A Probabilistic Least Squares Approach to Ordinal Regression. - P. K. Srijith; Shirish K. Shevade; S. Sundararajan (Australasian Conference on Artificial Intelligence, 2012)

Multi-Task Learning Using Shared and Task Specific Information. - P. K. Srijith; Shirish K. Shevade (ICONIP (3), 2012)

Validation Based Sparse Gaussian Processes for Ordinal Regression. - P. K. Srijith; Shirish K. Shevade; S. Sundararajan (ICONIP (2), 2012)

Semi-supervised Gaussian Process Ordinal Regression. - P. K. Srijith; Shirish K. Shevade; S. Sundararajan (ECML/PKDD (3), 2013)

Gaussian Process Multi-task Learning Using Joint Feature Selection. - P. K. Srijith; Shirish K. Shevade (ECML/PKDD (3), 2014)

Gaussian Process Pseudo-Likelihood Models for Sequence Labeling. - P. K. Srijith; Balamurugan P.; Shirish K. Shevade (CoRR, 2014)

Modeling Tweet Arrival Times using Log-Gaussian Cox Processes. - Michal Lukasik; P. K. Srijith; Trevor Cohn; Kalina Bontcheva (EMNLP, 2015)

Hawkes Processes for Continuous Time Sequence Classification: an Application to Rumour Stance Classification in Twitter. - Michal Lukasik; P. K. Srijith; Duy Vu; Kalina Bontcheva; Arkaitz Zubiaga; Trevor Cohn (ACL (2), 2016)

Studying the Temporal Dynamics of Word Co-occurrences: An Application to Event Detection. - Daniel Preotiuc-Pietro; P. K. Srijith; Mark Hepple; Trevor Cohn (LREC, 2016)

Gaussian Process Pseudo-Likelihood Models for Sequence Labeling. - P. K. Srijith; P. Balamurugan 001; Shirish K. Shevade (ECML/PKDD (1), 2016)

Longitudinal Modeling of Social Media with Hawkes Process Based on Users and Networks. - P. K. Srijith; Michal Lukasik; Kalina Bontcheva; Trevor Cohn (ASONAM, 2017)

Sub-story detection in Twitter with hierarchical Dirichlet processes. - P. K. Srijith; Mark Hepple; Kalina Bontcheva; Daniel Preotiuc-Pietro (Inf. Process. Manag., 2017)

Classification of Short-Texts Generated During Disasters: A Deep Neural Network Based Approach. - Shamik Kundu; P. K. Srijith; Maunendra Sankar Desarkar (ASONAM, 2018)

Accelerating Hawkes process for event history data: Application to social networks and recommendation systems. - Ashwin Ram 002; P. K. Srijith (COMSNETS, 2018)

A Bayesian Point Process Model for User Return Time Prediction in Recommendation Systems. - Sherin Thomas; P. K. Srijith; Michal Lukasik (UMAP, 2018)

Deep Gaussian Processes with Convolutional Kernels. - Vinayak Kumar; Vaibhav Singh; P. K. Srijith; Andreas C. Damianou (CoRR, 2018)

Term Specific TF-IDF Boosting for Detection of Rumours in Social Networks. - Uddipta Bhattacharjee; Srijith P. K; Maunendra Sankar Desarkar (COMSNETS, 2019)

Leveraging Social Media Towards Understanding Anti-Vaccination Campaigns. - Uddipta Bhattacharjee; P. K. Srijith; Maunendra Sankar Desarkar (COMSNETS, 2019)

Decision Making under Uncertainty with Convolutional Deep Gaussian Processes. - Dinesh Jain; Srinivas Anumasa; P. K. Srijith (COMAD/CODS, 2020)

HAP-SAP: Semantic Annotation in LBSNs using Latent Spatio-Temporal Hawkes Process. - Manisha Dubey; P. K. Srijith; Maunendra Sankar Desarkar (SIGSPATIAL/GIS, 2020)

Improving Adaptive Bayesian Optimization with Spectral Mixture Kernel. - Harsh Raj; Suvodip Dey; Hiransh Gupta; P. K. Srijith (ICONIP (5), 2020)

STM-GAN: Sequentially Trained Multiple Generators for Mitigating Mode Collapse. - Sakshi Varshney; P. K. Srijith; Vineeth N. Balasubramanian (ICONIP (5), 2020)

Evaluation of Deep Gaussian Processes for Text Classification. - P. Jayashree 001; P. K. Srijith (LREC, 2020)

Modeling Implicit Communities from Geo-Tagged Event Traces Using Spatio-Temporal Point Processes. - Ankita Likhyani; Vinayak Gupta; Srijith P. K; Deepak P 001; Srikanta Bedathur (WISE (1), 2020)

Modeling Implicit Communities using Spatio-Temporal Point Processes from Geo-tagged Event Traces. - Ankita Likhyani; Vinayak Gupta; Srijith P. K; Deepak P 001; Srikanta Bedathur (CoRR, 2020)

Multi-view hypergraph convolution network for semantic annotation in LBSNs. - Manisha Dubey; P. K. Srijith; Maunendra Sankar Desarkar (ASONAM, 2021)

Learning Multi-Sense Word Distributions using Approximate Kullback-Leibler Divergence. - P. Jayashree 001; Ballijepalli Shreya; P. K. Srijith (COMAD/CODS, 2021)

Bayesian Generative Adversarial Nets with Dropout Inference. - Ragja Palakkadavath; P. K. Srijith (COMAD/CODS, 2021)

Adiabatic Quantum Feature Selection for Sparse Linear Regression. - Surya Sai Teja Desu; P. K. Srijith; M. V. Panduranga Rao; Naveen Sivadasan (ICCS (6), 2021)

