Multi-armed bandits and applications to large datasets

This thesis considers the multi-armed bandit (MAB) problem, both the traditional bandit feedback and graphical bandits when there is side information. Motivated by the Boltzmann exploration algorithm often used in the more general context of reinforcement learning, we present Almost Boltzmann Exploration (ABE) which fixes the under-exploration issue while maintaining an expression similar to Boltzmann exploration. We then present some real world applications of the MAB framework, comparing the performance of ABE with other bandit algorithms on real world datasets

A Provably Improved Algorithm for Crowdsourcing with Hard and Easy Tasks

Crowdsourcing is a popular method used to estimate ground-truth labels by collecting noisy labels from workers. In this work, we are motivated by crowdsourcing applications where each worker can exhibit two levels of accuracy depending on a task's type. Applying algorithms designed for the traditional Dawid-Skene model to such a scenario results in performance which is limited by the hard tasks. Therefore, we first extend the model to allow worker accuracy to vary depending on a task's unknown type. Then we propose a spectral method to partition tasks by type. After separating tasks by type, any Dawid-Skene algorithm (i.e., any algorithm designed for the Dawid-Skene model) can be applied independently to each type to infer the truth values. We theoretically prove that when crowdsourced data contain tasks with varying levels of difficulty, our algorithm infers the true labels with higher accuracy than any Dawid-Skene algorithm. Experiments show that our method is effective in practical applications

A Neural Pre-Conditioning Active Learning Algorithm to Reduce Label Complexity

Deep learning (DL) algorithms rely on massive amounts of labeled data. Semi-supervised learning (SSL) and active learning (AL) aim to reduce this label complexity by leveraging unlabeled data or carefully acquiring labels, respectively. In this work, we primarily focus on designing an AL algorithm but first argue for a change in how AL algorithms should be evaluated. Although unlabeled data is readily available in pool-based AL, AL algorithms are usually evaluated by measuring the increase in supervised learning (SL) performance at consecutive acquisition steps. Because this measures performance gains from both newly acquired instances and newly acquired labels, we propose to instead evaluate the label efficiency of AL algorithms by measuring the increase in SSL performance at consecutive acquisition steps. After surveying tools that can be used to this end, we propose our neural pre-conditioning (NPC) algorithm inspired by a Neural Tangent Kernel (NTK) analysis. Our algorithm incorporates the classifier's uncertainty on unlabeled data and penalizes redundant samples within candidate batches to efficiently acquire a diverse set of informative labels. Furthermore, we prove that NPC improves downstream training in the large-width regime in a manner previously observed to correlate with generalization. Comparisons with other AL algorithms show that a state-of-the-art SSL algorithm coupled with NPC can achieve high performance using very few labeled data.Comment: NeurIPS 202

Traffic engineering in data center networks

Commodities in data centers today are often connected in a switch-centric approach to reduce link crosspoints. Today's data centers consist of a large number of servers that need to communicate among another. When data rates approach the capacities of links, congestion may occur or transmission delays increase resulting in a decrease of network throughput. Hence, sophisticated routing schemes are necessary to maximize the throughput of the network. It is possible to compute the optimal routing scheme via linear programming. However, linear programming algorithms have insufficient computation complexity to be useful in large data centers. Proposed in this thesis are two randomized algorithms for load balancing that achieve a suboptimal routing scheme with significantly reduced runtime. The proposed algorithms demonstrate performance very close to the solution obtained by multi-path routing when computed through a convex-programming solver. Further, the runtimes are incomparably faster than the convex-programming solver, even when the solver utilizes multi-threading to compute the optimal solution.U of I OnlyUndergraduate senior thesis not recommended for open acces

Multi-armed bandits and applications to large datasets

This thesis considers the multi-armed bandit (MAB) problem, both the traditional bandit feedback and graphical bandits when there is side information. Motivated by the Boltzmann exploration algorithm often used in the more general context of reinforcement learning, we present Almost Boltzmann Exploration (ABE) which fixes the under-exploration issue while maintaining an expression similar to Boltzmann exploration. We then present some real world applications of the MAB framework, comparing the performance of ABE with other bandit algorithms on real world datasets.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste

Leveraging the Generalization Ability of Deep Convolutional Neural Networks for Improving Classifiers for Color Fundus Photographs

