Christopher De Sa

Affiliations:
  • Stanford University


According to our database1, Christopher De Sa authored at least 73 papers between 2014 and 2022.

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Bibliography

2022
GraB: Finding Provably Better Data Permutations than Random Reshuffling.
CoRR, 2022

Structured Pruning is All You Need for Pruning CNNs at Initialization.
CoRR, 2022

HyLa: Hyperbolic Laplacian Features For Graph Learning.
CoRR, 2022

Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam.
CoRR, 2022

Understanding Hyperdimensional Computing for Parallel Single-Pass Learning.
CoRR, 2022

2021
Tecnologica cosa: Modeling Storyteller Personalities in Boccaccio's Decameron.
CoRR, 2021

Pruning Neural Networks with Interpolative Decompositions.
CoRR, 2021

How Low Can We Go: Trading Memory for Error in Low-Precision Training.
CoRR, 2021

Model Selection's Disparate Impact in Real-World Deep Learning Applications.
CoRR, 2021

Variance Reduction in Training Forecasting Models with Subgroup Sampling.
CoRR, 2021

Low-Precision Reinforcement Learning.
CoRR, 2021

Hyperparameter Optimization Is Deceiving Us, and How to Stop It.
CoRR, 2021

Representing Hyperbolic Space Accurately using Multi-Component Floats.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Equivariant Manifold Flows.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Hyperparameter Optimization Is Deceiving Us, and How to Stop It.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

PipeMare: Asynchronous Pipeline Parallel DNN Training.
Proceedings of Machine Learning and Systems 2021, 2021

Optimal Complexity in Decentralized Training.
Proceedings of the 38th International Conference on Machine Learning, 2021

Variance Reduced Training with Stratified Sampling for Forecasting Models.
Proceedings of the 38th International Conference on Machine Learning, 2021

Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision.
Proceedings of the 38th International Conference on Machine Learning, 2021

Accuracy-Efficiency Trade-Offs and Accountability in Distributed ML Systems.
Proceedings of the EAAMO 2021: ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, Virtual Event, USA, October 5, 2021

Meta-Learning Divergences for Variational Inference.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Revisiting BFloat16 Training.
CoRR, 2020

Meta-Learning for Variational Inference.
CoRR, 2020

Regulating Accuracy-Efficiency Trade-Offs in Distributed Machine Learning Systems.
CoRR, 2020

Towards Optimal Convergence Rate in Decentralized Stochastic Training.
CoRR, 2020

MixML: A Unified Analysis of Weakly Consistent Parallel Learning.
CoRR, 2020

Optimizing JPEG Quantization for Classification Networks.
CoRR, 2020

Asymptotically Optimal Exact Minibatch Metropolis-Hastings.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Random Reshuffling is Not Always Better.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Neural Manifold Ordinary Differential Equations.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Moniqua: Modulo Quantized Communication in Decentralized SGD.
Proceedings of the 37th International Conference on Machine Learning, 2020

Differentiating through the Fréchet Mean.
Proceedings of the 37th International Conference on Machine Learning, 2020

AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Cloud-Hosted Intelligence for Real-time IoT Applications.
ACM SIGOPS Oper. Syst. Rev., 2019

Overwrite Quantization: Opportunistic Outlier Handling for Neural Network Accelerators.
CoRR, 2019

SysML: The New Frontier of Machine Learning Systems.
CoRR, 2019

Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

QPyTorch: A Low-Precision Arithmetic Simulation Framework.
Proceedings of the Fifth Workshop on Energy Efficient Machine Learning and Cognitive Computing, 2019

Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Dimension-Free Bounds for Low-Precision Training.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Channel Gating Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Boosting the Performance of CNN Accelerators with Dynamic Fine-Grained Channel Gating.
Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture, 2019

Improving Neural Network Quantization without Retraining using Outlier Channel Splitting.
Proceedings of the 36th International Conference on Machine Learning, 2019

SWALP : Stochastic Weight Averaging in Low Precision Training.
Proceedings of the 36th International Conference on Machine Learning, 2019

A Kernel Theory of Modern Data Augmentation.
Proceedings of the 36th International Conference on Machine Learning, 2019

Distributed Learning with Sublinear Communication.
Proceedings of the 36th International Conference on Machine Learning, 2019

A Formal Framework for Probabilistic Unclean Databases.
Proceedings of the 22nd International Conference on Database Theory, 2019

Building Efficient Deep Neural Networks With Unitary Group Convolutions.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018
Soft optoelectronic sensory foams with proprioception.
Sci. Robotics, 2018

Channel Gating Neural Networks.
CoRR, 2018

High-Accuracy Low-Precision Training.
CoRR, 2018

A Two-pronged Progress in Structured Dense Matrix Vector Multiplication.
Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, 2018

The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory.
Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing, 2018

Representation Tradeoffs for Hyperbolic Embeddings.
Proceedings of the 35th International Conference on Machine Learning, 2018

Minibatch Gibbs Sampling on Large Graphical Models.
Proceedings of the 35th International Conference on Machine Learning, 2018

Accelerated Stochastic Power Iteration.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Incremental knowledge base construction using DeepDive.
VLDB J., 2017

Flipper: A Systematic Approach to Debugging Training Sets.
Proceedings of the 2nd Workshop on Human-In-the-Loop Data Analytics, 2017

Gaussian Quadrature for Kernel Features.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Understanding and Optimizing Asynchronous Low-Precision Stochastic Gradient Descent.
Proceedings of the 44th Annual International Symposium on Computer Architecture, 2017

2016
DeepDive: Declarative Knowledge Base Construction.
SIGMOD Rec., 2016

Parallel SGD: When does averaging help?
CoRR, 2016

Socratic Learning.
CoRR, 2016

Data Programming: Creating Large Training Sets, Quickly.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Have abstraction and eat performance, too: optimized heterogeneous computing with parallel patterns.
Proceedings of the 2016 International Symposium on Code Generation and Optimization, 2016

Generating Configurable Hardware from Parallel Patterns.
Proceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems, 2016

2015
Incremental Knowledge Base Construction Using DeepDive.
Proc. VLDB Endow., 2015

Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2014
Global Convergence of Stochastic Gradient Descent for Some Nonconvex Matrix Problems.
CoRR, 2014


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