Ashok Chandrashekar

According to our database, Ashok Chandrashekar authored at least 14 papers between 1998 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

CLIME: Completeness-Constrained LIME.

Claudia V. Roberts, Ehtsham Elahi, Ashok Chandrashekar

Proceedings of the Companion Proceedings of the ACM Web Conference 2023, 2023

2022

On the Bias-Variance Characteristics of LIME and SHAP in High Sparsity Movie Recommendation Explanation Tasks.

Claudia V. Roberts, Ehtsham Elahi, Ashok Chandrashekar

CoRR, 2022

Selectively Contextual Bandits.

Claudia V. Roberts, Maria Dimakopoulou, Qifeng Qiao, Ashok Chandrashekar, Tony Jebara

CoRR, 2022

On Negative Sampling for Audio-Visual Contrastive Learning from Movies.

Mahdi M. Kalayeh, Shervin Ardeshir, Lingyi Liu, Nagendra Kamath, Ashok Chandrashekar

CoRR, 2022

2021

Watching Too Much Television is Good: Self-Supervised Audio-Visual Representation Learning from Movies and TV Shows.

Mahdi M. Kalayeh, Nagendra Kamath, Lingyi Liu, Ashok Chandrashekar

CoRR, 2021

Off-Policy Evaluation of Slate Policies under Bayes Risk.

Nikos Vlassis, Fernando Amat Gil, Ashok Chandrashekar

CoRR, 2021

Bias-Variance Decomposition for Ranking.

Pannaga Shivaswamy, Ashok Chandrashekar

Proceedings of the WSDM '21, 2021

Control Variates for Slate Off-Policy Evaluation.

Nikos Vlassis, Ashok Chandrashekar, Fernando Amat Gil, Nathan Kallus

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Learning Representations of Hierarchical Slates in Collaborative Filtering.

Ehtsham Elahi, Ashok Chandrashekar

Proceedings of the RecSys 2020: Fourteenth ACM Conference on Recommender Systems, 2020

2018

Artwork personalization at netflix.

Fernando Amat Gil, Ashok Chandrashekar, Tony Jebara, Justin Basilico

Proceedings of the 12th ACM Conference on Recommender Systems, 2018

2014

Learning what is where from unlabeled images: joint localization and clustering of foreground objects.

Ashok Chandrashekar, Lorenzo Torresani, Richard Granger

Mach. Learn., 2014

2011

Derivation of a Novel Efficient Supervised Learning Algorithm from Cortical-Subcortical Loops.

Ashok Chandrashekar, Richard Granger

Frontiers Comput. Neurosci., 2011

2007

Novel Brain-Derived Algorithms Scale Linearly with Number of Processing Elements.

Jeff Furlong, Andrew Felch, Jayram Moorkanikara Nageswaran, Nikil D. Dutt, Alex Nicolau, Alexander V. Veidenbaum, Ashok Chandrashekar, Richard Granger

Proceedings of the Parallel Computing: Architectures, 2007

1998

A modelling study of the effects of continuous incremental improvement in the case of a process shop.

Ashok Chandrashekar, Thomas E. Callarman

Eur. J. Oper. Res., 1998