Christopher De Sa
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
39
Venues
16
Active years
2015–2025
Best venue rank
A*
Where they publish
Papers
39 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | ICLR | Compute-Optimal LLMs Provably Generalize Better with Scale. | Marc Anton Finzi, Sanyam Kapoor, Diego Granziol, Anming Gu, Christopher De Sa, J. Zico Kolter, Andrew Gordon Wilson |
| 2025 | ICLR | Zeroth-Order Fine-Tuning of LLMs with Transferable Static Sparsity. | 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 |
| 2024 | AAAI | Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification. | A. Feder Cooper, Katherine Lee, Madiha Zahrah Choksi, Solon Barocas, Christopher De Sa, James Grimmelmann, Jon M. Kleinberg, Siddhartha Sen, Baobao Zhang |
| 2024 | ICLR | Shadow Cones: A Generalized Framework for Partial Order Embeddings. | Tao Yu, Toni J. B. Liu, Albert Tseng, Christopher De Sa |
| 2024 | ICML | QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks. | Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, Christopher De Sa |
| 2023 | ICLR | Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam. | Yucheng Lu, Conglong Li, Minjia Zhang, Christopher De Sa, Yuxiong He |
| 2023 | ICLR | Random Laplacian Features for Learning with Hyperbolic Space. | Tao Yu, Christopher De Sa |
| 2023 | ICML | STEP: Learning N: M Structured Sparsity Masks from Scratch with Precondition. | Yucheng Lu, Shivani Agrawal, Suvinay Subramanian, Oleg Rybakov, Christopher De Sa, Amir Yazdanbakhsh |
| 2023 | ICML | CocktailSGD: Fine-tuning Foundation Models over 500Mbps Networks. | Jue Wang, Yucheng Lu, Binhang Yuan, Beidi Chen, Percy Liang, Christopher De Sa, Christopher R, Ce Zhang |
| 2023 | ICML | InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models. | Yingheng Wang, Yair Schiff, Aaron Gokaslan, Weishen Pan, Fei Wang, Christopher De Sa, Volodymyr Kuleshov |
| 2023 | UAI | Inference for probabilistic dependency graphs. | Oliver E. Richardson, Joseph Y. Halpern, Christopher De Sa |
| 2023 | SiggraphA | Neural Caches for Monte Carlo Partial Differential Equation Solvers. | Zilu Li, Guandao Yang, Xi Deng, Christopher De Sa, Bharath Hariharan, Steve Marschner |
| 2022 | ICLR | A General Analysis of Example-Selection for Stochastic Gradient Descent. | Yucheng Lu, Si Yi Meng, Christopher De Sa |
| 2022 | ICLR | How Low Can We Go: Trading Memory for Error in Low-Precision Training. | Chengrun Yang, Ziyang Wu, Jerry Chee, Christopher De Sa, Madeleine Udell |
| 2022 | ICML | Low-Precision Stochastic Gradient Langevin Dynamics. | Ruqi Zhang, Andrew Gordon Wilson, Christopher De Sa |
| 2021 | AISTATS | Meta-Learning Divergences for Variational Inference. | Ruqi Zhang, Yingzhen Li, Christopher De Sa, Sam Devlin, Cheng Zhang |
| 2021 | ICML | Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision. | Johan Bjrck, Xiangyu Chen, Christopher De Sa, Carla P. Gomes, Kilian Q. Weinberger |
| 2021 | ICML | Variance Reduced Training with Stratified Sampling for Forecasting Models. | Yucheng Lu, Youngsuk Park, Lifan Chen, Yuyang Wang, Christopher De Sa, Dean P. Foster |
| 2021 | ICML | Optimal Complexity in Decentralized Training. | Yucheng Lu, Christopher De Sa |
| 2020 | AISTATS | AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC. | Ruqi Zhang, A. Feder Cooper, Christopher De Sa |
| 2020 | ICML | Differentiating through the Frchet Mean. | Aaron Lou, Isay Katsman, Qingxuan Jiang, Serge J. Belongie, Ser-Nam Lim, Christopher De Sa |
| 2020 | ICML | Moniqua: Modulo Quantized Communication in Decentralized SGD. | Yucheng Lu, Christopher De Sa |
| 2019 | CVPR | Building Efficient Deep Neural Networks With Unitary Group Convolutions. | Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Christopher De Sa, Zhiru Zhang |
| 2019 | ICDT | A Formal Framework for Probabilistic Unclean Databases. | Christopher De Sa, Ihab F. Ilyas, Benny Kimelfeld, Christopher R, Theodoros Rekatsinas |
| 2019 | ICML | SWALP : Stochastic Weight Averaging in Low Precision Training. | Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, Christopher De Sa |
| 2019 | ICML | Improving Neural Network Quantization without Retraining using Outlier Channel Splitting. | Ritchie Zhao, Yuwei Hu, Jordan Dotzel, Christopher De Sa, Zhiru Zhang |
| 2019 | MICRO | Boosting the Performance of CNN Accelerators with Dynamic Fine-Grained Channel Gating. | Weizhe Hua, Yuan Zhou, Christopher De Sa, Zhiru Zhang, G. Edward Suh |
| 2018 | AISTATS | Accelerated Stochastic Power Iteration. | Peng Xu, Bryan D. He, Christopher De Sa, Ioannis Mitliagkas, Christopher R |
| 2018 | ICML | Minibatch Gibbs Sampling on Large Graphical Models. | Christopher De Sa, Vincent Chen, Wing Wong |
| 2018 | ICML | Representation Tradeoffs for Hyperbolic Embeddings. | Frederic Sala, Christopher De Sa, Albert Gu, Christopher R |
| 2018 | PODC | The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory. | Dan Alistarh, Christopher De Sa, Nikola Konstantinov |
| 2018 | SODA | A Two-pronged Progress in Structured Dense Matrix Vector Multiplication. | Christopher De Sa, Albert Gu, Rohan Puttagunta, Christopher R, Atri Rudra |
| 2017 | IJCAI | Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling. | Christopher De Sa, Kunle Olukotun, Christopher R |
| 2017 | ISCA | Understanding and Optimizing Asynchronous Low-Precision Stochastic Gradient Descent. | Christopher De Sa, Matthew Feldman, Christopher R, Kunle Olukotun |
| 2017 | SIGMOD | Flipper: A Systematic Approach to Debugging Training Sets. | Paroma Varma, Dan Iter, Christopher De Sa, Christopher R |
| 2016 | ASPLOS | Generating Configurable Hardware from Parallel Patterns. | Raghu Prabhakar, David Koeplinger, Kevin J. Brown, HyoukJoong Lee, Christopher De Sa, Christos Kozyrakis, Kunle Olukotun |
| 2016 | CGO | 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 R. Aberger, Kunle Olukotun |
| 2016 | ICML | Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling. | Christopher De Sa, Christopher R, Kunle Olukotun |
| 2015 | ICML | Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems. | Christopher De Sa, Christopher R, Kunle Olukotun |