MLI: An API for Distributed Machine Learning

MLI is an Application Programming Interface designed to address the challenges of building Machine Learn- ing algorithms in a distributed setting based on data-centric computing. Its primary goal is to simplify the development of high-performance, scalable, distributed algorithms. Our initial results show that, relative to existing systems, this interface can be used to build distributed implementations of a wide variety of common Machine Learning algorithms with minimal complexity and highly competitive performance and scalability

Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability

We empirically demonstrate that full-batch gradient descent on neural network training objectives typically operates in a regime we call the Edge of Stability. In this regime, the maximum eigenvalue of the training loss Hessian hovers just above the numerical value $2 / \text{(step size)}$, and the training loss behaves non-monotonically over short timescales, yet consistently decreases over long timescales. Since this behavior is inconsistent with several widespread presumptions in the field of optimization, our findings raise questions as to whether these presumptions are relevant to neural network training. We hope that our findings will inspire future efforts aimed at rigorously understanding optimization at the Edge of Stability. Code is available at https://github.com/locuslab/edge-of-stability.Comment: To appear in ICLR 2021. 72 pages, 107 figure

On landmark selection and sampling in high-dimensional data analysis

In recent years, the spectral analysis of appropriately defined kernel matrices has emerged as a principled way to extract the low-dimensional structure often prevalent in high-dimensional data. Here we provide an introduction to spectral methods for linear and nonlinear dimension reduction, emphasizing ways to overcome the computational limitations currently faced by practitioners with massive datasets. In particular, a data subsampling or landmark selection process is often employed to construct a kernel based on partial information, followed by an approximate spectral analysis termed the Nystrom extension. We provide a quantitative framework to analyse this procedure, and use it to demonstrate algorithmic performance bounds on a range of practical approaches designed to optimize the landmark selection process. We compare the practical implications of these bounds by way of real-world examples drawn from the field of computer vision, whereby low-dimensional manifold structure is shown to emerge from high-dimensional video data streams.Comment: 18 pages, 6 figures, submitted for publicatio

Small Molecule Drug Release Form in Situ Forming Degradable Scaffolds Incorporating Hydrogels and Bioceramic Microparticles

The present invention relates to an injectable system combining a hydrogel, a bioceramic and a degradable matrix that provides for sustained drug delivery and structural support to recovering tissue, such as bone and the periodontium

Reliability of causality assessment for drug, herbal and dietary supplement hepatotoxicity in the Drug‐Induced Liver Injury Network (DILIN)

Background & AimsBecause of the lack of objective tests to diagnose drug‐induced liver injury (DILI), causality assessment is a matter of debate. Expert opinion is often used in research and industry, but its test–retest reliability is unknown. To determine the test–retest reliability of the expert opinion process used by the Drug‐Induced Liver Injury Network (DILIN).MethodsThree DILIN hepatologists adjudicate suspected hepatotoxicity cases to one of five categories representing levels of likelihood of DILI. Adjudication is based on retrospective assessment of gathered case data that include prospective follow‐up information. One hundred randomly selected DILIN cases were re‐assessed using the same processes for initial assessment but by three different reviewers in 92% of cases.ResultsThe median time between assessments was 938 days (range 140–2352). Thirty‐one cases involved >1 agent. Weighted kappa statistics for overall case and individual agent category agreement were 0.60 (95% CI: 0.50–0.71) and 0.60 (0.52–0.68) respectively. Overall case adjudications were within one category of each other 93% of the time, while 5% differed by two categories and 2% differed by three categories. Fourteen per cent crossed the 50% threshold of likelihood owing to competing diagnoses or atypical timing between drug exposure and injury.ConclusionsThe DILIN expert opinion causality assessment method has moderate interobserver reliability but very good agreement within one category. A small but important proportion of cases could not be reliably diagnosed as ≥50% likely to be DILI.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111130/1/liv12540.pd

Low-Rank Subspace Override for Unsupervised Domain Adaptation

Current supervised learning models cannot generalize well across domain boundaries, which is a known problem in many applications, such as robotics or visual classification. Domain adaptation methods are used to improve these generalization properties. However, these techniques suffer either from being restricted to a particular task, such as visual adaptation, require a lot of computational time and data, which is not always guaranteed, have complex parameterization, or expensive optimization procedures. In this work, we present an approach that requires only a well-chosen snapshot of data to find a single domain invariant subspace. The subspace is calculated in closed form and overrides domain structures, which makes it fast and stable in parameterization. By employing low-rank techniques, we emphasize on descriptive characteristics of data. The presented idea is evaluated on various domain adaptation tasks such as text and image classification against state of the art domain adaptation approaches and achieves remarkable performance across all tasks

Liver injury from herbals and dietary supplements in the U.S. Drug‐Induced Liver Injury Network

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/108649/1/hep27317.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/108649/2/hep27317-sup-0001-supptbl1.pd

Hepatic histological findings in suspected drug‐induced liver injury: Systematic evaluation and clinical associations

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102713/1/hep26709.pd