3 citations · 6 across the 20 of their papers we have counts for
20 papers
Mood-Aware Music Recommendation: Integrating User Affective Signals into Ranking Systems
Terence Zeng, Abhishek K. Umrawal
Recommendation systems are essential in modern music streaming platforms due to the vast amount of available content. While collaborative filtering is widely used to suggest items…
A Theoretical Game of Attacks via Compositional Skills
Xinbo Wu, Huan Zhang, Abhishek Umrawal +1
As large language models grow increasingly capable, concerns about their safe deployment have intensified. While numerous alignment strategies aim to restrict harmful behavior, the…
LOFA: Online Influence Maximization under Full-Bandit Feedback using Lazy Forward Selection
Jinyu Xu, Abhishek K. Umrawal
We study the problem of influence maximization (IM) in an online setting, where the goal is to select a subset of nodes$\unicode{x2014}$called the seed set$\unicode{x2014}$at each…
Ungraded Assignments in Introductory Computing: A Report
Yehya Sleiman Tellawi, Abhishek K. Umrawal
This experience report explores the effects of ungraded assignments on the learning experience of students in an introductory computing course. Our study examines the impact of ung…
A Community-Aware Framework for Influence Maximization with Explicit Accounting for Inter-Community Influence
Eliot W. Robson, Abhishek K. Umrawal
Influence Maximization (IM) seeks to identify a small set of seed nodes in a social network to maximize expected information spread under a diffusion model. While community-based a…
A Pressure-Based Diffusion Model for Influence Maximization on Social Networks
Curt Stutsman, Eliot W. Robson, Abhishek K. Umrawal
In many real-world scenarios, an individual's local social network carries significant influence over the opinions they form and subsequently propagate. In this paper, we propose a…
Content filtering methods for music recommendation: A review
Terence Zeng, Abhishek K. Umrawal
Recommendation systems have become essential in modern music streaming platforms, shaping how users discover and engage with songs. One common approach in recommendation systems is…
Concealment of Intent: A Game-Theoretic Analysis
Xinbo Wu, Abhishek Umrawal, Lav R. Varshney
As large language models (LLMs) grow more capable, concerns about their safe deployment have also grown. Although alignment mechanisms have been introduced to deter misuse, they re…
JAM: Controllable and Responsible Text Generation via Causal Reasoning and Latent Vector Manipulation
Yingbing Huang, Deming Chen, Abhishek K. Umrawal
While large language models (LLMs) have made significant strides in generating coherent and contextually relevant text, they often function as opaque black boxes, trained on vast u…
Fractional Budget Allocation for Influence Maximization under General Marketing Strategies
Akhil Bhimaraju, Eliot W. Robson, Lav R. Varshney +1
We consider the fractional influence maximization problem, i.e., identifying users on a social network to be incentivized with potentially partial discounts to maximize the influen…
CyNetDiff -- A Python Library for Accelerated Implementation of Network Diffusion Models
Eliot W. Robson, Dhemath Reddy, Abhishek K. Umrawal
In recent years, there has been increasing interest in network diffusion models and related problems. The most popular of these are the independent cascade and linear threshold mod…
Simple is Better and Large is Not Enough: Towards Ensembling of Foundational Language Models
Nancy Tyagi, Aidin Shiri, Surjodeep Sarkar +2
Foundational Language Models (FLMs) have advanced natural language processing (NLP) research. Current researchers are developing larger FLMs (e.g., XLNet, T5) to enable contextuali…
A Genetic Algorithm-based Framework for Learning Statistical Power Manifold
Abhishek K. Umrawal, Sean P. Lane, Erin P. Hennes
Statistical power is a measure of the replicability of a categorical hypothesis test. Formally, it is the probability of detecting an effect, if there is a true effect present in t…
A Community-Aware Framework for Social Influence Maximization
Abhishek K. Umrawal, Christopher J. Quinn, Vaneet Aggarwal
We consider the problem of Influence Maximization (IM), the task of selecting seed nodes in a social network such that the expected number of nodes influenced is maximized. We…
Leveraging Causal Graphs for Blocking in Randomized Experiments
Abhishek Kumar Umrawal
Randomized experiments are often performed to study the causal effects of interest. Blocking is a technique to precisely estimate the causal effects when the experimental material…
On Parameter Estimation in Unobserved Components Models subject to Linear Inequality Constraints
Abhishek K. Umrawal, Joshua C. C. Chan
We propose a new \textit{quadratic programming-based} method of approximating a nonstandard density using a multivariate Gaussian density. Such nonstandard densities usually arise…
DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer Programming for Multi-transfer Truck Freight Delivery
Jiayu Chen, Abhishek K. Umrawal, Tian Lan +1
With the freight delivery demands and shipping costs increasing rapidly, intelligent control of fleets to enable efficient and cost-conscious solutions becomes an important problem…
DART: aDaptive Accept RejecT for non-linear top-K subset identification
Mridul Agarwal, Vaneet Aggarwal, Christopher J. Quinn +1
We consider the bandit problem of selecting out of arms at each time step. The reward can be a non-linear function of the rewards of the selected individual arms. The direc…
FlexPool: A Distributed Model-Free Deep Reinforcement Learning Algorithm for Joint Passengers & Goods Transportation
Kaushik Manchella, Abhishek K. Umrawal, Vaneet Aggarwal
The growth in online goods delivery is causing a dramatic surge in urban vehicle traffic from last-mile deliveries. On the other hand, ride-sharing has been on the rise with the su…
Stochastic Top- Subset Bandits with Linear Space and Non-Linear Feedback
Mridul Agarwal, Vaneet Aggarwal, Christopher J. Quinn +1
Many real-world problems like Social Influence Maximization face the dilemma of choosing the best out of options at a given time instant. This setup can be modeled as a com…