Author name cluster

Ban Kawas

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

2 author rows

AAAI Conference 2021 System Paper

AutoText: An End-to-End AutoAI Framework for Text

Arunima Chaudhary
Alayt Issak
Kiran Kate
Yannis Katsis
Abel Valente
Dakuo Wang
Alexandre Evfimievski
Sairam Gurajada

Building models for natural language processing (NLP) tasks remains a daunting task for many, requiring significant technical expertise, efforts, and resources. In this demonstration, we present AutoText, an end-to-end AutoAI framework for text, to lower the barrier of entry in building NLP models. AutoText combines state-of-the-art AutoAI optimization techniques and learning algorithms for NLP tasks into a single extensible framework. Through its simple, yet powerful UI, non-AI experts (e. g. , domain experts) can quickly generate performant NLP models with support to both control (e. g. , via specifying constraints) and understand learned models.

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AAAI Conference 2017 Conference Paper

Robust Partially-Compressed Least-Squares

Stephen Becker
Ban Kawas
Marek Petrik

Randomized matrix compression techniques, such as the Johnson-Lindenstrauss transform, have emerged as an effective and practical way for solving large-scale problems efﬁciently. With a focus on computational efﬁciency, however, forsaking solutions quality and accuracy becomes the tradeoff. In this paper, we investigate compressed least-squares problems and propose new models and algorithms that address the issue of error and noise introduced by compression. While maintaining computational efﬁciency, our models provide robust solutions that are more accurate than those of classical compressed variants. We introduce tools from robust optimization together with a form of partial compression to improve the error-time trade-offs of compressed least-squares solvers. We develop an efﬁcient solution algorithm for our Robust Partially-Compressed (RPC) model based on a reduction to a one-dimensional search.

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ECAI Conference 2014 Conference Paper

Symmetry Breaking for Exact Solutions in Adjustable Robust Optimisation

Steven D. Prestwich
Marco Laumanns
Ban Kawas

One of the key unresolved challenges in Adjustable Robust Optimisation is how to deal with large discrete uncertainty sets. In this paper we present a technique for handling such sets based on symmetry breaking ideas from Constraint Programming. In earlier work we applied the technique to a pre-disaster planning problem modelled as a two-stage Stochastic Program, and we were able to solve exactly instances that were previously considered intractable and only had approximate solutions. In this paper we show that the technique can also be applied to an adjustable robust formulation that scales up to larger instances than the stochastic formulation. We also describe a new fast symmetry breaking heuristic that gives improved results.

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