Search Results for author: Rupam Acharyya

Found 9 papers, 2 papers with code

Temporal Motifs for Financial Networks: A Study on Mercari, JPMC, and Venmo Platforms

no code implementations18 Jan 2023 Penghang Liu, Rupam Acharyya, Robert E. Tillman, Shunya Kimura, Naoki Masuda, Ahmet Erdem Sarıyüce

For the Venmo network, we investigate the interplay between financial and social relations on three tasks: friendship prediction, vendor identification, and analysis of temporal cycles.

Fraud Detection

Fairness in Rating Prediction by Awareness of Verbal and Gesture Quality of Public Speeches

1 code implementation11 Dec 2020 Ankani Chattoraj, Rupam Acharyya, Shouman Das, Md. Iftekhar Tanveer, Ehsan Hoque

Our work ties together a novel metric for public speeches in both verbal and non-verbal domain with the computational power of a neural network to design a fair prediction system for speakers.

Fairness

Statistical Mechanical Analysis of Neural Network Pruning

1 code implementation30 Jun 2020 Rupam Acharyya, Ankani Chattoraj, Boyu Zhang, Shouman Das, Daniel Stefankovic

We inspect different pruning techniques under the statistical mechanics formulation of a teacher-student framework and derive their generalization error (GE) bounds.

Computational Efficiency Network Pruning

Detection and Mitigation of Bias in Ted Talk Ratings

no code implementations2 Mar 2020 Rupam Acharyya, Shouman Das, Ankani Chattoraj, Oishani Sengupta, Md Iftekar Tanveer

Unbiased data collection is essential to guaranteeing fairness in artificial intelligence models.

Attribute Fairness

FairyTED: A Fair Rating Predictor for TED Talk Data

no code implementations25 Nov 2019 Rupam Acharyya, Shouman Das, Ankani Chattoraj, Md. Iftekhar Tanveer

This causal model contributes in generating counterfactual data to train a fair predictive model.

counterfactual Fairness

Infinite-Label Learning with Semantic Output Codes

no code implementations23 Aug 2016 Yang Zhang, Rupam Acharyya, Ji Liu, Boqing Gong

We develop a new statistical machine learning paradigm, named infinite-label learning, to annotate a data point with more than one relevant labels from a candidate set, which pools both the finite labels observed at training and a potentially infinite number of previously unseen labels.

Multi-Label Learning Zero-Shot Learning

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