47 citations · 115 across the 14 of their papers we have counts for
15 papers
Auto-Differentiation of Relational Computations for Very Large Scale Machine Learning
Yuxin Tang, Zhimin Ding, Dimitrije Jankov +3
The relational data model was designed to facilitate large-scale data management and analytics. We consider the problem of how to differentiate computations expressed relationally.…
LOFT: Finding Lottery Tickets through Filter-wise Training
Qihan Wang, Chen Dun, Fangshuo Liao +2
Recent work on the Lottery Ticket Hypothesis (LTH) shows that there exist ``\textit{winning tickets}'' in large neural networks. These tickets represent ``sparse'' versions of the…
Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout
Chen Dun, Mirian Hipolito, Chris Jermaine +2
Asynchronous learning protocols have regained attention lately, especially in the Federated Learning (FL) setup, where slower clients can severely impede the learning process. Here…
Neural Program Generation Modulo Static Analysis
Rohan Mukherjee, Yeming Wen, Dipak Chaudhari +3
State-of-the-art neural models of source code tend to be evaluated on the generation of individual expressions and lines of code, and commonly fail on long-horizon tasks such as th…
Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifier
Arkabandhu Chowdhury, Mingchao Jiang, Swarat Chaudhuri +1
Recent papers have suggested that transfer learning can outperform sophisticated meta-learning methods for few-shot image classification. We take this hypothesis to its logical con…
Tensor Relational Algebra for Machine Learning System Design
Binhang Yuan, Dimitrije Jankov, Jia Zou +3
We consider the question: what is the abstraction that should be implemented by the computational engine of a machine learning system? Current machine learning systems typically pu…
Meta-Meta Classification for One-Shot Learning
Arkabandhu Chowdhury, Dipak Chaudhari, Swarat Chaudhuri +1
We present a new approach, called meta-meta classification, to learning in small-data settings. In this approach, one uses a large set of learning problems to design an ensemble of…
Searching a Database of Source Codes Using Contextualized Code Search
Rohan Mukherjee, Swarat Chaudhuri, Chris Jermaine
Consider the case where a programmer has written some part of a program, but has left part of the program (such as a method or a function body) incomplete. The goal is to use the c…
Declarative Recursive Computation on an RDBMS, or, Why You Should Use a Database For Distributed Machine Learning
Dimitrije Jankov, Shangyu Luo, Binhang Yuan +4
A number of popular systems, most notably Google's TensorFlow, have been implemented from the ground up to support machine learning tasks. We consider how to make a very small set…
Pangea: Monolithic Distributed Storage for Data Analytics
Jia Zou, Arun Iyengar, Chris Jermaine
Storage and memory systems for modern data analytics are heavily layered, managing shared persistent data, cached data, and non-shared execution data in separate systems such as di…
PlinyCompute: A Platform for High-Performance, Distributed, Data-Intensive Tool Development
Jia Zou, R. Matthew Barnett, Tania Lorido-Botran +6
This paper describes PlinyCompute, a system for development of high-performance, data-intensive, distributed computing tools and libraries. In the large, PlinyCompute presents the…
Data-Driven Program Completion
Yanxin Lu, Swarat Chaudhuri, Chris Jermaine +1
We introduce program splicing, a programming methodology that aims to automate the commonly used workflow of copying, pasting, and modifying code available online. Here, the progra…
Finding Likely Errors with Bayesian Specifications
Vijayaraghavan Murali, Swarat Chaudhuri, Chris Jermaine
We present a Bayesian framework for learning probabilistic specifications from large, unstructured code corpora, and a method to use this framework to statically detect anomalous,…
Neural Sketch Learning for Conditional Program Generation
Vijayaraghavan Murali, Letao Qi, Swarat Chaudhuri +1
We study the problem of generating source code in a strongly typed, Java-like programming language, given a label (for example a set of API calls or types) carrying a small amount…
A Sampling Algebra for Aggregate Estimation
Supriya Nirkhiwale, Alin Dobra, Chris Jermaine
As of 2005, sampling has been incorporated in all major database systems. While efficient sampling techniques are realizable, determining the accuracy of an estimate obtained from…