7 research outputs found
Transfer-Learning Methods in Programming Course Outcome Prediction
The computing education research literature contains a wide variety of methods that can be used to identify students who are either at risk of failing their studies or who could benefit from additional challenges. Many of these are based on machine-learning models that learn to make predictions based on previously observed data. However, in educational contexts, differences between courses set huge challenges for the generalizability of these methods. For example, traditional machine-learning methods assume identical distribution in all data—in our terms, traditional machine-learning methods assume that all teaching contexts are alike. In practice, data collected from different courses can be very different as a variety of factors may change, including grading, materials, teaching approach, and the students. Transfer-learning methodologies have been created to address this challenge. They relax the strict assumption of identical distribution for training and test data. Some similarity between the contexts is still needed for efficient learning. In this work, we review the concept of transfer learning especially for the purpose of predicting the outcome of an introductory programming course and contrast the results with those from traditional machine-learning methods. The methods are evaluated using data collected in situ from two separate introductory programming courses. We empirically show that transfer-learning methods are able to improve the predictions, especially in cases with limited amount of training data, for example, when making early predictions for a new context. The difference in predictive power is, however, rather subtle, and traditional machine-learning models can be sufficiently accurate assuming the contexts are closely related and the features describing the student activity are carefully chosen to be insensitive to the fine differences.Peer reviewe
A Survey on Negative Transfer
Transfer learning (TL) tries to utilize data or knowledge from one or more
source domains to facilitate the learning in a target domain. It is
particularly useful when the target domain has few or no labeled data, due to
annotation expense, privacy concerns, etc. Unfortunately, the effectiveness of
TL is not always guaranteed. Negative transfer (NT), i.e., the source domain
data/knowledge cause reduced learning performance in the target domain, has
been a long-standing and challenging problem in TL. Various approaches to
handle NT have been proposed in the literature. However, this filed lacks a
systematic survey on the formalization of NT, their factors and the algorithms
that handle NT. This paper proposes to fill this gap. First, the definition of
negative transfer is considered and a taxonomy of the factors are discussed.
Then, near fifty representative approaches for handling NT are categorized and
reviewed, from four perspectives: secure transfer, domain similarity
estimation, distant transfer and negative transfer mitigation. NT in related
fields, e.g., multi-task learning, lifelong learning, and adversarial attacks
are also discussed
An Information-Theoretic Analysis for Transfer Learning: Error Bounds and Applications
Transfer learning, or domain adaptation, is concerned with machine learning
problems in which training and testing data come from possibly different
probability distributions. In this work, we give an information-theoretic
analysis on the generalization error and excess risk of transfer learning
algorithms, following a line of work initiated by Russo and Xu. Our results
suggest, perhaps as expected, that the Kullback-Leibler (KL) divergence
plays an important role in the characterizations where and
denote the distribution of the training data and the testing test,
respectively. Specifically, we provide generalization error upper bounds for
the empirical risk minimization (ERM) algorithm where data from both
distributions are available in the training phase. We further apply the
analysis to approximated ERM methods such as the Gibbs algorithm and the
stochastic gradient descent method. We then generalize the mutual information
bound with -divergence and Wasserstein distance. These generalizations
lead to tighter bounds and can handle the case when is not absolutely
continuous with respect to . Furthermore, we apply a new set of
techniques to obtain an alternative upper bound which gives a fast (and
optimal) learning rate for some learning problems. Finally, inspired by the
derived bounds, we propose the InfoBoost algorithm in which the importance
weights for source and target data are adjusted adaptively in accordance to
information measures. The empirical results show the effectiveness of the
proposed algorithm.Comment: 47 pages, 6 figure
Data-Efficient Machine Learning with Focus on Transfer Learning
Machine learning (ML) has attracted a significant amount of attention from the artifi- cial intelligence community. ML has shown state-of-art performance in various fields, such as signal processing, healthcare system, and natural language processing (NLP). However, most conventional ML algorithms suffer from three significant difficulties: 1) insufficient high-quality training data, 2) costly training process, and 3) domain dis- crepancy. Therefore, it is important to develop solutions for these problems, so the future of ML will be more sustainable. Recently, a new concept, data-efficient ma- chine learning (DEML), has been proposed to deal with the current bottlenecks of ML. Moreover, transfer learning (TL) has been considered as an effective solution to address the three shortcomings of conventional ML. Furthermore, TL is one of the most active areas in the DEML. Over the past ten years, significant progress has been made in TL.
In this dissertation, I propose to address the three problems by developing a software- oriented framework and TL algorithms. Firstly, I introduce a DEML framework and a evaluation system. Moreover, I present two novel TL algorithms and applications on real-world problems. Furthermore, I will first present the first well-defined DEML framework and introduce how it can address the challenges in ML. After that, I will give an updated overview of the state-of-the-art and open challenges in the TL. I will then introduce two novel algorithms for two of the most challenging TL topics: distant domain TL and cross-modality TL (image-text). A detailed algorithm introduction and preliminary results on real-world applications (Covid-19 diagnosis and image clas- sification) will be presented. Then, I will discuss the current trends in TL algorithms and real-world applications. Lastly, I will present the conclusion and future research directions
Selective Transfer Between Learning Tasks Using Task-Based Boosting
The success of transfer learning on a target task is highly dependent on the selected source data. Instance transfer methods reuse data from the source tasks to augment the training data for the target task. If poorly chosen, this source data may inhibit learning, resulting in negative transfer. The current most widely used algorithm for instance transfer, TrAdaBoost, performs poorly when given irrelevant source data. We present a novel task-based boosting technique for instance transfer that selectively chooses the source knowledge to transfer to the target task. Our approach performs boosting at both the instance level and the task level, assigning higher weight to those source tasks that show positive transferability to the target task, and adjusting the weights of individual instances within each source task via AdaBoost. We show that this combination of task- and instance-level boosting significantly improves transfer performance over existing instance transfer algorithms when given a mix of relevant and irrelevant source data, especially for small amounts of data on the target task