arXiv subjects

Avraham Shinnar

Publications and source records attributed to Avraham Shinnar.

23 records · Page 2Linked to original sources

arXiv2019

Type-Driven Automated Learning with Lale

Machine-learning automation tools, ranging from humble grid-search to hyperopt, auto-sklearn, and TPOT, help explore large search spaces of possible pipelines. Unfortunately, each of these tools has a different syntax for specifying its search space, leading to lack of portability, missed relevant points, and spurious points that are inconsistent with error checks and documentation of the searchable base components. This paper proposes using types (such as enum, float, or dictionary) both for checking the correctness of, and for automatically searching over, hyperparameters and pipeline configurations. Using types for both of these purposes guarantees consistency. We present Lale, an embedded language that resembles scikit learn but provides better automation, correctness checks, and portability. Lale extends the reach of existing automation tools across data modalities (tables, text, images, time-series) and programming languages (Python, Java, R). Thus, data scientists can leverage automation while remaining in control of their work.

cs.PL↗

arXiv2018

Yaps: Python Frontend to Stan

Stan is a popular probabilistic programming language with a self-contained syntax and semantics that is close to graphical models. Unfortunately, existing embeddings of Stan in Python use multi-line strings. That approach forces users to switch between two different language styles, with no support for syntax highlighting or simple error reporting within the Stan code. This paper tackles the question of whether Stan could use Python syntax while retaining its self-contained semantics. The answer is yes, that can be accomplished by reinterpreting the Python syntax. This paper introduces Yaps, a new frontend to Stan based on reinterpreted Python. We tested Yaps on over a thousand Stan models and made it available open-source.

cs.PL↗

arXiv2018

Ariadne: Analysis for Machine Learning Program

Machine learning has transformed domains like vision and translation, and is now increasingly used in science, where the correctness of such code is vital. Python is popular for machine learning, in part because of its wealth of machine learning libraries, and is felt to make development faster; however, this dynamic language has less support for error detection at code creation time than tools like Eclipse. This is especially problematic for machine learning: given its statistical nature, code with subtle errors may run and produce results that look plausible but are meaningless. This can vitiate scientific results. We report on Ariadne: applying a static framework, WALA, to machine learning code that uses TensorFlow. We have created static analysis for Python, a type system for tracking tensors---Tensorflow's core data structures---and a data flow analysis to track their usage. We report on how it was built and present some early results.

cs.PL↗

arXiv2013

Semantics of (Resilient) X10

We present a formal small-step structural operational semantics for a large fragment of X10, unifying past work. The fragment covers multiple places, mutable objects on the heap, sequencing, \code{try/catch}, \code{async}, \code{finish}, and \code{at} constructs. This model accurately captures the behavior of a large class of concurrent, multi-place X10 programs. Further, we introduce a formal model of resilience in X10. During execution of an X10 program, a place may fail for many reasons. Resilient X10 permits the program to continue executing, losing the data at the failed place, and most of the control state, and repairing the global control state in such a way that key semantic principles hold, the Invariant Happens Before Principle, and the Failure Masking Principle. These principles permit an X10 programmer to write clean code that continues to work in the presence of place failure. The given semantics have additionally been mechanized in Coq.

cs.PL↗

arXiv2012

M3R: Increased performance for in-memory Hadoop jobs

Main Memory Map Reduce (M3R) is a new implementation of the Hadoop Map Reduce (HMR) API targeted at online analytics on high mean-time-to-failure clusters. It does not support resilience, and supports only those workloads which can fit into cluster memory. In return, it can run HMR jobs unchanged -- including jobs produced by compilers for higher-level languages such as Pig, Jaql, and SystemML and interactive front-ends like IBM BigSheets -- while providing significantly better performance than the Hadoop engine on several workloads (e.g. 45x on some input sizes for sparse matrix vector multiply). M3R also supports extensions to the HMR API which can enable Map Reduce jobs to run faster on the M3R engine, while not affecting their performance under the Hadoop engine.

cs.DB↗