Search Results for author: Umang Gupta

Found 14 papers, 6 papers with code

"Define Your Terms" : Enhancing Efficient Offensive Speech Classification with Definition

1 code implementation5 Feb 2024 Huy Nghiem, Umang Gupta, Fred Morstatter

The propagation of offensive content through social media channels has garnered attention of the research community.

Jointly Reparametrized Multi-Layer Adaptation for Efficient and Private Tuning

1 code implementation30 May 2023 Umang Gupta, Aram Galstyan, Greg Ver Steeg

This can be a drawback for low-resource applications and training with differential-privacy constraints, where excessive noise may be introduced during finetuning.

Towards Sparsified Federated Neuroimaging Models via Weight Pruning

no code implementations24 Aug 2022 Dimitris Stripelis, Umang Gupta, Nikhil Dhinagar, Greg Ver Steeg, Paul Thompson, José Luis Ambite

In our experiments in centralized and federated settings on the brain age prediction task (estimating a person's age from their brain MRI), we demonstrate that models can be pruned up to 95% sparsity without affecting performance even in challenging federated learning environments with highly heterogeneous data distributions.

Federated Learning

Secure & Private Federated Neuroimaging

no code implementations11 May 2022 Dimitris Stripelis, Umang Gupta, Hamza Saleem, Nikhil Dhinagar, Tanmay Ghai, Rafael Chrysovalantis Anastasiou, Armaghan Asghar, Greg Ver Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, Jose Luis Ambite

Each site trains the neural network over its private data for some time, then shares the neural network parameters (i. e., weights, gradients) with a Federation Controller, which in turn aggregates the local models, sends the resulting community model back to each site, and the process repeats.

Federated Learning

Federated Progressive Sparsification (Purge, Merge, Tune)+

no code implementations26 Apr 2022 Dimitris Stripelis, Umang Gupta, Greg Ver Steeg, Jose Luis Ambite

Second, the models are incrementally constrained to a smaller set of parameters, which facilitates alignment/merging of the local models and improved learning performance at high sparsification rates.

Attributing Fair Decisions with Attention Interventions

1 code implementation NAACL (TrustNLP) 2022 Ninareh Mehrabi, Umang Gupta, Fred Morstatter, Greg Ver Steeg, Aram Galstyan

The widespread use of Artificial Intelligence (AI) in consequential domains, such as healthcare and parole decision-making systems, has drawn intense scrutiny on the fairness of these methods.

Decision Making Fairness

Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption

no code implementations7 Aug 2021 Dimitris Stripelis, Hamza Saleem, Tanmay Ghai, Nikhil Dhinagar, Umang Gupta, Chrysovalantis Anastasiou, Greg Ver Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, Jose Luis Ambite

Federated learning (FL) enables distributed computation of machine learning models over various disparate, remote data sources, without requiring to transfer any individual data to a centralized location.

Benchmarking Federated Learning

Membership Inference Attacks on Deep Regression Models for Neuroimaging

no code implementations6 May 2021 Umang Gupta, Dimitris Stripelis, Pradeep K. Lam, Paul M. Thompson, José Luis Ambite, Greg Ver Steeg

In particular, we show that it is possible to infer if a sample was used to train the model given only access to the model prediction (black-box) or access to the model itself (white-box) and some leaked samples from the training data distribution.

Federated Learning regression

Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation

2 code implementations11 Jan 2021 Umang Gupta, Aaron M Ferber, Bistra Dilkina, Greg Ver Steeg

Controlling bias in training datasets is vital for ensuring equal treatment, or parity, between different groups in downstream applications.

Fairness

Deep Generative Dual Memory Network for Continual Learning

no code implementations ICLR 2018 Nitin Kamra, Umang Gupta, Yan Liu

This phenomenon called catastrophic forgetting is a fundamental challenge to overcome before neural networks can learn continually from incoming data.

Continual Learning Hippocampus

A Sentiment-and-Semantics-Based Approach for Emotion Detection in Textual Conversations

no code implementations21 Jul 2017 Umang Gupta, Ankush Chatterjee, Radhakrishnan Srikanth, Puneet Agrawal

In this paper, we propose a novel approach to detect emotions like happy, sad or angry in textual conversations using an LSTM based Deep Learning model.

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