Publications

DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks
- - Dennis Wei
  - Tian Gao
  - et al.
- 2020
- NeurIPS 2020
The Discrete Gaussian for Differential Privacy
- - Clément L. Canonne
  - Gautam Kamath
  - et al.
- 2020
- NeurIPS 2020
Private Identity Testing for High-Dimensional Distributions
- - Clément L. Canonne
  - Gautam Kamath
  - et al.
- 2020
- NeurIPS 2020
Asymptotic Guarantees for Generative Modeling Based on the Smooth Wasserstein Distance
- - Ziv Goldfeld
  - Kristjan Greenewald
  - et al.
- 2020
- NeurIPS 2020
Training Stronger Baselines for Learning to Optimize
- - Tianlong Chen
  - Weiyi Zhang
  - et al.
- 2020
- NeurIPS 2020
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions
- - Matthew Faw
  - Rajat Sen
  - et al.
- 2020
- NeurIPS 2020
MCUNet: Tiny Deep Learning on IoT Devices
- - Ji Lin
  - Wei-Ming Chen
  - et al.
- 2020
- NeurIPS 2020
TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning
- - Han Cai
  - Chuang Gan
  - et al.
- 2020
- NeurIPS 2020
Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality
- - Nian Si
  - Jose Blanchet
  - et al.
- 2020
- NeurIPS 2020
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning
- - Amin Jaber
  - Murat Kocaoglu
  - et al.
- 2020
- NeurIPS 2020