activity
20232026
collaborators

8 papers

cs.IR2026

ATLAS: Learning to Recommend Across Unseen Domains

Pervez Shaik, Prosenjit Biswas, Abhinav Thorat +2

Recommender systems remain domain-bound: a model trained on one interaction environment typically requires retraining or target-domain adaptation before it can operate on a new cat…

cs.LG2026

GENESIS: Towards Explainable Causal Discovery

Abhinav Thorat, Ravi Kumar Kolla, Vishak K Bhat +2

Causal Discovery (CD) from observational data faces two fundamental challenges. First, purely statistical methods often lack the power to resolve structural ambiguities in low-samp…

cs.IR2026

RecRec: Recursive Refinement for Sequential Recommendation

Pervez Shaik, Prosenjit Biswas, Abhinav Thorat +2

Sequential recommender systems typically infer user preferences through single-pass encoding of interaction histories without iterative refinement, relying on increasingly deep arc…

cs.CL2026

PlotTwist: A Creative Plot Generation Framework with Small Language Models

Abhinav Thorat, Ravi Kolla, Jyotin Goel +2

Creative plot generation presents a fundamental challenge for language models: transforming a concise premise into a coherent narrative that sustains global coherence, character de…

cs.CL2025

From What to Why: Thought-Space Recommendation with Small Language Models

Prosenjit Biswas, Pervez Shaik, Abhinav Thorat +2

Large Language Models (LLMs) have advanced recommendation capabilities through enhanced reasoning, but pose significant challenges for real-world deployment due to high inference c…

cs.LG2025

KANITE: Kolmogorov-Arnold Networks for ITE estimation

Eshan Mehendale, Abhinav Thorat, Ravi Kolla +1

We introduce KANITE, a framework leveraging Kolmogorov-Arnold Networks (KANs) for Individual Treatment Effect (ITE) estimation under multiple treatments setting in causal inference…

cs.AI2024

I See, Therefore I Do: Estimating Causal Effects for Image Treatments

Abhinav Thorat, Ravi Kolla, Niranjan Pedanekar

Causal effect estimation under observational studies is challenging due to the lack of ground truth data and treatment assignment bias. Though various methods exist in literature f…

cs.LG2023

Estimation of individual causal effects in network setup for multiple treatments

Abhinav Thorat, Ravi Kolla, Niranjan Pedanekar +1

We study the problem of estimation of Individual Treatment Effects (ITE) in the context of multiple treatments and networked observational data. Leveraging the network information,…