40 citations · 124 across the 62 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classification
Gabriel Bénédict, Vincent Koops, Daan Odijk +1
Multiclass multilabel classification is the task of attributing multiple labels to examples via predictions. Current models formulate a reduction of the multilabel setting into eit…
How to Not Measure Disentanglement
Anna Sepliarskaia, Julia Kiseleva, Maarten de Rijke
To evaluate disentangled representations several metrics have been proposed. However, theoretical guarantees for conventional metrics of disentanglement are missing. Moreover, conv…