An improved Multiobjective Evolutionary Algorithm based on decomposition with fuzzy dominance. - Mohammed Nasir; Arnab Kumar Mondal; Soumyadip Sengupta; Swagatam Das; Ajith Abraham (IEEE Congress on Evolutionary Computation, 2011)

An Improved Multi-Objective Algorithm Based on Decomposition with Fuzzy Dominance for Deployment of Wireless Sensor Networks. - Soumyadip Sengupta; Md. Nasir; Arnab Kumar Mondal; Swagatam Das (SEMCCO (1), 2011)

Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning. - Arnab Kumar Mondal; Jose Dolz; Christian Desrosiers (CoRR, 2018)

Revisiting CycleGAN for semi-supervised segmentation. - Arnab Kumar Mondal; Aniket Agarwal; Jose Dolz; Christian Desrosiers (CoRR, 2019)

Towards Latent Space Optimality for Auto-Encoder Based Generative Models. - Arnab Kumar Mondal; Sankalan Pal Chowdhury; Aravind Jayendran; Parag Singla; Himanshu Asnani; Prathosh A. P. (CoRR, 2019)

C-MI-GAN : Estimation of Conditional Mutual Information using MinMax formulation. - Arnab Kumar Mondal; Arnab Bhattacharjee; Sudipto Mukherjee 001; Himanshu Asnani; Sreeram Kannan; Prathosh A. P. (UAI, 2020)

MaskAAE: Latent space optimization for Adversarial Auto-Encoders. - Arnab Kumar Mondal; Sankalan Pal Chowdhury; Aravind Jayendran; Himanshu Asnani; Parag Singla; Prathosh A. P. (UAI, 2020)

C-MI-GAN : Estimation of Conditional Mutual Information using MinMax formulation. - Arnab Kumar Mondal; Arnab Bhattacharya 009; Sudipto Mukherjee 001; Sreeram Kannan; Himanshu Asnani; Prathosh AP (CoRR, 2020)

To Regularize or Not To Regularize? The Bias Variance Trade-off in Regularized AEs. - Arnab Kumar Mondal; Himanshu Asnani; Parag Singla; Prathosh AP (CoRR, 2020)

Group Equivariant Deep Reinforcement Learning. - Arnab Kumar Mondal; Pratheeksha Nair; Kaleem Siddiqi (CoRR, 2020)

RespVAD: Voice Activity Detection via Video-Extracted Respiration Patterns. - Arnab Kumar Mondal; Prathosh A. P. (CoRR, 2020)

Mini-batch Similarity Graphs for Robust Image Classification. - Arnab Kumar Mondal; Vineet Jain; Kaleem Siddiqi (BMVC, 2021)

FlexAE: flexibly learning latent priors for wasserstein auto-encoders. - Arnab Kumar Mondal; Himanshu Asnani; Parag Singla; A. P. Prathosh (UAI, 2021)

Mini-batch graphs for robust image classification. - Arnab Kumar Mondal; Vineet Jain; Kaleem Siddiqi (CoRR, 2021)

SCLAiR : Supervised Contrastive Learning for User and Device Independent Airwriting Recognition. - Ayush Tripathi; Arnab Kumar Mondal; Lalan Kumar; Prathosh A. P. (CoRR, 2021)

EqR: Equivariant Representations for Data-Efficient Reinforcement Learning. - Arnab Kumar Mondal; Vineet Jain; Kaleem Siddiqi; Siamak Ravanbakhsh (ICML, 2022)

$ alpha$-ReQ : Assessing Representation Quality in Self-Supervised Learning by measuring eigenspectrum decay. - Kumar Krishna Agrawal; Arnab Kumar Mondal; Arna Ghosh; Blake A. Richards (NeurIPS, 2022)

Structuring Representations Using Group Invariants. - Mehran Shakerinava; Arnab Kumar Mondal; Siamak Ravanbakhsh (NeurIPS, 2022)

COVID-19 prognosis using limited chest X-ray images. - Arnab Kumar Mondal (Appl. Soft Comput., 2022)

Investigating Power laws in Deep Representation Learning. - Arna Ghosh; Arnab Kumar Mondal; Kumar Krishna Agrawal; Blake A. Richards (CoRR, 2022)

Transformation Coding: Simple Objectives for Equivariant Representations. - Mehran Shakerinava; Arnab Kumar Mondal; Siamak Ravanbakhsh (CoRR, 2022)

ImAiR : Airwriting Recognition framework using Image Representation of IMU Signals. - Ayush Tripathi; Arnab Kumar Mondal; Lalan Kumar; Prathosh A. P. (CoRR, 2022)

scRAE: Deterministic Regularized Autoencoders With Flexible Priors for Clustering Single-Cell Gene Expression Data. - Arnab Kumar Mondal; Himanshu Asnani; Parag Singla; Prathosh AP (IEEE ACM Trans. Comput. Biol. Bioinform., 2022)

Minority Oversampling for Imbalanced Data via Class-Preserving Regularized Auto-Encoders. - Arnab Kumar Mondal; Lakshya Singhal; Piyush Tiwary; Parag Singla; Prathosh AP (AISTATS, 2023)

Image Manipulation via Multi-Hop Instructions - A New Dataset and Weakly-Supervised Neuro-Symbolic Approach. - Harman Singh; Poorva Garg; Mohit Gupta; Kevin Shah; Ashish Goswami; Satyam Modi; Arnab Kumar Mondal; Dinesh Khandelwal; Dinesh Garg; Parag Singla (EMNLP, 2023)

