Sa, Sen Wu, Daniel Selsam, Christopher Ré
NIPS 2016 [blog] [talk] [code] [coverage]
Scan order in Gibbs sampling: Models in which it matters and bounds on how much
Bryan He, Christopher M. De Sa, Ioannis Mitliagkas, Christopher Ré
NIPS 2016
Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale
Firas Abuzaid, Joseph K Bradley, Feynman T Liang, Andrew Feng, Lee Yang, Matei Zaharia, Ameet S Talwalkar
NIPS 2016 [talk] [slides] [code]
Automatic generation of efficient accelerators for reconfigurable hardware
David Koeplinger, Christina Delimitrou, Raghu Prabhakar, Christos Kozyrakis, Yaqi Zhang, Kunle Olukotun
ISCA 2016 [slides]
Ensuring rapid mixing and low bias for asynchronous Gibbs sampling
Christopher De Sa, Kunle Olukotun, Christopher Ré
ICML 2016 Best Paper Award. [slides]
Old Techniques for New Join Algorithms: A Case Study in RDF Processing
Christopher R. Aberger, Susan Tu, Kunle Olukotun, Christopher Ré
ICDE Workshops 2016 [code]
Have abstraction and eat performance, too: Optimized heterogeneous computing with parallel patterns
Kevin J Brown, HyoukJoong Lee, Tiark Rompf, Arvind K Sujeeth, Christopher De Sa, Christopher Aberger, Kunle Olukotun
CGO 2016
Asynchrony begets momentum, with an application to deep learning
Ioannis Mitliagkas, Ce Zhang, Stefan Hadjis, Christopher Ré
Allerton 2016 [blog]
Generating configurable hardware from parallel patterns
Raghu Prabhakar, David Koeplinger, Kevin J Brown, HyoukJoong Lee, Christopher De Sa, Christos Kozyrakis, Kunle Olukotun
ASPLOS 2016