Search Results for author: Huzefa Rangwala

Found 41 papers, 15 papers with code

FeatNavigator: Automatic Feature Augmentation on Tabular Data

no code implementations13 Jun 2024 Jiaming Liang, Chuan Lei, Xiao Qin, Jiani Zhang, Asterios Katsifodimos, Christos Faloutsos, Huzefa Rangwala

FeatNavigator evaluates a feature from two aspects: (1) the intrinsic value of a feature towards an ML task (i. e., feature importance) and (2) the efficacy of a join path connecting the feature to the base table (i. e., integration quality).

Feature Importance

GraphStorm: all-in-one graph machine learning framework for industry applications

1 code implementation10 Jun 2024 Da Zheng, Xiang Song, Qi Zhu, Jian Zhang, Theodore Vasiloudis, Runjie Ma, Houyu Zhang, Zichen Wang, Soji Adeshina, Israt Nisa, Alejandro Mottini, Qingjun Cui, Huzefa Rangwala, Belinda Zeng, Christos Faloutsos, George Karypis

GraphStorm has the following desirable properties: (a) Easy to use: it can perform graph construction and model training and inference with just a single command; (b) Expert-friendly: GraphStorm contains many advanced GML modeling techniques to handle complex graph data and improve model performance; (c) Scalable: every component in GraphStorm can operate on graphs with billions of nodes and can scale model training and inference to different hardware without changing any code.

graph construction

DispaRisk: Assessing and Interpreting Disparity Risks in Datasets

1 code implementation20 May 2024 Jonathan Vasquez, Carlotta Domeniconi, Huzefa Rangwala

In this paper, we introduce DispaRisk, a novel framework designed to proactively assess the potential risks of disparities in datasets during the initial stages of the ML pipeline.

Benchmarking Fairness

OpenTab: Advancing Large Language Models as Open-domain Table Reasoners

1 code implementation22 Feb 2024 Kezhi Kong, Jiani Zhang, Zhengyuan Shen, Balasubramaniam Srinivasan, Chuan Lei, Christos Faloutsos, Huzefa Rangwala, George Karypis

Large Language Models (LLMs) trained on large volumes of data excel at various natural language tasks, but they cannot handle tasks requiring knowledge that has not been trained on previously.

Retrieval

NameGuess: Column Name Expansion for Tabular Data

1 code implementation19 Oct 2023 Jiani Zhang, Zhengyuan Shen, Balasubramaniam Srinivasan, Shen Wang, Huzefa Rangwala, George Karypis

Recent advances in large language models have revolutionized many sectors, including the database industry.

Text Generation

BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs

1 code implementation5 Oct 2023 Zifeng Wang, Zichen Wang, Balasubramaniam Srinivasan, Vassilis N. Ioannidis, Huzefa Rangwala, Rishita Anubhai

Foundation models (FMs) are able to leverage large volumes of unlabeled data to demonstrate superior performance across a wide range of tasks.

Cross-Modal Retrieval Domain Generalization +3

GenSyn: A Multi-stage Framework for Generating Synthetic Microdata using Macro Data Sources

1 code implementation8 Dec 2022 Angeela Acharya, Siddhartha Sikdar, Sanmay Das, Huzefa Rangwala

Our method combines the estimation of a dependency graph and conditional probabilities from the target location with the use of a Gaussian copula to leverage the available information from the auxiliary locations.

Synthetic Data Generation

Training self-supervised peptide sequence models on artificially chopped proteins

no code implementations9 Nov 2022 Gil Sadeh, Zichen Wang, Jasleen Grewal, Huzefa Rangwala, Layne Price

In this paper, we propose a new peptide data augmentation scheme, where we train peptide language models on artificially constructed peptides that are small contiguous subsets of longer, wild-type proteins; we refer to the training peptides as "chopped proteins".

Data Augmentation Language Modelling +2

Ex-Ante Assessment of Discrimination in Dataset

no code implementations16 Aug 2022 Jonathan Vasquez, Xavier Gitiaux, Huzefa Rangwala

Data owners face increasing liability for how the use of their data could harm under-priviliged communities.

SoFaiR: Single Shot Fair Representation Learning

no code implementations26 Apr 2022 Xavier Gitiaux, Huzefa Rangwala

To avoid discriminatory uses of their data, organizations can learn to map them into a representation that filters out information related to sensitive attributes.

