12 papers
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…
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…
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…
Obliviate: Efficient Unlearning in Recommender Systems
Tushar Prakash, Brijraj Singh, Niranjan Pedanekar +1
Machine unlearning is becoming increasingly critical in the context of data privacy regulations, particularly for recommendation systems that are directly trained on user interacti…
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…
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…
Demystifying ChatGPT: How It Masters Genre Recognition
Subham Raj, Sriparna Saha, Brijraj Singh +1
The introduction of ChatGPT has garnered significant attention within the NLP community and beyond. Previous studies have demonstrated ChatGPT's substantial advancements across var…
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…
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…
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,…
MParrotTTS: Multilingual Multi-speaker Text to Speech Synthesis in Low Resource Setting
Neil Shah, Vishal Tambrahalli, Saiteja Kosgi +2
We present MParrotTTS, a unified multilingual, multi-speaker text-to-speech (TTS) synthesis model that can produce high-quality speech. Benefiting from a modularized training parad…
I Know Therefore I Score: Label-Free Crafting of Scoring Functions using Constraints Based on Domain Expertise
Ragja Palakkadavath, Sarath Sivaprasad, Shirish Karande +1
Several real-life applications require crafting concise, quantitative scoring functions (also called rating systems) from measured observations. For example, an effectiveness score…