Robust graph transduction

University of Technology Sydney. Faculty of Engineering and Information Technology.Given a weighted graph, graph transduction aims to assign unlabeled examples explicit class labels rather than build a general decision function based on the available labeled examples. Practically, a dataset usually contains many noisy data, such as the “bridge points” located across different classes, and the “outliers” that incur abnormal distances from the normal examples of their classes. The labels of these examples are usually ambiguous and also difficult to decide. Labeling them incorrectly may further bring about erroneous classifications on the remaining unlabeled examples. Therefore, their accurate classifications are critical to obtaining satisfactory final performance. Unfortunately, current graph transduction algorithms usually fall short of tackling the noisy but critical examples, so they may become fragile and produce imperfect results sometimes. Therefore, in this thesis we aim to develop a series of robust graph transduction methodologies via iterative or non-iterative way, so that they can perfectly handle the difficult noisy data points. Our works are summarized as follows: In Chapter 2, we propose a robust non-iterative algorithm named “Label Prediction via Deformed Graph Laplacian” (LPDGL). Different from the existing methods that usually employ a traditional graph Laplacian to achieve label smoothness among pairs of examples, in LPDGL we introduce a deformed graph Laplacian, which not only induces the existing pairwise smoothness term, but also leads to a novel local smoothness term. This local smoothness term detects the ambiguity of each example by exploring the associated degree, and assigns confident labels to the examples with large degree, as well as allocates “weak labels” to the uncertain examples with small degree. As a result, the negative effects of outliers and bridge points are suppressed, leading to more robust transduction performance than some existing representative algorithms. Although LPDGL is designed for transduction purpose, we show that it can be easily extended to inductive settings. In Chapter 3, we develop an iterative label propagation approach, called “Fick’s Law Assisted Propagation” (FLAP), for robust graph transduction. To be specific, we regard label propagation on the graph as the practical fluid diffusion on a plane, and develop a novel label propagation algorithm by utilizing a well-known physical theory called Fick’s Law of Diffusion. Different from existing machine learning models that are based on some heuristic principles, FLAP conducts label propagation in a “natural” way, namely when and how much label information is received or transferred by an example, or where these labels should be propagated to, are naturally governed. As a consequence, FLAP not only yields more robust propagation results, but also requires less computational time than the existing iterative methods. In Chapter 4, we propose a propagation framework called “Teaching-to-Learn and Learning-to-Teach” (TLLT), in which a “teacher” (i.e. a teaching algorithm) is introduced to guide the label propagation. Different from existing methods that equally treat all the unlabeled examples, in TLLT we assume that different examples have different classification difficulties, and their propagations should follow a simple-to-difficult sequence. As such, the previously “learned” simple examples can ease the learning for the subsequent more difficult examples, and thus these difficult examples can be correctly classified. In each iteration of propagation, the teacher will designate the simplest examples to the “learner” (i.e. a propagation algorithm). After “learning” these simplest examples, the learner will deliver a learning feedback to the teacher to assist it in choosing the next simplest examples. Due to the collaborative teaching and learning process, all the unlabeled examples are propagated in a well-organized sequence, which contributes to the improved performance over existing methods. In Chapter 5, we apply the TLLT framework proposed in Chapter 4 to accomplish saliency detection, so that the saliency values of all the superpixels are decided from simple superpixels to more difficult ones. The difficulty of a superpixel is judged by its informativity, individuality, inhomogeneity, and connectivity. As a result, our saliency detector generates manifest saliency maps, and outperforms baseline methods on the typical public datasets

Multi-modal curriculum learning for semi-supervised image classification

Semi-supervised image classification aims to classify a large quantity of unlabeled images by typically harnessing scarce labeled images. Existing semi-supervised methods often suffer from inadequate classification accuracy when encountering difficult yet critical images, such as outliers, because they treat all unlabeled images equally and conduct classifications in an imperfectly ordered sequence. In this paper, we employ the curriculum learning methodology by investigating the difficulty of classifying every unlabeled image. The reliability and the discriminability of these unlabeled images are particularly investigated for evaluating their difficulty. As a result, an optimized image sequence is generated during the iterative propagations, and the unlabeled images are logically classified from simple to difficult. Furthermore, since images are usually characterized by multiple visual feature descriptors, we associate each kind of features with a teacher, and design a multi-modal curriculum learning (MMCL) strategy to integrate the information from different feature modalities. In each propagation, each teacher analyzes the difficulties of the currently unlabeled images from its own modality viewpoint. A consensus is subsequently reached among all the teachers, determining the currently simplest images (i.e., a curriculum), which are to be reliably classified by the multi-modal learner. This well-organized propagation process leveraging multiple teachers and one learner enables our MMCL to outperform five state-of-the-art methods on eight popular image data sets

Learning to Reweight with Deep Interactions

Recently, the concept of teaching has been introduced into machine learning, in which a teacher model is used to guide the training of a student model (which will be used in real tasks) through data selection, loss function design, etc. Learning to reweight, which is a specific kind of teaching that reweights training data using a teacher model, receives much attention due to its simplicity and effectiveness. In existing learning to reweight works, the teacher model only utilizes shallow/surface information such as training iteration number and loss/accuracy of the student model from training/validation sets, but ignores the internal states of the student model, which limits the potential of learning to reweight. In this work, we propose an improved data reweighting algorithm, in which the student model provides its internal states to the teacher model, and the teacher model returns adaptive weights of training samples to enhance the training of the student model. The teacher model is jointly trained with the student model using meta gradients propagated from a validation set. Experiments on image classification with clean/noisy labels and neural machine translation empirically demonstrate that our algorithm makes significant improvement over previous methods.Comment: Accepted to AAAI-202

Replication strategies and the evolution of cooperation by exploitation

Introducing the concept of replication strategies this paper studies the evolution of cooperation in populations of agents whose offspring follow a social strategy that is determined by a parent's replication strategy. Importantly, social and replication strategies may differ, thus allowing parents to construct their own social niche, defined by the behaviour of their offspring. We analyse the co-evolution of social and replication strategies in well-mixed and spatial populations. In well-mixed populations, cooperation-supporting equilibria can only exist if the transmission processes of social strategies and replication strategies are completely separate. In space, cooperation can evolve without complete separation of the timescales at which both strategy traits are propagated. Cooperation then evolves through the presence of offspring exploiting defectors whose presence and spatial arrangement can shield clusters of pure cooperators