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ORCIDChristopher De Sa
Chris De Sa
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- affiliation: Stanford University
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2020 – today
- 2021
[c40]Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang:
Meta-Learning Divergences for Variational Inference. AISTATS 2021: 4024-4032- [c39]A. Feder Cooper, Karen Levy, Christopher De Sa:
Accuracy-Efficiency Trade-Offs and Accountability in Distributed ML Systems. EAAMO 2021: 4:1-4:11 - [c38]Johan Björck, Xiangyu Chen, Christopher De Sa, Carla P. Gomes, Kilian Q. Weinberger:
Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision. ICML 2021: 980-991 - [c37]Yucheng Lu, Christopher De Sa:
Optimal Complexity in Decentralized Training. ICML 2021: 7111-7123 - [c36]Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean Foster:
Variance Reduced Training with Stratified Sampling for Forecasting Models. ICML 2021: 7145-7155 - [i46]A. Feder Cooper, Yucheng Lu, Christopher De Sa:
Hyperparameter Optimization Is Deceiving Us, and How to Stop It. CoRR abs/2102.03034 (2021) - [i45]Johan Bjorck, Xiangyu Chen, Christopher De Sa, Carla P. Gomes, Kilian Q. Weinberger:
Low-Precision Reinforcement Learning. CoRR abs/2102.13565 (2021) - [i44]Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean Foster:
Variance Reduction in Training Forecasting Models with Subgroup Sampling. CoRR abs/2103.02062 (2021) - [i43]Jessica Zosa Forde, A. Feder Cooper, Kweku Kwegyir-Aggrey, Chris De Sa, Michael L. Littman:
Model Selection's Disparate Impact in Real-World Deep Learning Applications. CoRR abs/2104.00606 (2021) - [i42]Chengrun Yang, Ziyang Wu, Jerry Chee, Christopher De Sa, Madeleine Udell:
How Low Can We Go: Trading Memory for Error in Low-Precision Training. CoRR abs/2106.09686 (2021) - [i41]Isay Katsman, Aaron Lou, Derek Lim, Qingxuan Jiang, Ser-Nam Lim, Christopher De Sa:
Equivariant Manifold Flows. CoRR abs/2107.08596 (2021) - [i40]Jerry Chee, Megan Renz, Anil Damle, Chris De Sa:
Pruning Neural Networks with Interpolative Decompositions. CoRR abs/2108.00065 (2021) - [i39]A. Feder Cooper, Maria Antoniak, Christopher De Sa, Marilyn Migiel, David Mimno:
Tecnologica cosa: Modeling Storyteller Personalities in Boccaccio's Decameron. CoRR abs/2109.10506 (2021) - 2020
- [c35]Ruqi Zhang, A. Feder Cooper, Christopher De Sa:
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC. AISTATS 2020: 2142-2152 - [c34]Aaron Lou, Isay Katsman, Qingxuan Jiang, Serge J. Belongie, Ser-Nam Lim, Christopher De Sa:
Differentiating through the Fréchet Mean. ICML 2020: 6393-6403 - [c33]Yucheng Lu, Christopher De Sa:
Moniqua: Modulo Quantized Communication in Decentralized SGD. ICML 2020: 6415-6425 - [c32]Aaron Lou, Derek Lim, Isay Katsman, Leo Huang, Qingxuan Jiang, Ser-Nam Lim, Christopher De Sa:
Neural Manifold Ordinary Differential Equations. NeurIPS 2020 - [c31]Christopher De Sa:
Random Reshuffling is Not Always Better. NeurIPS 2020 - [c30]Ruqi Zhang, A. Feder Cooper, Christopher De Sa:
Asymptotically Optimal Exact Minibatch Metropolis-Hastings. NeurIPS 2020 - [i38]Yucheng Lu, Christopher De Sa:
Moniqua: Modulo Quantized Communication in Decentralized SGD. CoRR abs/2002.11787 (2020) - [i37]Ruqi Zhang, A. Feder Cooper, Christopher De Sa:
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC. CoRR abs/2003.00193 (2020) - [i36]Aaron Lou, Isay Katsman, Qingxuan Jiang, Serge J. Belongie, Ser-Nam Lim, Christopher De Sa:
Differentiating through the Fréchet Mean. CoRR abs/2003.00335 (2020) - [i35]Zhijing Li, Christopher De Sa, Adrian Sampson:
