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ORCIDChristopher De Sa
Chris De Sa
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- affiliation: Stanford University
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2020 – today
- 2025
[c72]Marc Anton Finzi, Sanyam Kapoor, Diego Granziol, Anming Gu, Christopher De Sa, J. Zico Kolter, Andrew Gordon Wilson:
Compute-Optimal LLMs Provably Generalize Better with Scale. ICLR 2025- [c71]Wentao Guo, Jikai Long, Yimeng Zeng, Zirui Liu, Xinyu Yang, Yide Ran, Jacob R. Gardner, Osbert Bastani, Christopher De Sa, Xiaodong Yu, Beidi Chen, Zhaozhuo Xu:
Zeroth-Order Fine-Tuning of LLMs with Transferable Static Sparsity. ICLR 2025 - [i78]Marc Finzi, Sanyam Kapoor, Diego Granziol, Anming Gu, Christopher De Sa, J. Zico Kolter, Andrew Gordon Wilson:
Compute-Optimal LLMs Provably Generalize Better With Scale. CoRR abs/2504.15208 (2025) - [i77]A. Feder Cooper, Aaron Gokaslan, Amy Cyphert, Christopher De Sa, Mark A. Lemley, Daniel E. Ho, Percy Liang:
Extracting memorized pieces of (copyrighted) books from open-weight language models. CoRR abs/2505.12546 (2025) - [i76]Albert Tseng, Zhaofeng Sun, Christopher De Sa:
Model-Preserving Adaptive Rounding. CoRR abs/2505.22988 (2025) - 2024
- [j7]Junjie Yin, Jiahao Dong, Yingheng Wang, Christopher De Sa, Volodymyr Kuleshov:
ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers. Trans. Mach. Learn. Res. 2024 (2024) - [c70]A. Feder Cooper, Katherine Lee, Madiha Zahrah Choksi, Solon Barocas, Christopher De Sa, James Grimmelmann, Jon M. Kleinberg, Siddhartha Sen, Baobao Zhang:
Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification. AAAI 2024: 22004-22012 - [c69]Tao Yu, Toni J. B. Liu, Albert Tseng, Christopher De Sa:
Shadow Cones: A Generalized Framework for Partial Order Embeddings. ICLR 2024 - [c68]Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, Christopher De Sa:
QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks. ICML 2024 - [c67]Andres Potapczynski, Shikai Qiu, Marc Finzi, Christopher Ferri, Charlie Chen, Micah Goldblum, C. Bayan Bruss, Christopher De Sa, Andrew Gordon Wilson:
Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices. NeurIPS 2024 - [c66]Subham S. Sahoo, Aaron Gokaslan, Christopher De Sa, Volodymyr Kuleshov:
Diffusion Models With Learned Adaptive Noise. NeurIPS 2024 - [c65]Albert Tseng, Qingyao Sun, David Hou, Christopher De Sa:
QTIP: Quantization with Trellises and Incoherence Processing. NeurIPS 2024 - [e1]Phillip B. Gibbons, Gennady Pekhimenko, Christopher De Sa:
Proceedings of the Seventh Annual Conference on Machine Learning and Systems, MLSys 2024, Santa Clara, CA, USA, May 13-16, 2024. mlsys.org 2024 [contents] - [i75]Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, Christopher De Sa:
QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks. CoRR abs/2402.04396 (2024) - [i74]Megan Flynn, Alexander Wang, Dean Edward Alvarez, Christopher De Sa, Anil Damle:
STAT: Shrinking Transformers After Training. CoRR abs/2406.00061 (2024) - [i73]Wentao Guo, Jikai Long, Yimeng Zeng, Zirui Liu, Xinyu Yang, Yide Ran, Jacob R. Gardner, Osbert Bastani, Christopher De Sa, Xiaodong Yu, Beidi Chen, Zhaozhuo Xu:
Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity. CoRR abs/2406.02913 (2024) - [i72]Si Yi Meng, Antonio Orvieto, Daniel Yiming Cao, Christopher De Sa:
