Parallel Bi-dimensional Pattern Matching with Scaling. - Marghoob Mohiyuddin; Vidit Jain; Phalguni Gupta (IASTED PDCS, 2002)

Many Heads Are Better Than One: Jointly Removing Bias from Multiple MRIs Using Nonparametric Maximum Likelihood. - Erik G. Learned-Miller; Vidit Jain (IPMI, 2005)

Discriminative Training of Hyper-feature Models for Object Identification. - Vidit Jain; Andras Ferencz; Erik G. Learned-Miller (BMVC, 2006)

Unsupervised Joint Alignment of Complex Images. - Gary B. Huang; Vidit Jain; Erik G. Learned-Miller (ICCV, 2007)

People-LDA: Anchoring Topics to People using Face Recognition. - Vidit Jain; Erik G. Learned-Miller; Andrew McCallum (ICCV, 2007)

Selective hidden random fields: Exploiting domain-specific saliency for event classification. - Vidit Jain; Amit Singhal 001; Jiebo Luo 001 (CVPR, 2008)

Online domain adaptation of a pre-trained cascade of classifiers. - Vidit Jain; Erik G. Learned-Miller (CVPR, 2011)

Learning to re-rank: query-dependent image re-ranking using click data. - Vidit Jain; Manik Varma (WWW, 2011)

Adapting Classification Cascades to New Domains. - Vidit Jain; Sachin Sudhakar Farfade (ICCV, 2013)

Topical organization of user comments and application to content recommendation. - Vidit Jain; Esther Galbrun (WWW (Companion Volume), 2013)

Min-d-Occur: Ensuring Future Occurrences in Streaming Sets. - Vidit Jain; Sainyam Galhotra (UAI, 2014)

Short-text representation using diffusion wavelets. - Vidit Jain; Jay Mahadeokar (WWW (Companion Volume), 2014)

Tracking the Conductance of Rapidly Evolving Topic-Subgraphs. - Sainyam Galhotra; Amitabha Bagchi; Srikanta Bedathur; Maya Ramanath; Vidit Jain (Proc. VLDB Endow., 2015)

Deep Latent Factor Model for Predicting Drug Target Interactions. - Aanchal Mongia; Vidit Jain; Emilie Chouzenoux; Angshul Majumdar (ICASSP, 2019)

Deep Matrix Factorization on Graphs: Application to Collaborative Filtering. - Aanchal Mongia; Vidit Jain; Angshul Majumdar (ICONIP (4), 2020)

Exploring Semi-Supervised Learning for Predicting Listener Backchannels. - Vidit Jain; Maitree Leekha; Rajiv Ratn Shah; Jainendra Shukla (CHI, 2021)

Neural Models for Output-Space Invariance in Combinatorial Problems. - Yatin Nandwani; Vidit Jain; Mausam; Parag Singla (ICLR, 2022)

AENeT: an attention-enabled neural architecture for fake news detection using contextual features. - Vidit Jain; Rohit Kumar Kaliyar; Anurag Goswami; Pratik Narang; Yashvardhan Sharma (Neural Comput. Appl., 2022)

Renee: End-To-End Training of Extreme Classification Models. - Vidit Jain; Jatin Prakash; Deepak Saini; Jian Jiao 007; Ramachandran Ramjee; Manik Varma (MLSys, 2023)

NGAME: Negative Mining-aware Mini-batching for Extreme Classification. - Kunal Dahiya; Nilesh Gupta; Deepak Saini; Akshay Soni; Yajun Wang; Kushal Dave 001; Jian Jiao 007; Gururaj K; Prasenjit Dey; Amit Singh 003; Deepesh Hada; Vidit Jain; et al. (WSDM, 2023)

HiGen: Hierarchy-Aware Sequence Generation for Hierarchical Text Classification. - Vidit Jain; Mukund Rungta; Yuchen Zhuang; Yue Yu 001; Zeyu Wang; Mu Gao; Jeffrey Skolnick; Chao Zhang 014 (EACL (1), 2024)

Enhancing Tail Performance in Extreme Classifiers by Label Variance Reduction. - Anirudh Buvanesh; Rahul Chand; Jatin Prakash; Bhawna Paliwal; Mudit Dhawan; Neelabh Madan; Deepesh Hada; Vidit Jain; Sonu Mehta; Yashoteja Prabhu; Manish Gupta; Ramachandran Ramjee; et al. (ICLR, 2024)