Efficient, noise-tolerant, and private learning via boosting

We introduce a simple framework for designing private boosting algorithms. We give natural conditions under which these algorithms are differentially private, efficient, and noise-tolerant PAC learners. To demonstrate our framework, we use it to construct noise-tolerant and private PAC learners for large-margin halfspaces whose sample complexity does not depend on the dimension. We give two sample complexity bounds for our large-margin halfspace learner. One bound is based only on differential privacy, and uses this guarantee as an asset for ensuring generalization. This first bound illustrates a general methodology for obtaining PAC learners from privacy, which may be of independent interest. The second bound uses standard techniques from the theory of large-margin classification (the fat-shattering dimension) to match the best known sample complexity for differentially private learning of large-margin halfspaces, while additionally tolerating random label noise.https://arxiv.org/pdf/2002.01100.pd

Scalable Greedy Algorithms for Transfer Learning

In this paper we consider the binary transfer learning problem, focusing on how to select and combine sources from a large pool to yield a good performance on a target task. Constraining our scenario to real world, we do not assume the direct access to the source data, but rather we employ the source hypotheses trained from them. We propose an efficient algorithm that selects relevant source hypotheses and feature dimensions simultaneously, building on the literature on the best subset selection problem. Our algorithm achieves state-of-the-art results on three computer vision datasets, substantially outperforming both transfer learning and popular feature selection baselines in a small-sample setting. We also present a randomized variant that achieves the same results with the computational cost independent from the number of source hypotheses and feature dimensions. Also, we theoretically prove that, under reasonable assumptions on the source hypotheses, our algorithm can learn effectively from few examples

Inquiries into words, constraints and contexts : Festschrift in the honour of Kimmo Koskenniemi on his 60th birthday

Peer reviewe

Nonparametric Bayesian Topic Modelling with Auxiliary Data

The intent of this dissertation in computer science is to study topic models for text analytics. The first objective of this dissertation is to incorporate auxiliary information present in text corpora to improve topic modelling for natural language processing (NLP) applications. The second objective of this dissertation is to extend existing topic models to employ state-of-the-art nonparametric Bayesian techniques for better modelling of text data. In particular, this dissertation focusses on: - incorporating hashtags, mentions, emoticons, and target-opinion dependency present in tweets, together with an external sentiment lexicon, to perform opinion mining or sentiment analysis on products and services; - leveraging abstracts, titles, authors, keywords, categorical labels, and the citation network to perform bibliographic analysis on research publications, using a supervised or semi-supervised topic model; and - employing the hierarchical Pitman-Yor process (HPYP) and the Gaussian process (GP) to jointly model text, hashtags, authors, and the follower network in tweets for corpora exploration and summarisation. In addition, we provide a framework for implementing arbitrary HPYP topic models to ease the development of our proposed topic models, made possible by modularising the Pitman-Yor processes. Through extensive experiments and qualitative assessment, we find that topic models fit better to the data as we utilise more auxiliary information and by employing the Bayesian nonparametric method

Technology for large space systems: A bibliography with indexes (supplement 16)

This bibliography lists 673 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1, 1986 and December 31, 1986. Its purpose is to provide helpful information to the researcher, manager, and designer in technology development and mission design according to system interactive analysis and design, structural and thermal analysis and design, structural concepts and control systems, electronics, advanced materials, assembly concepts, propulsion, and solar power satellite systems