Gradient Descent on Logistic Regression with Non-Separable Data and Large Step Sizes. CoRR abs/2406.05033 (2024) - [i71]Albert Tseng, Qingyao Sun, David Hou, Christopher De Sa:
QTIP: Quantization with Trellises and Incoherence Processing. CoRR abs/2406.11235 (2024) - [i70]Andres Potapczynski, Shikai Qiu, Marc Finzi, Christopher Ferri, Zixi Chen, Micah Goldblum, C. Bayan Bruss, Christopher De Sa, Andrew Gordon Wilson:
Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices. CoRR abs/2410.02117 (2024) - [i69]A. Feder Cooper, Christopher A. Choquette-Choo, Miranda Bogen, Matthew Jagielski, Katja Filippova, Ken Ziyu Liu, Alexandra Chouldechova, Jamie Hayes, Yangsibo Huang, Niloofar Mireshghallah, Ilia Shumailov, Eleni Triantafillou, Peter Kairouz, Nicole Mitchell, Percy Liang, Daniel E. Ho, Yejin Choi, Sanmi Koyejo, Fernando A. Delgado, James Grimmelmann, Vitaly Shmatikov, Christopher De Sa, Solon Barocas, Amy Cyphert, Mark A. Lemley, danah boyd, Jennifer Wortman Vaughan, Miles Brundage, David Bau, Seth Neel, Abigail Z. Jacobs, Andreas Terzis, Hanna M. Wallach, Nicolas Papernot, Katherine Lee:
Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy, Research, and Practice. CoRR abs/2412.06966 (2024) - 2023
- [j6]Yucheng Lu, Christopher De Sa:
Decentralized Learning: Theoretical Optimality and Practical Improvements. J. Mach. Learn. Res. 24: 93:1-93:62 (2023) - [c64]Yucheng Lu, Conglong Li, Minjia Zhang, Christopher De Sa, Yuxiong He:
Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam. ICLR 2023 - [c63]Tao Yu, Christopher De Sa:
Random Laplacian Features for Learning with Hyperbolic Space. ICLR 2023 - [c62]Yucheng Lu, Shivani Agrawal, Suvinay Subramanian, Oleg Rybakov, Christopher De Sa, Amir Yazdanbakhsh:
STEP: Learning N: M Structured Sparsity Masks from Scratch with Precondition. ICML 2023: 22812-22824 - [c61]Jue Wang, Yucheng Lu, Binhang Yuan, Beidi Chen, Percy Liang, Christopher De Sa, Christopher Ré, Ce Zhang:
CocktailSGD: Fine-tuning Foundation Models over 500Mbps Networks. ICML 2023: 36058-36076 - [c60]Yingheng Wang, Yair Schiff, Aaron Gokaslan, Weishen Pan, Fei Wang, Christopher De Sa, Volodymyr Kuleshov:
InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models. ICML 2023: 36336-36354 - [c59]Kush Bhatia, Avanika Narayan, Christopher De Sa, Christopher Ré:
TART: A plug-and-play Transformer module for task-agnostic reasoning. NeurIPS 2023 - [c58]Jerry Chee, Yaohui Cai, Volodymyr Kuleshov, Christopher De Sa:
QuIP: 2-Bit Quantization of Large Language Models With Guarantees. NeurIPS 2023 - [c57]A. Feder Cooper, Wentao Guo, Khiem Pham, Tiancheng Yuan, Charlie Ruan, Yucheng Lu, Christopher De Sa:
CD-GraB: Coordinating Distributed Example Orders for Provably Accelerated Training. NeurIPS 2023 - [c56]Isay Katsman, Eric Ming Chen, Sidhanth Holalkere, Anna Asch, Aaron Lou, Ser Nam Lim, Christopher De Sa:
Riemannian Residual Neural Networks. NeurIPS 2023 - [c55]Albert Tseng, Tao Yu, Toni J. B. Liu, Christopher De Sa:
Coneheads: Hierarchy Aware Attention. NeurIPS 2023 - [c54]Zilu Li
, Guandao Yang, Xi Deng, Christopher De Sa, Bharath Hariharan, Steve Marschner:
Neural Caches for Monte Carlo Partial Differential Equation Solvers. SIGGRAPH Asia 2023: 34:1-34:10 - [c53]Oliver E. Richardson, Joseph Y. Halpern, Christopher De Sa:
Inference for probabilistic dependency graphs. UAI 2023: 1741-1751 - [i68]A. Feder Cooper, Solon Barocas, Christopher De Sa, Siddhartha Sen:
