601 citations · 1k across the 9 of their papers we have counts for
9 papers
VisTIRA: Closing the Image-Text Modality Gap in Visual Math Reasoning via Structured Tool Integration
Saeed Khaki, Ashudeep Singh, Nima Safaei +1
Vision-language models (VLMs) lag behind text-only language models on mathematical reasoning when the same problems are presented as images rather than text. We empirically charact…
RecRec: Algorithmic Recourse for Recommender Systems
Sahil Verma, Ashudeep Singh, Varich Boonsanong +2
Recommender systems play an essential role in the choices people make in domains such as entertainment, shopping, food, news, employment, and education. The machine learning models…
Representation Online Matters: Practical End-to-End Diversification in Search and Recommender Systems
Pedro Silva, Bhawna Juneja, Shloka Desai +2
As the use of online platforms continues to grow across all demographics, users often express a desire to feel represented in the content. To improve representation in search resul…
Fairness in Ranking under Uncertainty
Ashudeep Singh, David Kempe, Thorsten Joachims
Fairness has emerged as an important consideration in algorithmic decision-making. Unfairness occurs when an agent with higher merit obtains a worse outcome than an agent with lowe…
Controlling Fairness and Bias in Dynamic Learning-to-Rank
Marco Morik, Ashudeep Singh, Jessica Hong +1
Rankings are the primary interface through which many online platforms match users to items (e.g. news, products, music, video). In these two-sided markets, not only the users draw…
Policy Learning for Fairness in Ranking
Ashudeep Singh, Thorsten Joachims
Conventional Learning-to-Rank (LTR) methods optimize the utility of the rankings to the users, but they are oblivious to their impact on the ranked items. However, there has been a…
Fairness of Exposure in Rankings
Ashudeep Singh, Thorsten Joachims
Rankings are ubiquitous in the online world today. As we have transitioned from finding books in libraries to ranking products, jobs, job applicants, opinions and potential romanti…
Recommendations as Treatments: Debiasing Learning and Evaluation
Tobias Schnabel, Adith Swaminathan, Ashudeep Singh +2
Most data for evaluating and training recommender systems is subject to selection biases, either through self-selection by the users or through the actions of the recommendation sy…
A Semantic Approach to Summarization
Divyanshu Bhartiya, Ashudeep Singh
Sentence extraction based summarization methods has some limitations as it doesn't go into the semantics of the document. Also, it lacks the capability of sentence generation which…