Search Results for author: Rahul Gupta

Found 53 papers, 11 papers with code

Controlled Data Generation via Insertion Operations for NLU

no code implementations NAACL (ACL) 2022 Manoj Kumar, Yuval Merhav, Haidar Khan, Rahul Gupta, Anna Rumshisky, Wael Hamza

Use of synthetic data is rapidly emerging as a realistic alternative to manually annotating live traffic for industry-scale model building.

intent-classification Intent Classification +4

FLIRT: Feedback Loop In-context Red Teaming

no code implementations8 Aug 2023 Ninareh Mehrabi, Palash Goyal, Christophe Dupuy, Qian Hu, Shalini Ghosh, Richard Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta

Here we propose an automatic red teaming framework that evaluates a given model and exposes its vulnerabilities against unsafe and inappropriate content generation.

Response Generation

FedMultimodal: A Benchmark For Multimodal Federated Learning

no code implementations15 Jun 2023 Tiantian Feng, Digbalay Bose, Tuo Zhang, Rajat Hebbar, Anil Ramakrishna, Rahul Gupta, Mi Zhang, Salman Avestimehr, Shrikanth Narayanan

In order to facilitate the research in multimodal FL, we introduce FedMultimodal, the first FL benchmark for multimodal learning covering five representative multimodal applications from ten commonly used datasets with a total of eight unique modalities.

Emotion Recognition Federated Learning

Experimental Validation of Model-less Robust Voltage Control using Measurement-based Estimated Voltage Sensitivity Coefficients

no code implementations26 Apr 2023 Rahul Gupta, Mario Paolone

The estimated voltage sensitivity coefficients are used to model the nodal voltages, and the control robustness is achieved by accounting for their uncertainties.

MUTANT: A Multi-sentential Code-mixed Hinglish Dataset

no code implementations23 Feb 2023 Rahul Gupta, Vivek Srivastava, Mayank Singh

As a use case, we leverage multilingual articles from two different data sources and build a first-of-its-kind multi-sentential code-mixed Hinglish dataset i. e., MUTANT.

Design Considerations For Hypothesis Rejection Modules In Spoken Language Understanding Systems

no code implementations31 Oct 2022 Aman Alok, Rahul Gupta, Shankar Ananthakrishnan

Hypothesis rejection modules in both schemes reject/accept a hypothesis based on features drawn from the utterance directed to the SLU system, the associated SLU hypothesis and SLU confidence score.

Spoken Language Understanding

An Analysis of the Effects of Decoding Algorithms on Fairness in Open-Ended Language Generation

no code implementations7 Oct 2022 Jwala Dhamala, Varun Kumar, Rahul Gupta, Kai-Wei Chang, Aram Galstyan

We present a systematic analysis of the impact of decoding algorithms on LM fairness, and analyze the trade-off between fairness, diversity and quality.

Fairness Text Generation

AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model

1 code implementation2 Aug 2022 Saleh Soltan, Shankar Ananthakrishnan, Jack FitzGerald, Rahul Gupta, Wael Hamza, Haidar Khan, Charith Peris, Stephen Rawls, Andy Rosenbaum, Anna Rumshisky, Chandana Satya Prakash, Mukund Sridhar, Fabian Triefenbach, Apurv Verma, Gokhan Tur, Prem Natarajan

In this work, we demonstrate that multilingual large-scale sequence-to-sequence (seq2seq) models, pre-trained on a mixture of denoising and Causal Language Modeling (CLM) tasks, are more efficient few-shot learners than decoder-only models on various tasks.

Causal Language Modeling Common Sense Reasoning +8

Differentially Private Decoding in Large Language Models

no code implementations26 May 2022 Jimit Majmudar, Christophe Dupuy, Charith Peris, Sami Smaili, Rahul Gupta, Richard Zemel

Recent large-scale natural language processing (NLP) systems use a pre-trained Large Language Model (LLM) on massive and diverse corpora as a headstart.