Variance, Self-Consistency, and Arbitrariness in Fair Classification. CoRR abs/2301.11562 (2023) - [i67]Wentao Guo, Khiem Pham, Yucheng Lu, Tiancheng Yuan, Charlie F. Ruan, Christopher De Sa:
Scale up with Order: Finding Good Data Permutations for Distributed Training. CoRR abs/2302.00845 (2023) - [i66]Yucheng Lu, Shivani Agrawal, Suvinay Subramanian, Oleg Rybakov, Christopher De Sa, Amir Yazdanbakhsh:
STEP: Learning N: M Structured Sparsity Masks from Scratch with Precondition. CoRR abs/2302.01172 (2023) - [i65]Tao Yu, Toni J. B. Liu, Albert Tseng, Christopher De Sa:
Shadow Cones: Unveiling Partial Orders in Hyperbolic Space. CoRR abs/2305.15215 (2023) - [i64]Albert Tseng, Tao Yu, Toni J. B. Liu, Christopher De Sa:
Coneheads: Hierarchy Aware Attention. CoRR abs/2306.00392 (2023) - [i63]Kush Bhatia, Avanika Narayan, Christopher De Sa, Christopher Ré:
TART: A plug-and-play Transformer module for task-agnostic reasoning. CoRR abs/2306.07536 (2023) - [i62]Yingheng Wang, Yair Schiff, Aaron Gokaslan, Weishen Pan, Fei Wang, Christopher De Sa, Volodymyr Kuleshov:
InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models. CoRR abs/2306.08757 (2023) - [i61]Jerry Chee, Yaohui Cai, Volodymyr Kuleshov, Christopher De Sa:
QuIP: 2-Bit Quantization of Large Language Models With Guarantees. CoRR abs/2307.13304 (2023) - [i60]Junjie Yin, Jiahao Dong, Yingheng Wang, Christopher De Sa, Volodymyr Kuleshov:
ModuLoRA: Finetuning 3-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers. CoRR abs/2309.16119 (2023) - [i59]Isay Katsman, Eric Ming Chen, Sidhanth Holalkere, Anna Asch, Aaron Lou, Ser-Nam Lim, Christopher De Sa:
Riemannian Residual Neural Networks. CoRR abs/2310.10013 (2023) - [i58]Oliver E. Richardson, Joseph Y. Halpern, Christopher De Sa:
Inference for Probabilistic Dependency Graphs. CoRR abs/2311.05580 (2023) - [i57]A. Feder Cooper, Katherine Lee, James Grimmelmann, Daphne Ippolito, Christopher Callison-Burch, Christopher A. Choquette-Choo, Niloofar Mireshghallah, Miles Brundage, David Mimno, Madiha Zahrah Choksi, Jack M. Balkin, Nicholas Carlini, Christopher De Sa, Jonathan Frankle, Deep Ganguli, Bryant Gipson, Andres Guadamuz, Swee Leng Harris, Abigail Z. Jacobs, Elizabeth Joh, Gautam Kamath, Mark A. Lemley, Cass Matthews, Christine McLeavey, Corynne McSherry, Milad Nasr, Paul Ohm, Adam Roberts, Tom Rubin, Pamela Samuelson, Ludwig Schubert, Kristen Vaccaro, Luis Villa, Felix Wu, Elana Zeide:
Report of the 1st Workshop on Generative AI and Law. CoRR abs/2311.06477 (2023) - [i56]Subham Sekhar Sahoo, Aaron Gokaslan, Chris De Sa, Volodymyr Kuleshov:
Diffusion Models With Learned Adaptive Noise. CoRR abs/2312.13236 (2023) - 2022
- [c52]A. Feder Cooper, Jonathan Frankle, Christopher De Sa:
Non-Determinism and the Lawlessness of Machine Learning Code. CSLAW 2022: 1-8 - [c51]Yucheng Lu, Si Yi Meng, Christopher De Sa:
A General Analysis of Example-Selection for Stochastic Gradient Descent. ICLR 2022 - [c50]Chengrun Yang, Ziyang Wu, Jerry Chee, Christopher De Sa, Madeleine Udell:
How Low Can We Go: Trading Memory for Error in Low-Precision Training. ICLR 2022 - [c49]Ruqi Zhang, Andrew Gordon Wilson, Christopher De Sa:
Low-Precision Stochastic Gradient Langevin Dynamics. ICML 2022: 26624-26644 - [c48]Jerry Chee, Megan Flynn, Anil Damle, Christopher De Sa:
Model Preserving Compression for Neural Networks. NeurIPS 2022 - [c47]Yucheng Lu, Wentao Guo, Christopher De Sa:
