Christopher De Sa
Christopher De Sa
Assistant Professor of Computer Science, Cornell University
Verified email at cs.cornell.edu - Homepage
TitleCited byYear
Data programming: Creating large training sets, quickly
AJ Ratner, CM De Sa, S Wu, D Selsam, C Ré
Advances in neural information processing systems, 3567-3575, 2016
1842016
Incremental knowledge base construction using DeepDive
J Shin, S Wu, F Wang, C De Sa, C Zhang, C Ré
Proceedings of the VLDB Endowment 8 (11), 1310-1321, 2015
1672015
Global convergence of stochastic gradient descent for some non-convex matrix problems
C De Sa, K Olukotun, C Ré
arXiv preprint arXiv:1411.1134, 2014
1292014
Taming the wild: A unified analysis of hogwild-style algorithms
CM De Sa, C Zhang, K Olukotun, C Ré
Advances in neural information processing systems, 2674-2682, 2015
932015
Understanding and optimizing asynchronous low-precision stochastic gradient descent
C De Sa, M Feldman, C Ré, K Olukotun
ACM SIGARCH Computer Architecture News 45 (2), 561-574, 2017
642017
Representation tradeoffs for hyperbolic embeddings
C De Sa, A Gu, C Ré, F Sala
Proceedings of machine learning research 80, 4460, 2018
472018
Generating configurable hardware from parallel patterns
R Prabhakar, D Koeplinger, KJ Brown, HJ Lee, C De Sa, C Kozyrakis, ...
ACM SIGARCH Computer Architecture News 44 (2), 651-665, 2016
432016
Have abstraction and eat performance, too: Optimized heterogeneous computing with parallel patterns
KJ Brown, HJ Lee, T Romp, AK Sujeeth, C De Sa, C Aberger, K Olukotun
2016 IEEE/ACM International Symposium on Code Generation and Optimization …, 2016
392016
High-accuracy low-precision training
C De Sa, M Leszczynski, J Zhang, A Marzoev, CR Aberger, K Olukotun, ...
arXiv preprint arXiv:1803.03383, 2018
352018
Deepdive: Declarative knowledge base construction
C De Sa, A Ratner, C Ré, J Shin, F Wang, S Wu, C Zhang
ACM SIGMOD Record 45 (1), 60-67, 2016
352016
Ensuring rapid mixing and low bias for asynchronous Gibbs sampling
C De Sa, K Olukotun, C Ré
JMLR workshop and conference proceedings 48, 1567, 2016
312016
DeepDive: declarative knowledge base construction
C Zhang, C Ré, M Cafarella, C De Sa, A Ratner, J Shin, F Wang, S Wu
Communications of the ACM 60 (5), 93-102, 2017
282017
Parallel SGD: When does averaging help?
J Zhang, C De Sa, I Mitliagkas, C Ré
arXiv preprint arXiv:1606.07365, 2016
242016
Accelerated stochastic power iteration
C De Sa, B He, I Mitliagkas, C Ré, P Xu
Proceedings of machine learning research 84, 58, 2018
182018
Incremental knowledge base construction using DeepDive
C Sa, A Ratner, C Ré, J Shin, F Wang, S Wu, C Zhang
The VLDB Journal—The International Journal on Very Large Data Bases 26 (1 …, 2017
142017
Socratic learning: Augmenting generative models to incorporate latent subsets in training data
P Varma, B He, D Iter, P Xu, R Yu, C De Sa, C Ré
arXiv preprint arXiv:1610.08123, 2016
132016
Scan order in Gibbs sampling: Models in which it matters and bounds on how much
BD He, CM De Sa, I Mitliagkas, C Ré
Advances in neural information processing systems, 1-9, 2016
132016
Gaussian quadrature for kernel features
T Dao, CM De Sa, C Ré
Advances in neural information processing systems, 6107-6117, 2017
122017
Rapidly mixing Gibbs sampling for a class of factor graphs using hierarchy width
CM De Sa, C Zhang, K Olukotun, C Ré
Advances in neural information processing systems, 3097-3105, 2015
122015
Flipper: A systematic approach to debugging training sets
P Varma, D Iter, C De Sa, C Ré
Proceedings of the 2nd Workshop on Human-in-the-Loop Data Analytics, 5, 2017
112017
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