Statistical Learning
Bayesian Semi-supervised Learning with Graph Gaussian Processes
Yin Cheng Ng, Nicolรฒ Colombo, Ricardo Silva
Neural Information Processing Systems http://nips.cc/
Why Is My Classifier Discriminatory?
Irene Chen, Fredrik D. Johansson, David Sontag
Recent attempts to achieve fairness in predictive models focus on the balance between fairness and accuracy.
Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces
Yu-An Chung, Wei-Hung Weng, Schrasing Tong, James Glass
The framework consists of two steps.
Generalizing to Unseen Domains via Adversarial Data Augmentation
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John C. Duchi, Vittorio Murino, Silvio Savarese
We are concerned with learning models that generalize well to different unseen domains.
Variational Inference with Tail-adaptive f-Divergence
Dilin Wang, Hao Liu, Qiang Liu
V ariational inference (VI) (e.g., Jordan et al., 1999; Wainwright et al., 2008) has been established Combined with techniques like stochastic optimization (Ranganath et al., A key component of successful variational inference lies on choosing a proper divergence metric. Work done at UT Austin 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montrรฉal, Canada.
Exponentiated Strongly Rayleigh Distributions
Strongly Rayleigh (SR) measures are discrete probability distributions over the subsets of a ground set. They enjoy strong negative dependence properties, as a result of which they assign higher probability to subsets of diverse elements. We introduce in this paper Exponentiated Strongly Rayleigh (ESR) measures, which sharpen (or smoothen) the negative dependence property of SR measures via a single parameter (the exponent) that can be intuitively understood as an inverse temperature. We develop efficient MCMC procedures for approximate sampling from ESRs, and obtain explicit mixing time bounds for two concrete instances: exponentiated versions of Determinantal Point Processes and Dual V olume Sampling. We illustrate some of the potential of ESRs, by applying them to a few machine learning problems; empirical results confirm that beyond their theoretical appeal, ESR-based models hold significant promise for these tasks.
Gaussian Process Prior Variational Autoencoders
Francesco Paolo Casale, Adrian Dalca, Luca Saglietti, Jennifer Listgarten, Nicolo Fusi
Dimensionality reduction is a fundamental approach to compression of complex, large-scale data sets, either for visualization or for pre-processing before application of supervised approaches.
Gen-Oja: A Simple and Efficient Algorithm for Streaming Generalized Eigenvector Computation
In this paper, we study the problems of principal Generalized Eigenvector computation and Canonical Correlation Analysis in the stochastic setting. We propose a simple and efficient algorithm, Gen-Oja, for these problems.
Statistical and Computational Trade-Offs in Kernel K-Means
Daniele Calandriello, Lorenzo Rosasco
We investigate the efficiency of k-means in terms of both statistical and computational requirements.