Fundamental Limits of Testing the Independence of Irrelevant Alternatives in Discrete Choice. - Arjun Seshadri; Johan Ugander (EC, 2019)

Discovering Context Effects from Raw Choice Data. - Arjun Seshadri; Alex Peysakhovich; Johan Ugander (ICML, 2019)

Learning Rich Rankings. - Arjun Seshadri; Stephen Ragain; Johan Ugander (NeurIPS, 2020)

Contrastive Learning for Interactive Recommendation in Fashion. - Karin Sevegnani; Arjun Seshadri; Tian Wang; Anurag Beniwal; Julian J. McAuley; Alan Lu; Gerard Medioni (CoRR, 2022)

RecXplainer: Post-Hoc Attribute-Based Explanations for Recommender Systems. - Sahil Verma; Anurag Beniwal; Narayanan Sadagopan; Arjun Seshadri (CoRR, 2022)

HandsOff: Labeled Dataset Generation With No Additional Human Annotations. - Austin Xu; Mariya I. Vasileva; Achal Dave; Arjun Seshadri (CVPR, 2023)

Rank-heterogeneous Preference Models for School Choice. - Amel Awadelkarim; Arjun Seshadri; Itai Ashlagi; Irene Lo; Johan Ugander (KDD, 2023)

Diffusion Soup: Model Merging for Text-to-Image Diffusion Models. - Benjamin Biggs; Arjun Seshadri; Yang Zou; Achin Jain; Aditya Golatkar; Yusheng Xie; Alessandro Achille; Ashwin Swaminathan; Stefano Soatto (ECCV (63), 2024)

B'MOJO: Hybrid State Space Realizations of Foundation Models with Eidetic and Fading Memory. - Luca Zancato; Arjun Seshadri; Yonatan Dukler; Aditya Golatkar; Yantao Shen 002; Benjamin Bowman; Matthew Trager; Alessandro Achille; Stefano Soatto (NeurIPS, 2024)

Re-visiting Skip-Gram Negative Sampling: Dimension Regularization for More Efficient Dissimilarity Preservation in Graph Embeddings. - David Liu 006; Arjun Seshadri; Tina Eliassi-Rad; Johan Ugander (CoRR, 2024)