9 citations · 16 across the 7 of their papers we have counts for
7 papers
Towards Two-Stage Counterfactual Learning to Rank
Shashank Gupta, Yiming Liao, Maarten de Rijke
Counterfactual learning to rank (CLTR) aims to learn a ranking policy from user interactions while correcting for the inherent biases in interaction data, such as position bias. Ex…
A Simple and Effective Reinforcement Learning Method for Text-to-Image Diffusion Fine-tuning
Shashank Gupta, Chaitanya Ahuja, Tsung-Yu Lin +4
Reinforcement learning (RL)-based fine-tuning has emerged as a powerful approach for aligning diffusion models with black-box objectives. Proximal policy optimization (PPO) is a po…
A Simpler Alternative to Variational Regularized Counterfactual Risk Minimization
Hua Chang Bakker, Shashank Gupta, Harrie Oosterhuis
Variance regularized counterfactual risk minimization (VRCRM) has been proposed as an alternative off-policy learning (OPL) method. VRCRM method uses a lower-bound on the -diver…
Proximal Ranking Policy Optimization for Practical Safety in Counterfactual Learning to Rank
Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke
Counterfactual learning to rank (CLTR) can be risky and, in various circumstances, can produce sub-optimal models that hurt performance when deployed. Safe CLTR was introduced to m…
Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank
Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke
Counterfactual learning to rank (CLTR) can be risky and, in various circumstances, can produce sub-optimal models that hurt performance when deployed. Safe CLTR was introduced to m…
Optimal Baseline Corrections for Off-Policy Contextual Bandits
Shashank Gupta, Olivier Jeunen, Harrie Oosterhuis +1
The off-policy learning paradigm allows for recommender systems and general ranking applications to be framed as decision-making problems, where we aim to learn decision policies t…
A First Look at Selection Bias in Preference Elicitation for Recommendation
Shashank Gupta, Harrie Oosterhuis, Maarten de Rijke
Preference elicitation explicitly asks users what kind of recommendations they would like to receive. It is a popular technique for conversational recommender systems to deal with…