Transfer learning through greedy subset selection

We study the binary transfer learning problem, focusing on how to select sources from a large pool and how to combine them to yield a good performance on a target task. In particular, we consider the transfer learning setting where one does not have direct access to the source data, but rather employs the source hypotheses trained from them. Building on the literature on the best subset selection problem, we propose an efficient algorithm that selects relevant source hypotheses and feature dimensions simultaneously. On three computer vision datasets we achieve state-of-the-art results, substantially outperforming transfer learning and popular feature selection baselines in a small-sample setting. Also, we theoretically prove that, under reasonable assumptions on the source hypotheses, our algorithm can learn effectively from few examples

Theory and Algorithms for Hypothesis Transfer Learning

The design and analysis of machine learning algorithms typically considers the problem of learning on a single task, and the nature of learning in such scenario is well explored. On the other hand, very often tasks faced by machine learning systems arrive sequentially, and therefore it is reasonable to ask whether a better approach can be taken than retraining such systems from scratch given newly available data. Indeed, by drawing analogy from human learning, a novel skill could be acquired more easily whenever the learner shares a relevant past experience. In response to this observation, the machine learning community has drawn its attention towards a form of learning known as transfer learning - learning a novel task by leveraging upon auxiliary information extracted from previous tasks. Tangible progress has been made in both theory and practice of transfer learning; however, many questions are still to be addressed. In this thesis we will focus on an efficient type of transfer learning, known as the Hypothesis Transfer Learning (HTL), where auxiliary information is retained in a form of previously induced hypotheses. This is in contrast to the large body of work where one transfers from the data associated with previously encountered tasks. In particular, we theoretically investigate conditions when HTL guarantees improved generalization on a novel task subject to the relevant auxiliary (source) hypotheses. We investigate HTL theoretically by considering three scenarios: HTL through regularized least squares with biased regularization, through convex empirical risk minimization, and through stochastic optimization, which also touches the theory of non-convex transfer learning problems. In addition, we demonstrate the benefits of HTL empirically, by proposing two algorithms tailored for real-life situations with application to visual learning problems - learning a new class in a multi-class classification setting by transferring from known classes, and an efficient greedy HTL algorithm for learning with large number of source hypotheses. From theoretical point of view this thesis consistently identifies the key quantitative characteristics of relatedness between novel and previous tasks, and explicitates them in generalization bounds. These findings corroborate many previous works in the transfer learning literature and provide a theoretical basis for design and analysis of new HTL algorithms

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

Adaptive learning to speed-up control of prosthetic hands: A few things everybody should know

Domain adaptation methods have been proposed to reduce the training efforts needed to control an upper-limb prosthesis by adapting well performing models from previous subjects to the new subject. These studies generally reported impressive reductions in the required number of training samples to achieve a certain level of accuracy for intact subjects. We further investigate two popular methods in this field to verify whether this result also applies to amputees. Our findings show instead that this improvement can largely be attributed to a suboptimal hyperparameter configuration. When hyperparameters are appropriately tuned, the standard approach that does not exploit prior information performs on par with the more complicated transfer learning algorithms. Additionally, earlier studies erroneously assumed that the number of training samples relates proportionally to the efforts required from the subject. However, a repetition of a movement is the atomic unit for subjects and the total number of repetitions should therefore be used as reliable measure for training efforts. Also when correcting for this mistake, we do not find any performance increase due to the use of prior models

Export learning process in local supplier networks

The objective of this study is to analyze the influence of a multinational corporation on the productive network of the host territory and the proliferation of entrepreneurs. In particular, an attempt has been made to analyze the influence on the exporting activities of local SMEs, both suppliers and non-suppliers. The study has shown that strategic integrated suppliers show greater exporting tendencies than those which are not considered to be strategic suppliers for the MNC. Similarly, those companies whose founder and/or part of the executive team have worked previously in the MNC show greater levels of export activity, compared to those companies founded by local entrepreneurs

Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems

This paper was motivated by the problem of how to make robots fuse and transfer their experience so that they can effectively use prior knowledge and quickly adapt to new environments. To address the problem, we present a learning architecture for navigation in cloud robotic systems: Lifelong Federated Reinforcement Learning (LFRL). In the work, We propose a knowledge fusion algorithm for upgrading a shared model deployed on the cloud. Then, effective transfer learning methods in LFRL are introduced. LFRL is consistent with human cognitive science and fits well in cloud robotic systems. Experiments show that LFRL greatly improves the efficiency of reinforcement learning for robot navigation. The cloud robotic system deployment also shows that LFRL is capable of fusing prior knowledge. In addition, we release a cloud robotic navigation-learning website based on LFRL