GraB: Finding Provably Better Data Permutations than Random Reshuffling. NeurIPS 2022 - [c46]Christopher De Sa, Satyen Kale, Jason D. Lee, Ayush Sekhari, Karthik Sridharan:
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. NeurIPS 2022 - [c45]Tao Yu, Yichi Zhang, Zhiru Zhang, Christopher De Sa:
Understanding Hyperdimensional Computing for Parallel Single-Pass Learning. NeurIPS 2022 - [i55]Tao Yu, Yichi Zhang, Zhiru Zhang, Christopher De Sa:
Understanding Hyperdimensional Computing for Parallel Single-Pass Learning. CoRR abs/2202.04805 (2022) - [i54]Yucheng Lu, Conglong Li, Minjia Zhang, Christopher De Sa, Yuxiong He:
Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam. CoRR abs/2202.06009 (2022) - [i53]Tao Yu, Christopher De Sa:
HyLa: Hyperbolic Laplacian Features For Graph Learning. CoRR abs/2202.06854 (2022) - [i52]Yaohui Cai, Weizhe Hua, Hongzheng Chen, G. Edward Suh, Christopher De Sa, Zhiru Zhang:
Structured Pruning is All You Need for Pruning CNNs at Initialization. CoRR abs/2203.02549 (2022) - [i51]Yucheng Lu, Wentao Guo, Christopher De Sa:
GraB: Finding Provably Better Data Permutations than Random Reshuffling. CoRR abs/2205.10733 (2022) - [i50]Ruqi Zhang, Andrew Gordon Wilson, Christopher De Sa:
Low-Precision Stochastic Gradient Langevin Dynamics. CoRR abs/2206.09909 (2022) - [i49]A. Feder Cooper, Jonathan Frankle, Christopher De Sa:
Non-Determinism and the Lawlessness of ML Code. CoRR abs/2206.11834 (2022) - [i48]Tao Yu, Went Guo, Jianan Canal Li, Tiancheng Yuan, Christopher De Sa:
MCTensor: A High-Precision Deep Learning Library with Multi-Component Floating-Point. CoRR abs/2207.08867 (2022) - [i47]Satyen Kale, Jason D. Lee, Chris De Sa, Ayush Sekhari, Karthik Sridharan:
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent. CoRR abs/2210.06705 (2022) - 2021
- [c44]Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang:
Meta-Learning Divergences for Variational Inference. AISTATS 2021: 4024-4032 - [c43]A. Feder Cooper, Karen Levy, Christopher De Sa:
Accuracy-Efficiency Trade-Offs and Accountability in Distributed ML Systems. EAAMO 2021: 4:1-4:11 - [c42]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 - [c41]Yucheng Lu, Christopher De Sa:
Optimal Complexity in Decentralized Training. ICML 2021: 7111-7123 - [c40]Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean P. Foster:
Variance Reduced Training with Stratified Sampling for Forecasting Models. ICML 2021: 7145-7155 - [c39]Bowen Yang, Jian Zhang, Jonathan Li, Christopher Ré, Christopher R. Aberger, Christopher De Sa:
PipeMare: Asynchronous Pipeline Parallel DNN Training. MLSys 2021 - [c38]A. Feder Cooper, Yucheng Lu, Jessica Forde, Christopher De Sa:
Hyperparameter Optimization Is Deceiving Us, and How to Stop It. NeurIPS 2021: 3081-3095 - [c37]Isay Katsman, Aaron Lou, Derek Lim, Qingxuan Jiang, Ser-Nam Lim, Christopher De Sa:
Equivariant Manifold Flows. NeurIPS 2021: 10600-10612 - [c36]Tao Yu, Christopher De Sa:
Representing Hyperbolic Space Accurately using Multi-Component Floats. NeurIPS 2021: 15570-15581 - [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 P. 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 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 S. 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 Randall 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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