Language Modelling Large Language Model +1

Federated Learning with Noisy User Feedback

no code implementations NAACL 2022 Rahul Sharma, Anil Ramakrishna, Ansel MacLaughlin, Anna Rumshisky, Jimit Majmudar, Clement Chung, Salman Avestimehr, Rahul Gupta

Federated learning (FL) has recently emerged as a method for training ML models on edge devices using sensitive user data and is seen as a way to mitigate concerns over data privacy.

Federated Learning text-classification +1

Training Mixed-Domain Translation Models via Federated Learning

no code implementations NAACL 2022 Peyman Passban, Tanya Roosta, Rahul Gupta, Ankit Chadha, Clement Chung

Training mixed-domain translation models is a complex task that demands tailored architectures and costly data preparation techniques.

Benchmarking Federated Learning +3

Canary Extraction in Natural Language Understanding Models

no code implementations ACL 2022 Rahil Parikh, Christophe Dupuy, Rahul Gupta

In this work, we present a version of such an attack by extracting canaries inserted in NLU training data.

Natural Language Understanding

Learnings from Federated Learning in the Real world

no code implementations8 Feb 2022 Christophe Dupuy, Tanya G. Roosta, Leo Long, Clement Chung, Rahul Gupta, Salman Avestimehr

In this study, we evaluate the impact of such idiosyncrasies on Natural Language Understanding (NLU) models trained using FL.

Federated Learning Natural Language Understanding

Model-less Robust Voltage Control in Active Distribution Networks using Sensitivity Coefficients Estimated from Measurements

no code implementations11 Jan 2022 Rahul Gupta, Fabrizio Sossan, Mario Paolone

This formulation is applied to control distributed controllable photovoltaic (PV) generation in a distribution network to restrict the voltage within prescribed limits.

Coordinated Day-ahead Dispatch of Multiple Power Distribution Grids hosting Stochastic Resources: An ADMM-based Framework

no code implementations10 Jan 2022 Rahul Gupta, Sherif Fahmy, Mario Paolone

Specifically, the proposed framework optimizes the dispatch plan of an upstream medium voltage (MV) grid accounting for the flexibility offered by downstream low voltage (LV) grids and the knowledge of the uncertainties of the stochastic resources.

Distributed Optimization

Towards Realistic Single-Task Continuous Learning Research for NER

1 code implementation Findings (EMNLP) 2021 Justin Payan, Yuval Merhav, He Xie, Satyapriya Krishna, Anil Ramakrishna, Mukund Sridhar, Rahul Gupta

There is an increasing interest in continuous learning (CL), as data privacy is becoming a priority for real-world machine learning applications.


Optimal Grid-Forming Control of Battery Energy Storage Systems Providing Multiple Services: Modelling and Experimental Validation

no code implementations19 Oct 2021 Francesco Gerini, Yihui Zuo, Rahul Gupta, Elena Vagnoni, Rachid Cherkaoui, Mario Paolone

This paper proposes and experimentally validates a joint control and scheduling framework for a grid-forming converter-interfaced BESS providing multiple services to the electrical grid.


An Efficient DP-SGD Mechanism for Large Scale NLP Models

no code implementations14 Jul 2021 Christophe Dupuy, Radhika Arava, Rahul Gupta, Anna Rumshisky

However, the data used to train NLU models may contain private information such as addresses or phone numbers, particularly when drawn from human subjects.

Natural Language Understanding Privacy Preserving

Does Robustness Improve Fairness? Approaching Fairness with Word Substitution Robustness Methods for Text Classification

no code implementations Findings (ACL) 2021 Yada Pruksachatkun, Satyapriya Krishna, Jwala Dhamala, Rahul Gupta, Kai-Wei Chang

Existing bias mitigation methods to reduce disparities in model outcomes across cohorts have focused on data augmentation, debiasing model embeddings, or adding fairness-based optimization objectives during training.

Data Augmentation Fairness +2

FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks

1 code implementation Findings (NAACL) 2022 Bill Yuchen Lin, Chaoyang He, Zihang Zeng, Hulin Wang, Yufen Huang, Christophe Dupuy, Rahul Gupta, Mahdi Soltanolkotabi, Xiang Ren, Salman Avestimehr

Increasing concerns and regulations about data privacy and sparsity necessitate the study of privacy-preserving, decentralized learning methods for natural language processing (NLP) tasks.

