Search Results for author: Phanideep Gampa

Found 7 papers, 1 papers with code

Multi-Task Learning For Reduced Popularity Bias In Multi-Territory Video Recommendations

no code implementations25 Sep 2023 Phanideep Gampa, Farnoosh Javadi, Belhassen Bayar, Ainur Yessenalina

Our proposed framework is designed to enrich training examples with active users representation through upsampling, and capable of learning geographic-based user embeddings by leveraging MTL.

Multi-Task Learning Recommendation Systems

Design Principles of Robust Multi-Armed Bandit Framework in Video Recommendations

no code implementations24 Sep 2023 Belhassen Bayar, Phanideep Gampa, Ainur Yessenalina, Zhen Wen

Current multi-armed bandit approaches in recommender systems (RS) have focused more on devising effective exploration techniques, while not adequately addressing common exploitation challenges related to distributional changes and item cannibalization.

Fairness Recommendation Systems

Dynamics of Local Elasticity During Training of Neural Nets

1 code implementation1 Nov 2021 Soham Dan, Anirbit Mukherjee, Avirup Das, Phanideep Gampa

On various state-of-the-art neural network training on SVHN, CIFAR-10 and CIFAR-100 we demonstrate how our new proposal of $S_{\rm rel}$, as opposed to the original definition, much more sharply detects the property of the weight updates preferring to make prediction changes within the same class as the sampled data.

regression

Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments

no code implementations ICLR 2021 Anirudh Goyal, Alex Lamb, Phanideep Gampa, Philippe Beaudoin, Charles Blundell, Sergey Levine, Yoshua Bengio, Michael Curtis Mozer

To use a video game as an illustration, two enemies of the same type will share schemata but will have separate object files to encode their distinct state (e. g., health, position).

Object

Object Files and Schemata: Factorizing Declarative and Procedural Knowledge in Dynamical Systems

no code implementations29 Jun 2020 Anirudh Goyal, Alex Lamb, Phanideep Gampa, Philippe Beaudoin, Sergey Levine, Charles Blundell, Yoshua Bengio, Michael Mozer

To use a video game as an illustration, two enemies of the same type will share schemata but will have separate object files to encode their distinct state (e. g., health, position).

Object

BanditRank: Learning to Rank Using Contextual Bandits

no code implementations23 Oct 2019 Phanideep Gampa, Sumio Fujita

In the domain of learning to rank for IR, current deep learning models are trained on objective functions different from the measures they are evaluated on.

Information Retrieval Learning-To-Rank +3

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