Dr Sham Puthiya Parambath

  • Research Associate (School of Computing Science)

Biography

I, Shameem Ahamed Puthiya Parambath, am a member of the Knowledge & Data Engineering Systems (KDES)  under the research group Information, Data and Analysis (IDA) in the School of Computing Science, University of Glasgow, working with Dr Christos Anagnostopoulos and Prof. Roderick Murray-Smith.

I am from a small, beautiful coastal town named Thalassery in the state of Kerala, India, where I spent most of my life. My home is next to the wonderful Government Brennen College, Dharmadom, near a river. I spent two unforgettable years of my life in Brennen.

I completed PhD in Machine Learning from the University of Technology Compiegne (Sorbonne University Association). During that period, I was fortunate to be supervised by Dr. Nicolas Usunier and Dr. Yves Grandvalet. During my Masters at Umea University, I had the fortune of working with Dr. Sihem Amer-Yahia.

Before joining the University of Glasgow, I worked as a Postdoctoral Researcher at Qatar Computing Research Institute, Doha, Qatar. My current work looks at improving sequential algorithms for tasks like dynamic pricing, federated learning, edge computing, etc. At QCRI, I worked in Recommender Systems, Knowledge Graphs and Anomaly Detection. My work on group recommendation and the cold-start recommendation was published in AAAI and ECML conferences. My work on quantifying bias in the knowledge graphs was published in UAI. During my PhD thesis, I worked on multi-objective learning algorithms for multi-class/multi-label classification and personalized recommendations, which was published at venues such as NeurIPS and RecSys.

Research interests

 

  • Learning with feedback
  • Mutli-Armed Bandits
  • Recommender Systems
  • Federated Learning

 

Google Scholar: [link]

Publications

List by: Type | Date

Jump to: 2023 | 2022 | 2021 | 2020 | 2018 | 2017 | 2016 | 2014
Number of items: 9.

2023

Long, Q. , Anagnostopoulos, C. , Puthiya, S. and Bi, D. (2023) FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental Regularization. In: IEEE ICDM 2023, Shanghai, China, 1-4 December 2023, (Accepted for Publication)

2022

Puthiya Parambath, S. A. , Liu, S., Anagnostopoulos, C. , Murray-Smith, R. and Ounis, I. (2022) Parameter Tuning of Reranking-based Diversification Algorithms using Total Curvature Analysis. In: 8th ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR 2022), Madrid, Spain, 11-12 July 2022, ISBN 9781450394123 (doi: 10.1145/3539813.3545135)

2021

Puthiya Parambath, S. , Anagnostopoulos, C. , Murray-Smith, R. , MacAvaney, S. and Zervas, E. (2021) Max-Utility Based Arm Selection Strategy for Sequential Query Recommendations. In: 13th Asian Conference on Machine Learning (ACML 2021), 17-19 Nov 2021, pp. 564-579.

2020

Puthiya Parambath, S. A. and Chawla, S. (2020) Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations. Data Mining and Knowledge Discovery, 34(5), pp. 1560-1588. (doi: 10.1007/s10618-020-00708-6)

Mohamed, A., Parambath, S. A. , Kaoudi, Z. and Aboulnaga, A. (2020) Popularity Agnostic Evaluation of Knowledge Graph Embeddings. In: Thirty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI 2020), 3-6 Aug 2020, pp. 1059-1068.

2018

Puthiya Parambath, S. A. , Vijayakumar, N. and Chawla, S. (2018) SAGA: A Submodular Greedy Algorithm for Group Recommendation. In: Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, LA, USA, 2-7 Feb 2018, pp. 3900-3908.

2017

Sarkar, S., Chawla, S., Ahmad, S. , Srivastava, J., Hammady, H., Filali, F., Znaidi, W. and Borge-Holthoefer, J. (2017) Effective urban structure inference from traffic flow dynamics. IEEE Transactions on Big Data, 3(2), pp. 181-193. (doi: 10.1109/TBDATA.2016.2641003)

2016

Puthiya Parambath, S. A. , Usunier, N. and Grandvalet, Y. (2016) A Coverage-Based Approach to Recommendation Diversity On Similarity Graph. In: Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16), Boston, MA, USA, 15-19 Sept 2016, pp. 15-22. ISBN 9781450340359 (doi: 10.1145/2959100.2959149)

2014

Puthiya Parambath, S. , Usunier, N. and Grandvalet, Y. (2014) Optimizing F-measures by Cost-sensitive Classification. In: Advances in Neural Information Processing Systems 27 (NIPS 2014), Montreal, Canada, 8-13 Dec 2014, pp. 2123-2131.

This list was generated on Sun Sep 24 12:39:58 2023 BST.
Jump to: Articles | Conference Proceedings
Number of items: 9.

Articles

Puthiya Parambath, S. A. and Chawla, S. (2020) Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations. Data Mining and Knowledge Discovery, 34(5), pp. 1560-1588. (doi: 10.1007/s10618-020-00708-6)

Sarkar, S., Chawla, S., Ahmad, S. , Srivastava, J., Hammady, H., Filali, F., Znaidi, W. and Borge-Holthoefer, J. (2017) Effective urban structure inference from traffic flow dynamics. IEEE Transactions on Big Data, 3(2), pp. 181-193. (doi: 10.1109/TBDATA.2016.2641003)

Conference Proceedings

Long, Q. , Anagnostopoulos, C. , Puthiya, S. and Bi, D. (2023) FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental Regularization. In: IEEE ICDM 2023, Shanghai, China, 1-4 December 2023, (Accepted for Publication)

Puthiya Parambath, S. A. , Liu, S., Anagnostopoulos, C. , Murray-Smith, R. and Ounis, I. (2022) Parameter Tuning of Reranking-based Diversification Algorithms using Total Curvature Analysis. In: 8th ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR 2022), Madrid, Spain, 11-12 July 2022, ISBN 9781450394123 (doi: 10.1145/3539813.3545135)

Puthiya Parambath, S. , Anagnostopoulos, C. , Murray-Smith, R. , MacAvaney, S. and Zervas, E. (2021) Max-Utility Based Arm Selection Strategy for Sequential Query Recommendations. In: 13th Asian Conference on Machine Learning (ACML 2021), 17-19 Nov 2021, pp. 564-579.

Mohamed, A., Parambath, S. A. , Kaoudi, Z. and Aboulnaga, A. (2020) Popularity Agnostic Evaluation of Knowledge Graph Embeddings. In: Thirty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI 2020), 3-6 Aug 2020, pp. 1059-1068.

Puthiya Parambath, S. A. , Vijayakumar, N. and Chawla, S. (2018) SAGA: A Submodular Greedy Algorithm for Group Recommendation. In: Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, LA, USA, 2-7 Feb 2018, pp. 3900-3908.

Puthiya Parambath, S. A. , Usunier, N. and Grandvalet, Y. (2016) A Coverage-Based Approach to Recommendation Diversity On Similarity Graph. In: Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16), Boston, MA, USA, 15-19 Sept 2016, pp. 15-22. ISBN 9781450340359 (doi: 10.1145/2959100.2959149)

Puthiya Parambath, S. , Usunier, N. and Grandvalet, Y. (2014) Optimizing F-measures by Cost-sensitive Classification. In: Advances in Neural Information Processing Systems 27 (NIPS 2014), Montreal, Canada, 8-13 Dec 2014, pp. 2123-2131.

This list was generated on Sun Sep 24 12:39:58 2023 BST.

Supervision

Teaching

Database Theory & Applications

Additional information

Reviewer for AAAI 2024, 2021, 2020, 2019; AISTATS 2023, 2022; CIKM 2020; SDM 2020