Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment
- Ben Usman
- Avneesh Sud
- 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
Finding the Homology of Decision Boundaries with Active Learning
- Weizhi Li
- Gautam Dasarathy
- et al.
- 2020
- NeurIPS 2020
Entropic Causal Inference: Identifiability and Finite Sample Results Spencer Compton
- Spencer Compton
- Murat Kocaoglu
- et al.
- 2020
- NeurIPS 2020
Model Agnostic Learning of Multilevel Explanations
- Karthikeyan Natesan Ramamurthy
- Bhanu Vinzamuri
- et al.
- 2020
- NeurIPS 2020
The Lottery Ticket Hypothesis for Pre-trained BERT Networks
- Tianlong Chen
- Jonathan Frankle
- et al.
- 2020
- NeurIPS 2020
Heavy-tailed Representations, Text Polarity Classification & Data Augmentation
- Hamid Jalalzai
- Pierre Colombo
- 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
Private Identity Testing for High-Dimensional Distributions
- Clément L. Canonne
- Gautam Kamath
- 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