Few-shot Cross-domain Image Generation via Inference-time Latent-code Learning. - Arnab Kumar Mondal; Piyush Tiwary; Parag Singla; Prathosh AP (ICLR, 2023)

Hyperbolic Deep Reinforcement Learning for Continuous Control. - Omar Salemohamed; Edoardo Cetin; Sai Rajeswar; Arnab Kumar Mondal (Tiny Papers @ ICLR, 2023)

Equivariance with Learned Canonicalization Functions. - Sékou-Oumar Kaba; Arnab Kumar Mondal; Yan Zhang 067; Yoshua Bengio; Siamak Ravanbakhsh (ICML, 2023)

Equivariant Adaptation of Large Pretrained Models. - Arnab Kumar Mondal; Siba Smarak Panigrahi; Oumar Kaba; Sai Mudumba; Siamak Ravanbakhsh (NeurIPS, 2023)

Image Manipulation via Multi-Hop Instructions - A New Dataset and Weakly-Supervised Neuro-Symbolic Approach. - Harman Singh; Poorva Garg; Mohit Gupta; Kevin Shah; Arnab Kumar Mondal; Dinesh Khandelwal; Parag Singla; Dinesh Garg (CoRR, 2023)

Equivariant Adaptation of Large Pretrained Models. - Arnab Kumar Mondal; Siba Smarak Panigrahi; Sékou-Oumar Kaba; Sai Rajeswar; Siamak Ravanbakhsh (CoRR, 2023)

Clustering Single-Cell RNA Sequence Data Using Information Maximized and Noise-Invariant Representations. - Arnab Kumar Mondal; Indu Joshi; Pravendra Singh; Prathosh AP (IEEE ACM Trans. Comput. Biol. Bioinform., 2023)

Self-Supervised Representation Learning-Based OSA Detection Method Using Single-Channel ECG Signals. - Chandra Bhushan Kumar; Arnab Kumar Mondal; Manvir Bhatia; Bijaya Ketan Panigrahi; Tapan Kumar Gandhi (IEEE Trans. Instrum. Meas., 2023)

SSDMM-VAE: variational multi-modal disentangled representation learning. - Arnab Kumar Mondal; Ajay Sailopal; Parag Singla; Prathosh AP (Appl. Intell., 2023)

Deep Latent Space Clustering for Detection of Stealthy False Data Injection Attacks Against AC State Estimation in Power Systems. - Arnab Bhattacharjee; Arnab Kumar Mondal; Ashu Verma; Sukumar Mishra; Tapan Kumar Saha (IEEE Trans. Smart Grid, 2023)

HumMUSS: Human Motion Understanding Using State Space Models. - Arnab Kumar Mondal; Stefano Alletto; Denis Tomè (CVPR, 2024)

Efficient Dynamics Modeling in Interactive Environments with Koopman Theory. - Arnab Kumar Mondal; Siba Smarak Panigrahi; Sai Rajeswar; Kaleem Siddiqi; Siamak Ravanbakhsh (ICLR, 2024)

Stochastic Experience-Replay for Graph Continual Learning. - Arnab Kumar Mondal; Jay Nandy; Manohar Kaul; Mahesh Chandran (LoG, 2024)

Unravelling sleep patterns: Supervised contrastive learning with self-attention for sleep stage classification. - Chandra Bhushan Kumar; Arnab Kumar Mondal; Manvir Bhatia; Bijaya Ketan Panigrahi; Tapan Kumar Gandhi (Appl. Soft Comput., 2024)

Improved Canonicalization for Model Agnostic Equivariance. - Siba Smarak Panigrahi; Arnab Kumar Mondal (CoRR, 2024)

Learning Disentangled Representation in Object-Centric Models for Visual Dynamics Prediction via Transformers. - Sanket Gandhi; Atul; Samanyu Mahajan; Vishal Sharma; Rushil Gupta; Arnab Kumar Mondal; Parag Singla (CoRR, 2024)

SoLAD: Sampling Over Latent Adapter for Few Shot Generation. - Arnab Kumar Mondal; Piyush Tiwary; Parag Singla; Prathosh A. P. (IEEE Signal Process. Lett., 2024)

Teacher-Guided Graph Contrastive Learning. - Jay Nandy; Arnab Kumar Mondal; Manohar Kaul; Prathosh AP (Trans. Mach. Learn. Res., 2024)

Spectral State Space Model for Rotation-Invariant Visual Representation Learning. - Sahar Dastani; Ali Bahri; Moslem Yazdanpanah; Mehrdad Noori; David Osowiechi; Gustavo Adolfo Vargas Hakim; Farzad Beizaee; Milad Cheraghalikhani; Arnab Kumar Mondal; Herve Lombaert; Christian Desrosiers (CVPR, 2025)

Surprising Effectiveness of pretraining Ternary Language Model at Scale. - Ayush Kaushal; Tejas Vaidhya; Arnab Kumar Mondal; Tejas Pandey; Aaryan Bhagat; Irina Rish (ICLR, 2025)

Symmetry-Aware Generative Modeling through Learned Canonicalization. - Kusha Sareen; Daniel Levy; Arnab Kumar Mondal; Sékou-Oumar Kaba; Tara Akhound-Sadegh; Siamak Ravanbakhsh (CoRR, 2025)

Rendering-Aware Reinforcement Learning for Vector Graphics Generation. - Juan A. Rodríguez; Haotian Zhang; Abhay Puri; Aarash Feizi; Rishav Pramanik; Pascal Wichmann; Arnab Kumar Mondal; Mohammad Reza Samsami; Rabiul Awal; Perouz Taslakian; Spandana Gella; Sai Rajeswar; et al. (CoRR, 2025)