Deep learning demands a large amount of annotated data, and the annotation task is often crowdsourced for economic efficiency. When the annotation task is delegated to non-experts, the dataset may contain data with inaccurate labels. Noisy labels not only yield classification models with sub-optimal performance, but may also impede their optimization dynamics. In this work, we propose exploiting the pattern recognition capacity of deep convolutional neural networks to filter out supposedly mislabeled cases while training. We suggest a training method that references softmax outputs to judge the correctness of the given labels. This approach achieved outstanding performance compared to the existing methods in various noise settings on a large-scale dataset (Kaggle 2015 Diabetic Retinopathy). Furthermore, we demonstrate a method mining positive cases from a pool of unlabeled images by exploiting the generalization ability. With this method, we won first place on the offsite validation dataset in pathological myopia classification challenge (PALM), achieving the AUROC of 0.9993 in the final submission. Source codes are publicly available

Key Feature Replacement of In-Distribution Samples for Out-of-Distribution Detection

Out-of-distribution (OOD) detection can be used in deep learning-based applications to reject outlier samples from being unreliably classified by deep neural networks. Learning to classify between OOD and in-distribution samples is difficult because data comprising the former is extremely diverse. It has been observed that an auxiliary OOD dataset is most effective in training a ``rejection'' network when its samples are semantically similar to in-distribution images. We first deduce that OOD images are perceived by a deep neural network to be semantically similar to in-distribution samples when they share a common background, as deep networks are observed to incorrectly classify such images with high confidence. We then propose a simple yet effective Key In-distribution feature Replacement BY inpainting (KIRBY) procedure that constructs a surrogate OOD dataset by replacing class-discriminative features of in-distribution samples with marginal background features. The procedure can be implemented using off-the-shelf vision algorithms, where each step within the algorithm is shown to make the surrogate data increasingly similar to in-distribution data. Design choices in each step are studied extensively, and an exhaustive comparison with state-of-the-art algorithms demonstrates KIRBY's competitiveness on various benchmarks

Leveraging the Generalization Ability of Deep Convolutional Neural Networks for Improving Classifiers for Color Fundus Photographs

Deep learning demands a large amount of annotated data, and the annotation task is often crowdsourced for economic efficiency. When the annotation task is delegated to non-experts, the dataset may contain data with inaccurate labels. Noisy labels not only yield classification models with sub-optimal performance, but may also impede their optimization dynamics. In this work, we propose exploiting the pattern recognition capacity of deep convolutional neural networks to filter out supposedly mislabeled cases while training. We suggest a training method that references softmax outputs to judge the correctness of the given labels. This approach achieved outstanding performance compared to the existing methods in various noise settings on a large-scale dataset (Kaggle 2015 Diabetic Retinopathy). Furthermore, we demonstrate a method mining positive cases from a pool of unlabeled images by exploiting the generalization ability. With this method, we won first place on the offsite validation dataset in pathological myopia classification challenge (PALM), achieving the AUROC of 0.9993 in the final submission. Source codes are publicly available

Ultra-soft and highly stretchable tissue-adhesive hydrogel based multifunctional implantable sensor for monitoring of overactive bladder

A highly stretchable and tissue-adhesive multifunctional sensor based on structurally engineered islets embedded in ultra-soft hydrogel is reported for monitoring of bladder activity in overactive bladder (OAB) induced rat and anesthetized pig. The use of hydrogel yielded a much lower sensor modulus (1 kPa) compared to that of the bladder (300 kPa), while the strong adhesiveness of the hydrogel (adhesive strength: 260.86 N/m) allowed firm attachment onto the bladder. The change in resistance of printed liquid metal particle thin-film lines under strain were used to detect bladder inflation and deflation; due to the high stretchability and reliability of the lines, surface strains of 200% could be measured repeatedly. Au electrodes coated with Platinum black were used to detect electromyography (EMG). These electrodes were placed on structurally engineered rigid islets so that no interfacial fracture occurs under high strains associated with bladder expansion. On the OAB induced rat, stronger signals (change in resistance and EMG root-mean-square) were detected near intra-bladder pressure maxima, thus showing correlation to bladder activity. Moreover, using robot-assisted laparoscopic surgery, the sensor was placed onto the bladder of an anesthetized pig. Under voiding and filling, bladder strain and EMG were once again monitored. These results confirm that our proposed sensor is a highly feasible, clinically relevant implantable device for continuous monitoring OAB for diagnosis and treatment.11Nsciescopu