Benchmarking Federated Learning +5

ADePT: Auto-encoder based Differentially Private Text Transformation

1 code implementation EACL 2021 Satyapriya Krishna, Rahul Gupta, Christophe Dupuy

We prove the theoretical privacy guarantee of our algorithm and assess its privacy leakage under Membership Inference Attacks(MIA) (Shokri et al., 2017) on models trained with transformed data.

ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification

no code implementations28 Jan 2021 Manoj Kumar, Varun Kumar, Hadrien Glaude, Cyprien delichy, Aman Alok, Rahul Gupta

We make use of a conditional generator for data augmentation that is trained directly using the meta-learning objective and simultaneously with prototypical networks, hence ensuring that data augmentation is customized to the task.

Classification Data Augmentation +8

BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation

1 code implementation27 Jan 2021 Jwala Dhamala, Tony Sun, Varun Kumar, Satyapriya Krishna, Yada Pruksachatkun, Kai-Wei Chang, Rahul Gupta

To systematically study and benchmark social biases in open-ended language generation, we introduce the Bias in Open-Ended Language Generation Dataset (BOLD), a large-scale dataset that consists of 23, 679 English text generation prompts for bias benchmarking across five domains: profession, gender, race, religion, and political ideology.

Benchmarking Text Generation

Co$_2$FeAl full Heusler compound based spintronic terahertz emitter

no code implementations26 Jan 2021 Rahul Gupta, Sajid Husain, Ankit Kumar, Rimantas Brucas, Anders Rydberg, Peter Svedlindh

To achieve a large terahertz (THz) amplitude from a spintronic THz emitter (STE), materials with 100\% spin polarisation such as Co-based Heusler compounds as the ferromagnetic layer are required.

Materials Science Mesoscale and Nanoscale Physics Other Condensed Matter Optics

Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks

no code implementations EMNLP (insights) 2020 Ansel MacLaughlin, Jwala Dhamala, Anoop Kumar, Sriram Venkatapathy, Ragav Venkatesan, Rahul Gupta

Neural Architecture Search (NAS) methods, which automatically learn entire neural model or individual neural cell architectures, have recently achieved competitive or state-of-the-art (SOTA) performance on variety of natural language processing and computer vision tasks, including language modeling, natural language inference, and image classification.

Image Classification Language Modelling +6

Phase Transition Behavior in Knowledge Compilation

no code implementations20 Jul 2020 Rahul Gupta, Subhajit Roy, Kuldeep S. Meel

The study of phase transition behaviour in SAT has led to deeper understanding and algorithmic improvements of modern SAT solvers.

Automatic Discovery of Novel Intents & Domains from Text Utterances

no code implementations22 May 2020 Nikhita Vedula, Rahul Gupta, Aman Alok, Mukund Sridhar

We propose a novel framework, ADVIN, to automatically discover novel domains and intents from large volumes of unlabeled data.

General Classification Natural Language Understanding +1

Towards classification parity across cohorts

no code implementations16 May 2020 Aarsh Patel, Rahul Gupta, Mukund Harakere, Satyapriya Krishna, Aman Alok, Peng Liu

In this research work, we aim to achieve classification parity across explicit as well as implicit sensitive features.

Classification Clustering +6

Joint Multi-Dimensional Model for Global and Time-Series Annotations

no code implementations6 May 2020 Anil Ramakrishna, Rahul Gupta, Shrikanth Narayanan

In this work we address this by proposing a generative model for multi-dimensional annotation fusion, which models the dimensions jointly leading to more accurate ground truth estimates.

Time Series Time Series Analysis

Fast Intent Classification for Spoken Language Understanding

1 code implementation3 Dec 2019 Akshit Tyagi, Varun Sharma, Rahul Gupta, Lynn Samson, Nan Zhuang, Zihang Wang, Bill Campbell

To address the latency and computational complexity issues, we explore a BranchyNet scheme on an intent classification scheme within SLU systems.

