More Like this

1. Scalable Gaussian Process Variational Autoencoders

   Jazbec, Metod ; Ashman, Matthew ; Fortuin, Vincent ; Pearce, Michael ; Mandt, Stephan ; Raetsch, Gunnar ( April 2021 , Proceedings of Machine Learning Research)

   null (Ed.)

   Conventional variational autoencoders fail in modeling correlations between data points due to their use of factorized priors. Amortized Gaussian process inference through GPVAEs has led to significant improvements in this regard, but is still inhibited by the intrinsic complexity of exact GP inference. We improve the scalability of these methods through principled sparse inference approaches. We propose a new scalable GPVAE model that outperforms existing approaches in terms of runtime and memory footprint, is easy to implement, and allows for joint end-to-end optimization of all components 

   more » « less
2. Deterministic Routing between Layout Abstractions for Multi-Scale Classification of Visually Rich Documents

   https://doi.org/10.24963/ijcai.2019/466  

   Sarkhel, Ritesh ; Nandi, Arnab ( August 2019 , 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019)

   Classifying heterogeneous visually rich documents is a challenging task. Difficulty of this task increases even more if the maximum allowed inference turnaround time is constrained by a threshold. The increased overhead in inference cost, compared to the limited gain in classification capabilities make current multi-scale approaches infeasible in such scenarios. There are two major contributions of this work. First, we propose a spatial pyramid model to extract highly discriminative multi-scale feature descriptors from a visually rich document by leveraging the inherent hierarchy of its layout. Second, we propose a deterministic routing scheme for accelerating end-to-end inference by utilizing the spatial pyramid model. A depth-wise separable multi-column convolutional network is developed to enable our method. We evaluated the proposed approach on four publicly available, benchmark datasets of visually rich documents. Results suggest that our proposed approach demonstrates robust performance compared to the state-of-the-art methods in both classification accuracy and total inference turnaround.

    

   more » « less
3. Individualized inference through fusion learning

   https://doi.org/10.1002/wics.1498  

   Cai, Chencheng ; Chen, Rong ; Xie, Min‐ge ( January 2020 , WIREs Computational Statistics)

   Fusion learning methods, developed for the purpose of analyzing datasets from many different sources, have become a popular research topic in recent years. Individualized inference approaches through fusion learning extend fusion learning approaches to individualized inference problems over a heterogeneous population, where similar individuals are fused together to enhance the inference over the target individual. Both classical fusion learning and individualized inference approaches through fusion learning are established based on weighted aggregation of individual information, but the weight used in the latter is localized to thetargetindividual. This article provides a review on two individualized inference methods through fusion learning,iFusion andiGroup, that are developed under different asymptotic settings. Both procedures guarantee optimal asymptotic theoretical performance and computational scalability.

   This article is categorized under:

   Statistical Learning and Exploratory Methods of the Data Sciences > Manifold Learning

   Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods

   Statistical and Graphical Methods of Data Analysis > Nonparametric Methods

   Data: Types and Structure > Massive Data

    

   more » « less
4. α-deep Probabilistic Inference (α-DPI): Efficient Uncertainty Quantification from Exoplanet Astrometry to Black Hole Feature Extraction

   https://doi.org/10.3847/1538-4357/ac6be9  

   Sun, He ; Bouman, Katherine L. ; Tiede, Paul ; Wang, Jason J. ; Blunt, Sarah ; Mawet, Dimitri ( June 2022 , The Astrophysical Journal)

   Inference is crucial in modern astronomical research, where hidden astrophysical features and patterns are often estimated from indirect and noisy measurements. Inferring the posterior of hidden features, conditioned on the observed measurements, is essential for understanding the uncertainty of results and downstream scientific interpretations. Traditional approaches for posterior estimation include sampling-based methods and variational inference (VI). However, sampling-based methods are typically slow for high-dimensional inverse problems, while VI often lacks estimation accuracy. In this paper, we proposeα-deep probabilistic inference, a deep learning framework that first learns an approximate posterior usingα-divergence VI paired with a generative neural network, and then produces more accurate posterior samples through importance reweighting of the network samples. It inherits strengths from both sampling and VI methods: it is fast, accurate, and more scalable to high-dimensional problems than conventional sampling-based approaches. We apply our approach to two high-impact astronomical inference problems using real data: exoplanet astrometry and black hole feature extraction.

    

   more » « less
5. DeNASA: Destination-Naive AS-Awareness in Anonymous Communications

   https://doi.org/10.1515/popets-2016-0044  

   Barton, Armon ; Wright, Matthew ( January 2016 , Proceedings on Privacy Enhancing Technologies)

   Abstract Prior approaches to AS-aware path selection in Tor do not consider node bandwidth or the other characteristics that Tor uses to ensure load balancing and quality of service. Further, since the AS path from the client’s exit to her destination can only be inferred once the destination is known, the prior approaches may have problems constructing circuits in advance, which is important for Tor performance. In this paper, we propose and evaluate DeNASA, a new approach to AS-aware path selection that is destination-naive, in that it does not need to know the client’s destination to pick paths, and that takes advantage of Tor’s circuit selection algorithm. To this end, we first identify the most probable ASes to be traversed by Tor streams. We call this set of ASes the Suspect AS list and find that it consists of eight highest ranking Tier 1 ASes. Then, we test the accuracy of Qiu and Gao AS-level path inference on identifying the presence of these ASes in the path, and we show that inference accuracy is 90%. We develop an AS-aware algorithm called DeNASA that uses Qiu and Gao inference to avoid Suspect ASes. DeNASA reduces Tor stream vulnerability by 74%. We also show that DeNASA has performance similar to Tor. Due to the destination-naive property, time to first byte (TTFB) is close to Tor’s, and due to leveraging Tor’s bandwidth-weighted relay selection, time to last byte (TTLB) is also similar to Tor’s. 

   more » « less