Fairness Information Plane +1

Improving Zero-Shot Event Extraction via Sentence Simplification

no code implementations6 Apr 2022 Sneha Mehta, Huzefa Rangwala, Naren Ramakrishnan

We show how such context simplification can improve the performance of MRC-based event extraction by more than 5% for actor extraction and more than 10% for target extraction.

Event Argument Extraction Extractive Question-Answering +4

Causal Knowledge Guided Societal Event Forecasting

1 code implementation10 Dec 2021 Songgaojun Deng, Huzefa Rangwala, Yue Ning

(ii) Given spatiotemporal non-independent and identically distributed (non-IID) data, modeling hidden confounders for accurate causal effect estimation is not trivial.

Causal Inference

Asynchronous Federated Learning for Sensor Data with Concept Drift

no code implementations1 Sep 2021 Yujing Chen, Zheng Chai, Yue Cheng, Huzefa Rangwala

We propose a novel approach, FedConD, to detect and deal with the concept drift on local devices and minimize the effect on the performance of models in asynchronous FL.

Ensemble Learning Federated Learning

Cross-Lingual Text Classification of Transliterated Hindi and Malayalam

1 code implementation31 Aug 2021 Jitin Krishnan, Antonios Anastasopoulos, Hemant Purohit, Huzefa Rangwala

Transliteration is very common on social media, but transliterated text is not adequately handled by modern neural models for various NLP tasks.

Benchmarking Cross-Lingual Transfer +7

Fair Representations by Compression

no code implementations28 May 2021 Xavier Gitiaux, Huzefa Rangwala

Organizations that collect and sell data face increasing scrutiny for the discriminatory use of data.

Decoder Fairness

Metric-Free Individual Fairness with Cooperative Contextual Bandits

no code implementations13 Nov 2020 Qian Hu, Huzefa Rangwala

Group fairness requires that different groups should be treated similarly which might be unfair to some individuals within a group.

Decision Making Fairness +1

FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tiers

no code implementations12 Oct 2020 Zheng Chai, Yujing Chen, Ali Anwar, Liang Zhao, Yue Cheng, Huzefa Rangwala

By bridging the synchronous and asynchronous training through tiering, FedAT minimizes the straggler effect with improved convergence speed and test accuracy.

Federated Learning

Learning Smooth and Fair Representations

no code implementations15 Jun 2020 Xavier Gitiaux, Huzefa Rangwala

Organizations that own data face increasing legal liability for its discriminatory use against protected demographic groups, extending to contractual transactions involving third parties access and use of the data.

Fairness Representation Learning

Common-Knowledge Concept Recognition for SEVA

1 code implementation26 Mar 2020 Jitin Krishnan, Patrick Coronado, Hemant Purohit, Huzefa Rangwala

We build a common-knowledge concept recognition system for a Systems Engineer's Virtual Assistant (SEVA) which can be used for downstream tasks such as relation extraction, knowledge graph construction, and question-answering.

Entity Extraction using GAN graph construction +5

Generative Multi-Stream Architecture For American Sign Language Recognition

no code implementations9 Mar 2020 Dom Huh, Sai Gurrapu, Frederick Olson, Huzefa Rangwala, Parth Pathak, Jana Kosecka

With advancements in deep model architectures, tasks in computer vision can reach optimal convergence provided proper data preprocessing and model parameter initialization.

Sign Language Recognition

FineHand: Learning Hand Shapes for American Sign Language Recognition

no code implementations4 Mar 2020 Al Amin Hosain, Panneer Selvam Santhalingam, Parth Pathak, Huzefa Rangwala, Jana Kosecka

American Sign Language recognition is a difficult gesture recognition problem, characterized by fast, highly articulate gestures.

Gesture Recognition Sign Language Recognition

Unsupervised and Interpretable Domain Adaptation to Rapidly Filter Tweets for Emergency Services

1 code implementation4 Mar 2020 Jitin Krishnan, Hemant Purohit, Huzefa Rangwala

As deep networks struggle with sparse datasets, we show that this can be improved by sharing a base layer for multi-task learning and domain adversarial training.

Multi-Task Learning Unsupervised Domain Adaptation

Diversity-Based Generalization for Unsupervised Text Classification under Domain Shift

1 code implementation25 Feb 2020 Jitin Krishnan, Hemant Purohit, Huzefa Rangwala

At present, the state-of-the-art unsupervised domain adaptation approaches for subjective text classification problems leverage unlabeled target data along with labeled source data.

Diversity General Classification +3

Academic Performance Estimation with Attention-based Graph Convolutional Networks

no code implementations26 Dec 2019 Qian Hu, Huzefa Rangwala

Traditional methods for student's performance prediction usually neglect the underlying relationships between multiple courses; and how students acquire knowledge across them.

