A Theory of Generative Models and Robustness via Regularization

Regularization in Machine Learning (ML) is a central technique with great practical significance, whose motivations depend on the learning setting. For example, the popular entropic regularization scheme for Reinforcement Learning (RL) is used to aid in disambiguating optimal policies. On the other hand, Generative Adversarial Networks (GANs) employ regularization for computational purposes, avoiding instability in training. Despite the widespread use and multi-faceted motivation of regularization, extensive evidence has suggested that regularization is a crucial method towards empirical success. Therefore, it is natural that a formal study of regularization would present results that aid in closing the gap between theory and practice. In this thesis, we study a range of different learning problems from the unifying perspective of regularization and uncover various results that contribute to our understanding of machine learning methods. First, we focus on generative modelling and discover a primal-dual relationship between two pioneering methods in the literature of generative modelling, namely Generative Adversarial Networks (GANs) and Autoencoders. The discovery not only explicates a bridge between existing results but proves to be helpful in algorithmic guidance. The study on generative models is then extended to build a boosting-based model that can generate samples compliant with local differential privacy. We then focus on machine learning robustness, where one is interested in understanding the susceptibility of a model in the face of adversarial threats. We show that regularization is intimately connected to distributional robustness, which subsumes existing results and extends them to a great deal of generality, including applications to the unsupervised learning setting. We continue this narrative to the RL setting and similarly expose the robustifying benefits of using regularization, which sheds light on the widely-used entropy-regularized schemes, amongst others. In summary, this thesis's study of regularization contributes substantially within the literature of generative modelling, machine learning robustness, and RL while touching upon additional domains such as privacy and boosting

Data Preprocessing to Mitigate Bias with Boosted Fair Mollifiers

In a recent paper, Celis et al. (2020) introduced a new approach to fairness that corrects the data distribution itself. The approach is computationally appealing, but its approximation guarantees with respect to the target distribution can be quite loose as they need to rely on a (typically limited) number of constraints on data-based aggregated statistics; also resulting in a fairness guarantee which can be data dependent. Our paper makes use of a mathematical object recently introduced in privacy -- mollifiers of distributions -- and a popular approach to machine learning -- boosting -- to get an approach in the same lineage as Celis et al. but without the same impediments, including in particular, better guarantees in terms of accuracy and finer guarantees in terms of fairness. The approach involves learning the sufficient statistics of an exponential family. When the training data is tabular, the sufficient statistics can be defined by decision trees whose interpretability can provide clues on the source of (un)fairness. Experiments display the quality of the results for simulated and real-world data

A Novel Hybrid Notch (HN) Substrate Integrated Waveguide (SIW) Bandstop Filter

The advent of substrate integrated waveguide has seen an influx of researches on the study and design of microwave filters employing such a technique[1]-[4]. This technique provides an excellent avenue to design millimeter wave circuits such as filters, resonators and antennae [5]. A great advantage is that these devices can be easily connected to other planar microwave transmission lines and devices by using very simple transitions [6]. While many researches on SIW primarily focused on bandpass filters, researches on SIW bandstop filters for the GHz frequency ranges are gaining momentum working on the big list of advantages of SIW over microstrips. This paper presents the analysis and design of a novel Hybrid Notch Bandstop Filter working in the X-Band of the Frequency Spectrum

Application of matlab-based interface for the control of microbioreactor operation

This work presents the use of Arduino-based embedded system interfaced to MATLAB software packages as an alternative cost-effective solution for the control of the microbioreactor operation. In the presented work, a microbioreactor platform with a working volume of approximately 1.5 mL have been fabricated using a low-cost poly (methylmethacrylate) (PMMA) and poly(dimethylsiloxane) (PDMS) polymers. The reactor have been integrated with stirring control, fuzzy logic temperature control, and aeration feature via a miniature air compressor. Control program of the microbioreactor system was established using Simulink, MATLAB software were executed by interfacing the program with Arduino Mega 2560 microcontroller for input and output of signals. Numbers of experimentation were performed to validate and demonstrate the potential of the proposed method. Satisfactorily degree of control and supervision was achieved (± 1-3% of the set-point values). The entire microbioreactor system can be operated stably for a least 48 hours. The work demonstrated the usefulness of MATLAB software in establishing a microbioreactor operating interface that consisted merely few Simulink program block sets and executed on a low-cost Arduino board

Antibacterial activity of ethanol extract of Sargassum polycystum against Streptococcus mutans and Lactobacillus casei: in vitro

Introduction: Sargassum polycystum (brown seaweeds) has been recognized as a potential source of natural antibacterial and antimicrobial compounds. It has been used in many applications and products as they possess interesting biological activities that give benefit to the dental and medical area. Aims: The objectives of this study were to determine the growth curve of Streptococcus mutans and Lactobacillus casei, and also to determine minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) value of Sargassum polycystum extract against Streptococcus mutans and Lactobacillus casei. Materials and methods: The growth of both bacteria were studied by growth curve analysis. Besides that, the antibacterial activity of Sargassum polycystum ethanol extracts against two oral cariogenic bacteria were examined using broth dilution method. The bioactivity of the seaweed extract was studied by finding the MIC and MBC value. Results: The time taken for the Streptococcus mutans and Lactobacillus casei to achieve exponential phase during growth is 4 and 5 hours respectively. MIC values of ethanol extracts of Sargassum polycystum in different concentration against Streptococcus mutans and Lactobacillus casei were unreliable because the presence of fungus (white-precipitate) at all 96-well plates including the positive and negative control. As there was no MIC value, partial inhibition was determined by calculating the optical density. Therefore, MBC analysis was unable to carry out because the MIC values were unreliable. Conclusion: The ethanol extract of Sargassum polycystum used in this study show partial bacterial inhibition against pathogens used. Therefore, the same study by using different type of solvent are recommended to be carried out in the future