Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality
- Nian Si
- Jose Blanchet
- et al.
- 2020
- NeurIPS 2020
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Asymptotic Guarantees for Generative Modeling Based on the Smooth Wasserstein Distance
- Ziv Goldfeld
- Kristjan Greenewald
- et al.
- 2020
- NeurIPS 2020
Heavy-tailed Representations, Text Polarity Classification & Data Augmentation
- Hamid Jalalzai
- Pierre Colombo
- et al.
- 2020
- NeurIPS 2020
Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment
- Ben Usman
- Avneesh Sud
- et al.
- 2020
- NeurIPS 2020
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes
- Minh Hoang
- Trong Nghia Hoang
- 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
Learning Global Transparent Models consistent with Local Contrastive Explanations
- Tejaswini Pedapati
- Avinash Balakrishnan
- et al.
- 2020
- NeurIPS 2020
TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning
- Han Cai
- Chuang Gan
- et al.
- 2020
- NeurIPS 2020
Finding the Homology of Decision Boundaries with Active Learning
- Weizhi Li
- Gautam Dasarathy
- et al.
- 2020
- NeurIPS 2020
Auxiliary Task Reweighting for Minimum-data Learning
- Baifeng Shi
- Judy Hoffman
- et al.
- 2020
- NeurIPS 2020