Private and Efficient Meta-Learning with Low Rank and Sparse Decomposition

Meta-learning is critical for a variety of practical ML systems -- like personalized recommendations systems -- that are required to generalize to new tasks despite a small number of task-specific training points. Existing meta-learning techniques use two complementary approaches of either learning a low-dimensional representation of points for all tasks, or task-specific fine-tuning of a global model trained using all the tasks. In this work, we propose a novel meta-learning framework that combines both the techniques to enable handling of a large number of data-starved tasks. Our framework models network weights as a sum of low-rank and sparse matrices. This allows us to capture information from multiple domains together in the low-rank part while still allowing task specific personalization using the sparse part. We instantiate and study the framework in the linear setting, where the problem reduces to that of estimating the sum of a rank-$r$ and a $k$-column sparse matrix using a small number of linear measurements. We propose an alternating minimization method with hard thresholding -- AMHT-LRS -- to learn the low-rank and sparse part effectively and efficiently. For the realizable, Gaussian data setting, we show that AMHT-LRS indeed solves the problem efficiently with nearly optimal samples. We extend AMHT-LRS to ensure that it preserves privacy of each individual user in the dataset, while still ensuring strong generalization with nearly optimal number of samples. Finally, on multiple datasets, we demonstrate that the framework allows personalized models to obtain superior performance in the data-scarce regime.Comment: 97 pages, 3 figure

Spotcheck: Designing a derivative iaas cloud on the spot market

Abstract Infrastructure-as-a-Service (IaaS) cloud platforms rent resources, in the form of virtual machines (VMs), under a variety of contract terms that offer different levels of risk and cost. For example, users may acquire VMs in the spot market that are often cheap but entail significant risk, since their price varies over time based on market supply and demand and they may terminate at any time if the price rises too high. Currently, users must manage all the risks associated with using spot servers. As a result, conventional wisdom holds that spot servers are only appropriate for delay-tolerant batch applications. In this paper, we propose a derivative cloud platform, called SpotCheck, that transparently manages the risks associated with using spot servers for users. SpotCheck provides the illusion of an IaaS platform that offers always-available VMs on demand for a cost near that of spot servers, and supports all types of applications, including interactive ones. SpotCheck's design combines the use of nested VMs with live bounded-time migration and novel server pool management policies to maximize availability, while balancing risk and cost. We implement SpotCheck on Amazon's EC2 and show that it i) provides nested VMs to users that are 99.9989% available, ii) achieves nearly 5× cost savings compared to using equivalent types of ondemand VMs, and iii) eliminates any risk of losing VM state

Surgical Phase Recognition in Laparoscopic Cholecystectomy

Automatic recognition of surgical phases in surgical videos is a fundamental task in surgical workflow analysis. In this report, we propose a Transformer-based method that utilizes calibrated confidence scores for a 2-stage inference pipeline, which dynamically switches between a baseline model and a separately trained transition model depending on the calibrated confidence level. Our method outperforms the baseline model on the Cholec80 dataset, and can be applied to a variety of action segmentation methods

Safety of 6000 intravitreal dexamethasone implants

Purpose To evaluate the real-life safety profile of intravitreal dexamethasone implant injection for various retinal conditions. Methods Retrospective multicenter analysis of intravitreal dexamethasone implant injections (700 mu g) due to various retinal conditions including central retinal venous occlusion (1861 injections), diabetic macular oedema (3104 injections), post-surgical cystoid macular oedema (305 injections) and uveitis (381 injections). The eyes were evaluated mainly for the occurrence of adverse events such as glaucoma, cataract, retinal detachment and endophthalmitis along during the follow-up period. Results A total of 6015 injections in 2736 eyes of 1441 patients (mean age of 65.7 +/- 12.9 years) were in total analysed over an average period of 18 months (range 6 months to 102 months). A total of 576 eyes (32.5% of the phakic eyes) developed cataract requiring surgical intervention. However, visually insignificant cataract progression was observed in another 259 phakic eyes (14.6%) which did not require surgical removal. A total of 727 eyes (26.5%) experienced an intraocular pressure (IOP) rise of >25 mm Hg, with 155 eyes (5.67%) having a prior history of glaucoma and 572 eyes (20.9%) having new onset IOP rise. Overall, more than 90% of eyes with IOP rise were managed medically, and 0.5% eyes required filtering surgery. Endophthalmitis (0.07%), retinal detachment (0.03%) and vitreous haemorrhage (0.03%) were rare. There was no significant change in visual acuity (p=0.87) and central macular thickness (p=0.12) at the last follow-up. Conclusion This is the largest real-life study assessing the safety of intravitreal dexamethasone implant injections in various retinal conditions. Cataract progression and intraocular pressure rise are the most common side effects, but are often rather easily manageable

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset