15 citations · 18 across the 8 of their papers we have counts for
8 papers
HIERA: Hierarchical Multi-Agent Relevance Assessment for Content Discovery Systems
Pritom Saha Akash, Phanideep Gampa, Chao Shen +2
Content discovery systems depend on relevance judgment for search quality evaluation, but human annotation faces inter-annotator disagreement and scaling costs. While Large Languag…
LLM-based Weak Supervision Framework for Query Intent Classification in Video Search
Farnoosh Javadi, Phanideep Gampa, Alyssa Woo +5
Streaming services have reshaped how we discover and engage with digital entertainment. Despite these advancements, effectively understanding the wide spectrum of user search queri…
Multi-Task Learning For Reduced Popularity Bias In Multi-Territory Video Recommendations
Phanideep Gampa, Farnoosh Javadi, Belhassen Bayar +1
Various data imbalances that naturally arise in a multi-territory personalized recommender system can lead to a significant item bias for globally prevalent items. A locally popula…
Design Principles of Robust Multi-Armed Bandit Framework in Video Recommendations
Belhassen Bayar, Phanideep Gampa, Ainur Yessenalina +1
Current multi-armed bandit approaches in recommender systems (RS) have focused more on devising effective exploration techniques, while not adequately addressing common exploitatio…
Dynamics of Local Elasticity During Training of Neural Nets
Soham Dan, Anirbit Mukherjee, Avirup Das +1
In the recent past, a property of neural training trajectories in weight-space had been isolated, that of "local elasticity" (denoted as ). Local elasticity attempts t…
Object Files and Schemata: Factorizing Declarative and Procedural Knowledge in Dynamical Systems
Anirudh Goyal, Alex Lamb, Phanideep Gampa +5
Modeling a structured, dynamic environment like a video game requires keeping track of the objects and their states declarative knowledge) as well as predicting how objects behave…
BanditRank: Learning to Rank Using Contextual Bandits
Phanideep Gampa, Sumio Fujita
We propose an extensible deep learning method that uses reinforcement learning to train neural networks for offline ranking in information retrieval (IR). We call our method Bandit…
A Tractable Algorithm For Finite-Horizon Continuous Reinforcement Learning
Phanideep Gampa, Sairam Satwik Kondamudi, Lakshmanan Kailasam
We consider the finite horizon continuous reinforcement learning problem. Our contribution is three-fold. First,we give a tractable algorithm based on optimistic value iteration fo…