27 citations · 39 across the 11 of their papers we have counts for
11 papers
Stateful Online Monitoring Catches Distributed Agent Attacks
Davis Brown, Samarth Bhargav, Arav Santhanam +7
Language models can find thousands of severe software vulnerabilities, and agents are increasingly being misused for cyberattacks. To avoid detection, attackers frequently distribu…
Me, Myself, and : Evaluating and Explaining LLM Introspection
Atharv Naphade, Samarth Bhargav, Sean Lim +1
A hallmark of human intelligence is Introspection-the ability to assess and reason about one's own cognitive processes. Introspection has emerged as a promising but contested capab…
A Comprehensive Taxonomy of Negation for NLP and Neural Retrievers
Roxana Petcu, Samarth Bhargav, Maarten de Rijke +1
Understanding and solving complex reasoning tasks is vital for addressing the information needs of a user. Although dense neural models learn contextualised embeddings, they still…
ZT-RIC:A Zero Trust RIC Framework for ensuring data Privacy and Confidentiality in Open RAN
Diana Lin, Samarth Bhargav, Azuka Chiejina +2
The advancement of 5G and NextG networks through Open Radio Access Network (O-RAN) architecture enables a shift toward virtualized, modular, and disaggregated configurations. A cor…
RecFusion: A Binomial Diffusion Process for 1D Data for Recommendation
Gabriel Bénédict, Olivier Jeunen, Samuele Papa +3
In this paper we propose RecFusion, which comprise a set of diffusion models for recommendation. Unlike image data which contain spatial correlations, a user-item interaction matri…
When the Music Stops: Tip-of-the-Tongue Retrieval for Music
Samarth Bhargav, Anne Schuth, Claudia Hauff
We present a study of Tip-of-the-tongue (ToT) retrieval for music, where a searcher is trying to find an existing music entity, but is unable to succeed as they cannot accurately r…
Market-Aware Models for Efficient Cross-Market Recommendation
Samarth Bhargav, Mohammad Aliannejadi, Evangelos Kanoulas
We consider the cross-market recommendation (CMR) task, which involves recommendation in a low-resource target market using data from a richer, auxiliary source market. Prior work…
Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence
Ana Lucic, Maurits Bleeker, Sami Jullien +2
In this work, we explain the setup for a technical, graduate-level course on Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence (FACT-AI) at the…
Controllable Recommenders using Deep Generative Models and Disentanglement
Samarth Bhargav, Evangelos Kanoulas
In this paper, we consider controllability as a means to satisfy dynamic preferences of users, enabling them to control recommendations such that their current preference is met. W…
Robustness Evaluation of Entity Disambiguation Using Prior Probes:the Case of Entity Overshadowing
Vera Provatorova, Svitlana Vakulenko, Samarth Bhargav +1
Entity disambiguation (ED) is the last step of entity linking (EL), when candidate entities are reranked according to the context they appear in. All datasets for training and eval…
Sinkhorn AutoEncoders
Giorgio Patrini, Rianne van den Berg, Patrick Forré +5
Optimal transport offers an alternative to maximum likelihood for learning generative autoencoding models. We show that minimizing the p-Wasserstein distance between the generator…