Delay Differential Neural Networks. - Srinivas Anumasa; P. K. Srijith (ICMLT, 2021)

CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks. - Sakshi Varshney; Vinay Kumar Verma; P. K. Srijith; Lawrence Carin; Piyush Rai (NeurIPS, 2021)

Subset-of-data variational inference for deep Gaussian-processes regression. - Ayush Jain; P. K. Srijith; Mohammad Emtiyaz Khan (UAI, 2021)

Traffic Incident Duration Prediction using BERT Representation of Text. - Prashansa Agrawal; A. Antony Franklin; Digvijay S. Pawar; Srijith P. K (VTC Fall, 2021)

Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations. - Srinivas Anumasa; P. K. Srijith (WACV, 2021)

Monte Carlo DropBlock for Modelling Uncertainty in Object Detection. - Kumari Deepshikha; Sai Harsha Yelleni; P. K. Srijith; C. Krishna Mohan (CoRR, 2021)

Bi-Directional Recurrent Neural Ordinary Differential Equations for Social Media Text Classification. - Maunika Tamire; Srinivas Anumasa; P. K. Srijith (CoRR, 2021)

Latent Time Neural Ordinary Differential Equations. - Srinivas Anumasa; P. K. Srijith (AAAI, 2022)

Cosmic Ray Detection in Astronomical Images via Dictionary Learning and Sparse Representation. - Srinadh Reddy Bhavanam; Sumohana S. Channappayya; P. K. Srijith; Shantanu Desai (EUSIPCO, 2022)

Predicting Reputation Score of Users in Stack-overflow with Alternate Data. - Sahil Yerawar; Sagar Jinde; P. K. Srijith; Maunendra Sankar Desarkar; K. M. Annervaz; Shubhashis Sengupta (KDIR, 2022)

Hawkes Process Classification through Discriminative Modeling of Text. - Rohan Tondulkar; Manisha Dubey; P. K. Srijith; Michal Lukasik (IJCNN, 2022)

Cosmic Ray rejection with attention augmented deep learning. - Srinadh Reddy Bhavanam; Sumohana S. Channappayya; P. K. Srijith; Shantanu Desai (Astron. Comput., 2022)

Galaxy morphology classification using neural ordinary differential equations. - R. Gupta; P. K. Srijith; Shantanu Desai (Astron. Comput., 2022)

HyperHawkes: Hypernetwork based Neural Temporal Point Process. - Manisha Dubey; P. K. Srijith; Maunendra Sankar Desarkar (CoRR, 2022)

Towards Generalized and Explainable Long-Range Context Representation for Dialogue Systems. - Suvodip Dey; Maunendra Sankar Desarkar; P. K. Srijith (CoRR, 2022)

Event Uncertainty using Ensemble Neural Hawkes Process. - Manisha Dubey; Ragja Palakkadavath; P. K. Srijith (COMAD/CODS, 2023)

Monte Carlo Dropout Based BatchEnsemble For Improving Uncertainty Estimation. - Shubham Jain; Srijith P. K (COMAD/CODS, 2023)

Time-to-Event Modeling with Hypernetwork based Hawkes Process. - Manisha Dubey; P. K. Srijith; Maunendra Sankar Desarkar (KDD, 2023)

DialoGen: Generalized Long-Range Context Representation for Dialogue Systems. - Maunendra Sankar Desarkar Suvodip Dey; Asif Ekbal; P. K. Srijith (PACLIC, 2023)

Continuous Depth Recurrent Neural Differential Equations. - Srinivas Anumasa; Geetakrishnasai Gunapati; P. K. Srijith (ECML/PKDD (2), 2023)

Continual Learning with Dependency Preserving Hypernetworks. - Dupati Srikar Chandra; Sakshi Varshney; P. K. Srijith; Sunil Gupta 001 (WACV, 2023)

Drift-aware Neural Hawkes Process for Time-to-Event Modelling. - Vaibhav Shah; Manisha Dubey; Vishnuprasadh Kumaravelu; P. K. Srijith (COMAD/CODS (December), 2024)

TL-CL: Task And Language Incremental Continual Learning. - Shrey Satapara; P. K. Srijith (EMNLP, 2024)

Transformer based Multitask Learning for Image Captioning and Object Detection. - Debolena Basak; P. K. Srijith; Maunendra Sankar Desarkar (PAKDD (2), 2024)

Monte Carlo DropBlock for modeling uncertainty in object detection. - Sai Harsha Yelleni; Deepshikha Kumari; Srijith P. K; Krishna Mohan C (Pattern Recognit., 2024)

Neural Wave Equation for Irregularly Sampled Sequence Data. - Arkaprava Majumdar; M. Anand Krishna; P. K. Srijith (ICLR, 2025)

Linked Adapters: Linking Past and Future to Present for Effective Continual Learning. - Dupati Srikar Chandra; P. K. Srijith; Dana Rezazadegan; Chris McCarthy (PAKDD (1), 2025)

AdaPrefix++: Integrating Adapters, Prefixes and Hypernetwork for Continual Learning. - Sayanta Adhikari; Dupati Srikar Chandra; P. K. Srijith; Pankaj Wasnik; Naoyuki Onoe (WACV, 2025)

EvoCL: Continual Learning over Evolving Domains. - Vishnuprasadh Kumaravelu; P. K. Srijith; Sunil Gupta 001 (WACV, 2025)

Efficient Uncertainty in LLMs through Evidential Knowledge Distillation. - Lakshmana Sri Harsha Nemani; P. K. Srijith; Tomasz Kusmierczyk (CoRR, 2025)

Bayesian neural hawkes process for event uncertainty prediction. - Manisha Dubey; Ragja Palakkadavath; P. K. Srijith (Int. J. Data Sci. Anal., 2025)