Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments

The feasibility of deep neural networks (DNNs) to address data stream problems still requires intensive study because of the static and offline nature of conventional deep learning approaches. A deep continual learning algorithm, namely autonomous deep learning (ADL), is proposed in this paper. Unlike traditional deep learning methods, ADL features a flexible structure where its network structure can be constructed from scratch with the absence of an initial network structure via the self-constructing network structure. ADL specifically addresses catastrophic forgetting by having a different-depth structure which is capable of achieving a trade-off between plasticity and stability. Network significance (NS) formula is proposed to drive the hidden nodes growing and pruning mechanism. Drift detection scenario (DDS) is put forward to signal distributional changes in data streams which induce the creation of a new hidden layer. The maximum information compression index (MICI) method plays an important role as a complexity reduction module eliminating redundant layers. The efficacy of ADL is numerically validated under the prequential test-then-train procedure in lifelong environments using nine popular data stream problems. The numerical results demonstrate that ADL consistently outperforms recent continual learning methods while characterizing the automatic construction of network structures

Discrete MDL Predicts in Total Variation

The Minimum Description Length (MDL) principle selects the model that has the shortest code for data plus model. We show that for a countable class of models, MDL predictions are close to the true distribution in a strong sense. The result is completely general. No independence, ergodicity, stationarity, identifiability, or other assumption on the model class need to be made. More formally, we show that for any countable class of models, the distributions selected by MDL (or MAP) asymptotically predict (merge with) the true measure in the class in total variation distance. Implications for non-i.i.d. domains like time-series forecasting, discriminative learning, and reinforcement learning are discussed.Comment: 15 LaTeX page

Learning from Data Streams with Randomized Forests

Non-stationary streaming data poses a familiar challenge in machine learning: the need to obtain fast and accurate predictions. A data stream is a continuously generated sequence of data, with data typically arriving rapidly. They are often characterised by a non-stationary generative process, with concept drift occurring as the process changes. Such processes are commonly seen in the real world, such as in advertising, shopping trends, environmental conditions, electricity monitoring and traffic monitoring. Typical stationary algorithms are ill-suited for use with concept drifting data, thus necessitating more targeted methods. Tree-based methods are a popular approach to this problem, traditionally focussing on the use of the Hoeffding bound in order to guarantee performance relative to a stationary scenario. However, there are limited single learners available for regression scenarios, and those that do exist often struggle to choose between similarly discriminative splits, leading to longer training times and worse performance. This limited pool of single learners in turn hampers the performance of ensemble approaches in which they act as base learners. In this thesis we seek to remedy this gap in the literature, developing methods which focus on increasing randomization to both improve predictive performance and reduce the training times of tree-based ensemble methods. In particular, we have chosen to investigate the use of randomization as it is known to be able to improve generalization error in ensembles, and is also expected to lead to fast training times, thus being a natural method of handling the problems typically experienced by single learners. We begin in a regression scenario, introducing the Adaptive Trees for Streaming with Extreme Randomization (ATSER) algorithm; a partially randomized approach based on the concept of Extremely Randomized (extra) trees. The ATSER algorithm incrementally trains trees, using the Hoeffding bound to select the best of a random selection of splits. Simultaneously, the trees also detect and adapt to changes in the data stream. Unlike many traditional streaming algorithms ATSER trees can easily be extended to include nominal features. We find that compared to other contemporary methods ensembles of ATSER trees lead to improved predictive performance whilst also reducing run times. We then demonstrate the Adaptive Categorisation Trees for Streaming with Extreme Randomization (ACTSER) algorithm, an adaption of the ATSER algorithm to the more traditional categorization scenario, again showing improved predictive performance and reduced runtimes. The inclusion of nominal features is particularly novel in this setting since typical categorization approaches struggle to handle them. Finally we examine a completely randomized scenario, where an ensemble of trees is generated prior to having access to the data stream, while also considering multivariate splits in addition to the traditional axis-aligned approach. We find that through the combination of a forgetting mechanism in linear models and dynamic weighting for ensemble members, we are able to avoid explicitly testing for concept drift. This leads to fast ensembles with strong predictive performance, whilst also requiring fewer parameters than other contemporary methods. For each of the proposed methods in this thesis, we demonstrate empirically that they are effective over a variety of different non-stationary data streams, including on multiple types of concept drift. Furthermore, in comparison to other contemporary data streaming algorithms, we find the biggest improvements in performance are on noisy data streams.Engineers Gat

Bitcoin Volatility Forecasting with a Glimpse into Buy and Sell Orders

In this paper, we study the ability to make the short-term prediction of the exchange price fluctuations towards the United States dollar for the Bitcoin market. We use the data of realized volatility collected from one of the largest Bitcoin digital trading offices in 2016 and 2017 as well as order information. Experiments are performed to evaluate a variety of statistical and machine learning approaches.Comment: Full version of the paper published at IEEE International Conference on Data Mining (ICDM), 201

Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning

Intrinsically motivated spontaneous exploration is a key enabler of autonomous lifelong learning in human children. It enables the discovery and acquisition of large repertoires of skills through self-generation, self-selection, self-ordering and self-experimentation of learning goals. We present an algorithmic approach called Intrinsically Motivated Goal Exploration Processes (IMGEP) to enable similar properties of autonomous or self-supervised learning in machines. The IMGEP algorithmic architecture relies on several principles: 1) self-generation of goals, generalized as fitness functions; 2) selection of goals based on intrinsic rewards; 3) exploration with incremental goal-parameterized policy search and exploitation of the gathered data with a batch learning algorithm; 4) systematic reuse of information acquired when targeting a goal for improving towards other goals. We present a particularly efficient form of IMGEP, called Modular Population-Based IMGEP, that uses a population-based policy and an object-centered modularity in goals and mutations. We provide several implementations of this architecture and demonstrate their ability to automatically generate a learning curriculum within several experimental setups including a real humanoid robot that can explore multiple spaces of goals with several hundred continuous dimensions. While no particular target goal is provided to the system, this curriculum allows the discovery of skills that act as stepping stone for learning more complex skills, e.g. nested tool use. We show that learning diverse spaces of goals with intrinsic motivations is more efficient for learning complex skills than only trying to directly learn these complex skills