Classification Decision Making +8

Neural Attribution for Semantic Bug-Localization in Student Programs

1 code implementation NeurIPS 2019 Rahul Gupta, Aditya Kanade, Shirish Shevade

In this work, we present NeuralBugLocator, a deep learning based technique, that can localize the bugs in a faulty program with respect to a failing test, without even running the program.

Fault localization

Modeling Feature Representations for Affective Speech using Generative Adversarial Networks

no code implementations31 Oct 2019 Saurabh Sahu, Rahul Gupta, Carol Espy-Wilson

In this work, we experiment with variants of GAN architectures to generate feature vectors corresponding to an emotion in two ways: (i) A generator is trained with samples from a mixture prior.

Cross-corpus Emotion Recognition +1

One-vs-All Models for Asynchronous Training: An Empirical Analysis

no code implementations20 Jun 2019 Rahul Gupta, Aman Alok, Shankar Ananthakrishnan

An OVA system consists of as many OVA models as the number of classes, providing the advantage of asynchrony, where each OVA model can be re-trained independent of other models.

General Classification Natural Language Understanding +1

Deep Learning for Bug-Localization in Student Programs

no code implementations28 May 2019 Rahul Gupta, Aditya Kanade, Shirish Shevade

To localize the bugs, we analyze the trained network using a state-of-the-art neural prediction attribution technique and see which lines of the programs make it predict the test outcomes.

On evaluating CNN representations for low resource medical image classification

no code implementations26 Mar 2019 Taruna Agrawal, Rahul Gupta, Shrikanth Narayanan

Convolutional Neural Networks (CNNs) have revolutionized performances in several machine learning tasks such as image classification, object tracking, and keyword spotting.

General Classification Image Classification +4

Data augmentation for low resource sentiment analysis using generative adversarial networks

no code implementations18 Feb 2019 Rahul Gupta

Sentiment analysis is a task that may suffer from a lack of data in certain cases, as the datasets are often generated and annotated by humans.

Data Augmentation Sentiment Analysis +1

A Re-ranker Scheme for Integrating Large Scale NLU models

no code implementations25 Sep 2018 Chengwei Su, Rahul Gupta, Shankar Ananthakrishnan, Spyros Matsoukas

An ideal re-ranker will exhibit the following two properties: (a) it should prefer the most relevant hypothesis for the given input as the top hypothesis and, (b) the interpretation scores corresponding to each hypothesis produced by the re-ranker should be calibrated.

Natural Language Understanding

On Enhancing Speech Emotion Recognition using Generative Adversarial Networks

no code implementations18 Jun 2018 Saurabh Sahu, Rahul Gupta, Carol Espy-Wilson

GANs consist of a discriminator and a generator working in tandem playing a min-max game to learn a target underlying data distribution; when fed with data-points sampled from a simpler distribution (like uniform or Gaussian distribution).

Cross-corpus Speech Emotion Recognition

Adversarial Auto-encoders for Speech Based Emotion Recognition

no code implementations6 Jun 2018 Saurabh Sahu, Rahul Gupta, Ganesh Sivaraman, Wael Abd-Almageed, Carol Espy-Wilson

Recently, generative adversarial networks and adversarial autoencoders have gained a lot of attention in machine learning community due to their exceptional performance in tasks such as digit classification and face recognition.

Emotion Recognition Face Recognition

DeepFix: Fixing Common C Language Errors by Deep Learning

1 code implementation4 Feb 2017 Rahul Gupta, Soham Pal, Aditya Kanade, Shirish Shevade

The problem of automatically fixing programming errors is a very active research topic in software engineering.

Program Repair

Inferring object rankings based on noisy pairwise comparisons from multiple annotators

no code implementations13 Dec 2016 Rahul Gupta, Shrikanth Narayanan

In this work, we propose Expectation-Maximization (EM) based algorithms that rely on the judgments from multiple annotators and the object attributes for inferring the latent ground truth.

Cannot find the paper you are looking for? You can Submit a new open access paper.