Optimizing JPEG Quantization for Classification Networks. CoRR abs/2003.02874 (2020) - [i34]Yucheng Lu, Jack Nash, Christopher De Sa:
MixML: A Unified Analysis of Weakly Consistent Parallel Learning. CoRR abs/2005.06706 (2020) - [i33]Yucheng Lu, Zheng Li, Christopher De Sa:
Towards Optimal Convergence Rate in Decentralized Stochastic Training. CoRR abs/2006.08085 (2020) - [i32]Aaron Lou, Derek Lim
, Isay Katsman, Leo Huang, Qingxuan Jiang, Ser-Nam Lim, Christopher De Sa:
Neural Manifold Ordinary Differential Equations. CoRR abs/2006.10254 (2020) - [i31]Ruqi Zhang, A. Feder Cooper, Christopher De Sa:
Asymptotically Optimal Exact Minibatch Metropolis-Hastings. CoRR abs/2006.11677 (2020) - [i30]A. Feder Cooper, Karen Levy, Christopher De Sa:
Regulating Accuracy-Efficiency Trade-Offs in Distributed Machine Learning Systems. CoRR abs/2007.02203 (2020) - [i29]Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang:
Meta-Learning for Variational Inference. CoRR abs/2007.02912 (2020) - [i28]Pedram Zamirai, Jian Zhang, Christopher R. Aberger, Christopher De Sa:
Revisiting BFloat16 Training. CoRR abs/2010.06192 (2020)
2010 – 2019
- 2019
- [j5]Ken Birman, Bharath Hariharan, Christopher De Sa:
Cloud-Hosted Intelligence for Real-time IoT Applications. ACM SIGOPS Oper. Syst. Rev. 53(1): 7-13 (2019) - [c29]Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Christopher De Sa, Zhiru Zhang:
Building Efficient Deep Neural Networks With Unitary Group Convolutions. CVPR 2019: 11303-11312 - [c28]Christopher De Sa, Ihab F. Ilyas, Benny Kimelfeld, Christopher Ré, Theodoros Rekatsinas:
A Formal Framework for Probabilistic Unclean Databases. ICDT 2019: 6:1-6:18 - [c27]Jayadev Acharya, Chris De Sa, Dylan J. Foster, Karthik Sridharan:
Distributed Learning with Sublinear Communication. ICML 2019: 40-50 - [c26]Tri Dao, Albert Gu, Alexander Ratner, Virginia Smith, Chris De Sa, Christopher Ré:
A Kernel Theory of Modern Data Augmentation. ICML 2019: 1528-1537 - [c25]Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, Christopher De Sa:
SWALP : Stochastic Weight Averaging in Low Precision Training. ICML 2019: 7015-7024 - [c24]Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Christopher De Sa, Zhiru Zhang:
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting. ICML 2019: 7543-7552 - [c23]Weizhe Hua, Yuan Zhou, Christopher De Sa, Zhiru Zhang, G. Edward Suh:
Boosting the Performance of CNN Accelerators with Dynamic Fine-Grained Channel Gating. MICRO 2019: 139-150 - [c22]Tianyi Zhang, Zhiqiu Lin, Guandao Yang, Christopher De Sa:
QPyTorch: A Low-Precision Arithmetic Simulation Framework. EMC2@NeurIPS 2019: 10-13 - [c21]Weizhe Hua, Yuan Zhou, Christopher De Sa, Zhiru Zhang, G. Edward Suh:
Channel Gating Neural Networks. NeurIPS 2019: 1884-1894 - [c20]Tao Yu, Christopher De Sa:
Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models. NeurIPS 2019: 2021-2031 - [c19]Ruqi Zhang, Christopher De Sa:
Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees. NeurIPS 2019: 4923-4932 - [c18]Zheng Li, Christopher De Sa:
Dimension-Free Bounds for Low-Precision Training. NeurIPS 2019: 11728-11738 - [i27]Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Christopher De Sa, Zhiru Zhang:
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting. CoRR abs/1901.09504 (2019) - [i26]Jayadev Acharya, Christopher De Sa, Dylan J. Foster, Karthik Sridharan:
Distributed Learning with Sublinear Communication. CoRR abs/1902.11259 (2019) - [i25]Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan R. Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i24]Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, Christopher De Sa:
SWALP : Stochastic Weight Averaging in Low-Precision Training. CoRR abs/1904.11943 (2019) - [i23]Tianyi Zhang, Zhiqiu Lin, Guandao Yang, Christopher De Sa:
QPyTorch: A Low-Precision Arithmetic Simulation Framework. CoRR abs/1910.04540 (2019) - [i22]Bowen Yang, Jian Zhang, Jonathan Li, Christopher Ré, Christopher R. Aberger, Christopher De Sa:
PipeMare: Asynchronous Pipeline Parallel DNN Training. CoRR abs/1910.05124 (2019) - [i21]Ritchie Zhao, Christopher De Sa, Zhiru Zhang:
Overwrite Quantization: Opportunistic Outlier Handling for Neural Network Accelerators. CoRR abs/1910.06909 (2019) - [i20]Ruqi Zhang, Christopher De Sa:
Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees. CoRR abs/1911.09771 (2019) - 2018
- [j4]Christopher De Sa, Robert F. Shepherd:
Soft optoelectronic sensory foams with proprioception. Sci. Robotics 3(24) (2018) - [c17]Peng Xu, Bryan D. He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré:
Accelerated Stochastic Power Iteration. AISTATS 2018: 58-67 - [c16]Christopher De Sa, Vincent Chen, Wing Wong:
Minibatch Gibbs Sampling on Large Graphical Models. ICML 2018: 1173-1181 - [c15]Frederic Sala, Christopher De Sa, Albert Gu, Christopher Ré:
Representation Tradeoffs for Hyperbolic Embeddings. ICML 2018: 4457-4466 - [c14]Dan Alistarh, Christopher De Sa, Nikola Konstantinov:
The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory. PODC 2018: 169-178 - [c13]Christopher De Sa, Albert Gu, Rohan Puttagunta, Christopher Ré, Atri Rudra:
A Two-pronged Progress in Structured Dense Matrix Vector Multiplication. SODA 2018: 1060-1079 - [i19]Christopher De Sa, Ihab F. Ilyas, Benny Kimelfeld, Christopher Ré, Theodoros Rekatsinas:
A Formal Framework For Probabilistic Unclean Databases. CoRR abs/1801.06750 (2018) - [i18]Christopher De Sa, Megan Leszczynski, Jian Zhang, Alana Marzoev, Christopher R. Aberger, Kunle Olukotun, Christopher Ré:
High-Accuracy Low-Precision Training. CoRR abs/1803.03383 (2018) - [i17]Tri Dao, Albert Gu, Alexander J. Ratner, Virginia Smith, Christopher De Sa, Christopher Ré:
A Kernel Theory of Modern Data Augmentation. CoRR abs/1803.06084 (2018) - [i16]Dan Alistarh, Christopher De Sa, Nikola Konstantinov:
The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory. CoRR abs/1803.08841 (2018) - [i15]Christopher De Sa, Albert Gu, Christopher Ré, Frederic Sala:
Representation Tradeoffs for Hyperbolic Embeddings. CoRR abs/1804.03329 (2018) - [i14]Weizhe Hua, Christopher De Sa, Zhiru Zhang, G. Edward Suh:
Channel Gating Neural Networks. CoRR abs/1805.12549 (2018) - [i13]Christopher De Sa, Vincent Chen, Wing Wong:
Minibatch Gibbs Sampling on Large Graphical Models. CoRR abs/1806.06086 (2018) - [i12]Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Christopher De Sa, Zhiru Zhang:
Building Efficient Deep Neural Networks with Unitary Group Convolutions. CoRR abs/1811.07755 (2018) - 2017
- [j3]Christopher De Sa, Alexander Ratner, Christopher Ré, Jaeho Shin, Feiran Wang, Sen Wu, Ce Zhang:
Incremental knowledge base construction using DeepDive. VLDB J. 26(1): 81-105 (2017) - [c12]Christopher De Sa, Kunle Olukotun, Christopher Ré:
Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling. IJCAI 2017: 4811-4815 - [c11]Christopher De Sa, Matthew Feldman, Christopher Ré, Kunle Olukotun:
Understanding and Optimizing Asynchronous Low-Precision Stochastic Gradient Descent. ISCA 2017: 561-574 - [c10]Tri Dao, Christopher De Sa, Christopher Ré:
Gaussian Quadrature for Kernel Features. NIPS 2017: 6107-6117 - [c9]Paroma Varma, Dan Iter, Christopher De Sa, Christopher Ré:
Flipper: A Systematic Approach to Debugging Training Sets. HILDA@SIGMOD 2017: 5:1-5:5 - [i11]Christopher De Sa, Bryan D. He, Ioannis Mitliagkas, Christopher Ré, Peng Xu:
Accelerated Stochastic Power Iteration. CoRR abs/1707.02670 (2017) - [i10]Tri Dao, Christopher De Sa, Christopher Ré:
Gaussian Quadrature for Kernel Features. CoRR abs/1709.02605 (2017) - 2016
- [j2]Christopher De Sa, Alexander Ratner, Christopher Ré, Jaeho Shin, Feiran Wang, Sen Wu, Ce Zhang:
DeepDive: Declarative Knowledge Base Construction. SIGMOD Rec. 45(1): 60-67 (2016) - [c8]Raghu Prabhakar, David Koeplinger, Kevin J. Brown, HyoukJoong Lee, Christopher De Sa, Christos Kozyrakis, Kunle Olukotun:
Generating Configurable Hardware from Parallel Patterns. ASPLOS 2016: 651-665 - [c7]Kevin J. Brown, HyoukJoong Lee, Tiark Rompf, Arvind K. Sujeeth, Christopher De Sa, Christopher R. Aberger, Kunle Olukotun:
Have abstraction and eat performance, too: optimized heterogeneous computing with parallel patterns. CGO 2016: 194-205 - [c6]Christopher De Sa, Christopher Ré, Kunle Olukotun:
Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling. ICML 2016: 1567-1576 - [c5]Bryan D. He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré:
Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much. NIPS 2016: 1-9 - [c4]Alexander J. Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, Christopher Ré:
Data Programming: Creating Large Training Sets, Quickly. NIPS 2016: 3567-3575 - [i9]Christopher De Sa, Kunle Olukotun, Christopher Ré:
Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling. CoRR abs/1602.07415 (2016) - [i8]Alexander Ratner, Christopher De Sa, Sen Wu, Daniel Selsam, Christopher Ré:
Data Programming: Creating Large Training Sets, Quickly. CoRR abs/1605.07723 (2016) - [i7]Bryan D. He, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré:
Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much. CoRR abs/1606.03432 (2016) - [i6]Jian Zhang, Christopher De Sa, Ioannis Mitliagkas, Christopher Ré:
Parallel SGD: When does averaging help? CoRR abs/1606.07365 (2016) - [i5]Rose Yu, Paroma Varma, Dan Iter, Christopher De Sa, Christopher Ré:
Socratic Learning. CoRR abs/1610.08123 (2016) - 2015
- [j1]Jaeho Shin, Sen Wu, Feiran Wang, Christopher De Sa, Ce Zhang, Christopher Ré:
Incremental Knowledge Base Construction Using DeepDive. Proc. VLDB Endow. 8(11): 1310-1321 (2015) - [c3]Christopher De Sa, Christopher Ré, Kunle Olukotun:
Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems. ICML 2015: 2332-2341 - [c2]Christopher De Sa, Ce Zhang, Kunle Olukotun, Christopher Ré:
Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms. NIPS 2015: 2674-2682 - [c1]Christopher De Sa, Ce Zhang, Kunle Olukotun, Christopher Ré:
Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width. NIPS 2015: 3097-3105 - [i4]Christopher De Sa, Ce Zhang, Kunle Olukotun, Christopher Ré:
Taming the Wild: A Unified Analysis of Hogwild!-Style Algorithms. CoRR abs/1506.06438 (2015) - [i3]Christopher De Sa, Ce Zhang, Kunle Olukotun, Christopher Ré:
Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width. CoRR abs/1510.00756 (2015) - [i2]Raghu Prabhakar, David Koeplinger, Kevin J. Brown, HyoukJoong Lee, Christopher De Sa, Christos Kozyrakis, Kunle Olukotun:
Generating Configurable Hardware from Parallel Patterns. CoRR abs/1511.06968 (2015) - 2014
- [i1]Christopher De Sa, Kunle Olukotun, Christopher Ré:
Global Convergence of Stochastic Gradient Descent for Some Nonconvex Matrix Problems. CoRR abs/1411.1134 (2014)
Coauthor Index
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