Search Results for author: Sameena Shah

Found 18 papers, 1 papers with code

Bandit Sampling for Multiplex Networks

no code implementations8 Feb 2022 Cenk Baykal, Vamsi K. Potluru, Sameena Shah, Manuela M. Veloso

Most of the existing work focuses primarily on the monoplex setting where we have access to a network with only a single type of connection between entities.

Link Prediction Node Classification +1

Structure and Semantics Preserving Document Representations

no code implementations11 Jan 2022 Natraj Raman, Sameena Shah, Manuela Veloso

Retrieving relevant documents from a corpus is typically based on the semantic similarity between the document content and query text.

Metric Learning Semantic Similarity +1

Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training

no code implementations NeurIPS 2021 Shangshu Qian, Hung Pham, Thibaud Lutellier, Zeou Hu, Jungwon Kim, Lin Tan, YaoLiang Yu, Jiahao Chen, Sameena Shah

Our study of 22 mitigation techniques and five baselines reveals up to 12. 6% fairness variance across identical training runs with identical seeds.

Crime Prediction Fairness

Synthetic Document Generator for Annotation-free Layout Recognition

no code implementations11 Nov 2021 Natraj Raman, Sameena Shah, Manuela Veloso

Analyzing the layout of a document to identify headers, sections, tables, figures etc.

Parameterized Explanations for Investor / Company Matching

no code implementations27 Oct 2021 Simerjot Kaur, Ivan Brugere, Andrea Stefanucci, Armineh Nourbakhsh, Sameena Shah, Manuela Veloso

We compare the performance of our system with human generated recommendations and demonstrate the ability of our algorithm to perform extremely well on this task.

Decision Making Explanation Generation +1

FinQA: A Dataset of Numerical Reasoning over Financial Data

1 code implementation EMNLP 2021 Zhiyu Chen, Wenhu Chen, Charese Smiley, Sameena Shah, Iana Borova, Dylan Langdon, Reema Moussa, Matt Beane, Ting-Hao Huang, Bryan Routledge, William Yang Wang

In contrast to existing tasks on general domain, the finance domain includes complex numerical reasoning and understanding of heterogeneous representations.

Question Answering

Debiasing classifiers: is reality at variance with expectation?

no code implementations4 Nov 2020 Ashrya Agrawal, Florian Pfisterer, Bernd Bischl, Francois Buet-Golfouse, Srijan Sood, Jiahao Chen, Sameena Shah, Sebastian Vollmer

We present an empirical study of debiasing methods for classifiers, showing that debiasers often fail in practice to generalize out-of-sample, and can in fact make fairness worse rather than better.


Simulating and classifying behavior in adversarial environments based on action-state traces: an application to money laundering

no code implementations3 Nov 2020 Daniel Borrajo, Manuela Veloso, Sameena Shah

One of the key characteristics of these applications is the wide range of strategies that an adversary may choose as they adapt their strategy dynamically to sustain benefits and evade authorities.

Robust Document Representations using Latent Topics and Metadata

no code implementations23 Oct 2020 Natraj Raman, Armineh Nourbakhsh, Sameena Shah, Manuela Veloso

Task specific fine-tuning of a pre-trained neural language model using a custom softmax output layer is the de facto approach of late when dealing with document classification problems.

Document Classification Language Modelling

Explicit Group Sparse Projection with Applications to Deep Learning and NMF

no code implementations9 Dec 2019 Riyasat Ohib, Nicolas Gillis, Niccolò Dalmasso, Sameena Shah, Vamsi K. Potluru, Sergey Plis

Instead, in our approach we set the sparsity level for the whole set explicitly and simultaneously project a group of vectors with the sparsity level of each vector tuned automatically.

Network Pruning

Reuters Tracer: Toward Automated News Production Using Large Scale Social Media Data

no code implementations11 Nov 2017 Xiaomo Liu, Armineh Nourbakhsh, Quanzhi Li, Sameena Shah, Robert Martin, John Duprey

It has a bottom-up approach to news detection, and does not rely on a predefined set of sources or subjects.

Social and Information Networks

Data Sets: Word Embeddings Learned from Tweets and General Data

no code implementations14 Aug 2017 Quanzhi Li, Sameena Shah, Xiaomo Liu, Armineh Nourbakhsh

In addition to the data sets learned from just tweet data, we also built embedding sets from the general data and the combination of tweets with the general data.

Sentiment Analysis Topic Classification +1

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