Decision Making Recommendation Systems

Low Rank Factorization for Compact Multi-Head Self-Attention

1 code implementation26 Nov 2019 Sneha Mehta, Huzefa Rangwala, Naren Ramakrishnan

Effective representation learning from text has been an active area of research in the fields of NLP and text mining.

General Classification Representation Learning +4

Asynchronous Online Federated Learning for Edge Devices with Non-IID Data

no code implementations5 Nov 2019 Yujing Chen, Yue Ning, Martin Slawski, Huzefa Rangwala

In this paper, we present an Asynchronous Online Federated Learning (ASO-Fed) framework, where the edge devices perform online learning with continuous streaming local data and a central server aggregates model parameters from clients.

Federated Learning

Sign Language Recognition Analysis using Multimodal Data

no code implementations24 Sep 2019 Al Amin Hosain, Panneer Selvam Santhalingam, Parth Pathak, Jana Kosecka, Huzefa Rangwala

Despite having similarity with the well-studied human activity recognition, the use of 3D skeleton data in sign language recognition is rare.

Human Activity Recognition Sign Language Recognition

Federated Multi-task Hierarchical Attention Model for Sensor Analytics

no code implementations13 May 2019 Yujing Chen, Yue Ning, Zheng Chai, Huzefa Rangwala

The attention mechanism of the proposed model seeks to extract feature representations from the input and learn a shared representation focused on time dimensions across multiple sensors.

Activity Recognition General Classification +1

Multi-Differential Fairness Auditor for Black Box Classifiers

no code implementations18 Mar 2019 Xavier Gitiaux, Huzefa Rangwala

Machine learning algorithms are increasingly involved in sensitive decision-making process with adversarial implications on individuals.

Decision Making Fairness

Reliable Deep Grade Prediction with Uncertainty Estimation

no code implementations26 Feb 2019 Qian Hu, Huzefa Rangwala

Prior research on students' grade prediction include shallow linear models; however, students' learning is a highly complex process that involves the accumulation of knowledge across a sequence of courses that can not be sufficiently modeled by these linear models.

Decision Making

ALE: Additive Latent Effect Models for Grade Prediction

no code implementations17 Jan 2018 Zhiyun Ren, Xia Ning, Huzefa Rangwala

Grade prediction methods seek to estimate a grade that a student may achieve in a course that she may take in the future (e. g., next term).

Grade Prediction with Temporal Course-wise Influence

no code implementations15 Sep 2017 Zhiyun Ren, Xia Ning, Huzefa Rangwala

The grade of a student on a course is modeled as the similarity of their latent representation in the "knowledge" space.

Embedding Feature Selection for Large-scale Hierarchical Classification

no code implementations6 Jun 2017 Azad Naik, Huzefa Rangwala

Our experimental evaluation on text and image datasets with varying distribution of features, classes and instances shows upto 3x order of speed-up on massive datasets and upto 45% less memory requirements for storing the weight vectors of learned model without any significant loss (improvement for some datasets) in the classification accuracy.

Classification Dimensionality Reduction +2

Classifying Documents within Multiple Hierarchical Datasets using Multi-Task Learning

no code implementations6 Jun 2017 Azad Naik, Anveshi Charuvaka, Huzefa Rangwala

Multi-task learning (MTL) is a supervised learning paradigm in which the prediction models for several related tasks are learned jointly to achieve better generalization performance.

Binary Classification Classification +3

Inconsistent Node Flattening for Improving Top-down Hierarchical Classification

no code implementations5 Jun 2017 Azad Naik, Huzefa Rangwala

In this paper, we propose two different data-driven approaches (local and global) for hierarchical structure modification that identifies and flattens inconsistent nodes present within the hierarchy.

Classification General Classification

Predicting Performance on MOOC Assessments using Multi-Regression Models

no code implementations8 May 2016 Zhiyun Ren, Huzefa Rangwala, Aditya Johri

The past few years has seen the rapid growth of data min- ing approaches for the analysis of data obtained from Mas- sive Open Online Courses (MOOCs).

regression

Filter based Taxonomy Modification for Improving Hierarchical Classification

no code implementations2 Mar 2016 Azad Naik, Huzefa Rangwala

Experimental comparisons of top-down HC with our modified hierarchy, on a wide range of datasets shows classification performance improvement over the baseline hierarchy (i:e:, defined by expert), clustered hierarchy and flattening based hierarchy modification approaches.

Classification General Classification

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