7 citations · 14 across the 8 of their papers we have counts for
9 papers
Temporal Motifs for Financial Networks: A Study on Mercari, JPMC, and Venmo Platforms
Penghang Liu, Bahadir Altun, Rupam Acharyya +4
Understanding the dynamics of financial transactions among people is critical for various applications such as fraud detection. One important aspect of financial transaction networ…
Fairness in Rating Prediction by Awareness of Verbal and Gesture Quality of Public Speeches
Ankani Chattoraj, Rupam Acharyya, Shouman Das +2
The role of verbal and non-verbal cues towards great public speaking has been a topic of exploration for many decades. We identify a commonality across present theories, the elemen…
Detecting Individuals with Depressive Disorder fromPersonal Google Search and YouTube History Logs
Boyu Zhang, Anis Zaman, Rupam Acharyya +3
Depressive disorder is one of the most prevalent mental illnesses among the global population. However, traditional screening methods require exacting in-person interviews and may…
Statistical Mechanical Analysis of Neural Network Pruning
Rupam Acharyya, Ankani Chattoraj, Boyu Zhang +2
Deep learning architectures with a huge number of parameters are often compressed using pruning techniques to ensure computational efficiency of inference during deployment. Despit…
Detection and Mitigation of Bias in Ted Talk Ratings
Rupam Acharyya, Shouman Das, Ankani Chattoraj +2
Unbiased data collection is essential to guaranteeing fairness in artificial intelligence models. Implicit bias, a form of behavioral conditioning that leads us to attribute predet…
To be or not to be? A spatial predictive crime model for Rochester
Ankani Chattoraj, Rupam Acharyya, Sabyasachi Shivkumar +2
This project uses a spatial model (Geographically Weighted Regression) to relate various physical and social features to crime rates. Besides making interesting predictions from ba…
FairyTED: A Fair Rating Predictor for TED Talk Data
Rupam Acharyya, Shouman Das, Ankani Chattoraj +1
With the recent trend of applying machine learning in every aspect of human life, it is important to incorporate fairness into the core of the predictive algorithms. We address the…
Infinite-Label Learning with Semantic Output Codes
Yang Zhang, Rupam Acharyya, Ji Liu +1
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…
Counting Popular Matchings in House Allocation Problems
Rupam Acharyya, Sourav Chakraborty, Nitesh Jha
We study the problem of counting the number of popular matchings in a given instance. A popular matching instance consists of agents A and houses H, where each agent ranks a subset…