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Found 11 papers, 6 papers with code
Confidence Sets under Generalized Self-Concordance
This paper revisits a fundamental problem in statistical inference from a non-asymptotic theoretical viewpoint $\unicode{x2013}$ the construction of confidence sets.
MAUVE Scores for Generative Models: Theory and Practice
We present MAUVE, a family of comparison measures between pairs of distributions such as those encountered in the generative modeling of text or images.
Stochastic Optimization for Spectral Risk Measures
Spectral risk objectives - also called $L$-risks - allow for learning systems to interpolate between optimizing average-case performance (as in empirical risk minimization) and worst-case performance on a task.
Statistical and Computational Guarantees for Influence Diagnostics
Influence diagnostics such as influence functions and approximate maximum influence perturbations are popular in machine learning and in AI domain applications.
Orthogonal Statistical Learning with Self-Concordant Loss
Orthogonal statistical learning and double machine learning have emerged as general frameworks for two-stage statistical prediction in the presence of a nuisance component.
Distribution Embedding Networks for Generalization from a Diverse Set of Classification Tasks
We propose Distribution Embedding Networks (DEN) for classification with small data.
Entropy Regularized Optimal Transport Independence Criterion
We introduce an independence criterion based on entropy regularized optimal transport.
Score-Based Change Detection for Gradient-Based Learning Machines
The widespread use of machine learning algorithms calls for automatic change detection algorithms to monitor their behavior over time.
Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals
The spectacular success of deep generative models calls for quantitative tools to measure their statistical performance.
Distribution Embedding Network for Meta-Learning with Variable-Length Input
We propose Distribution Embedding Network (DEN) for meta-learning, which is designed for applications where both the distribution and the number of features could vary across tasks.
Asymptotics of Discrete Schrödinger Bridges via Chaos Decomposition
We consider instead in this paper the problem where each matching is endowed with a Gibbs probability weight proportional to the exponential of the negative total cost of that matching.
