7 citations · 32 across the 26 of their papers we have counts for
6 papers · 1 filter
Preference Adaptive and Sequential Text-to-Image Generation
Ofir Nabati, Guy Tennenholtz, ChihWei Hsu +5
We address the problem of interactive text-to-image (T2I) generation, designing a reinforcement learning (RL) agent which iteratively improves a set of generated images for a user…
Beyond Thumbs Up/Down: Untangling Challenges of Fine-Grained Feedback for Text-to-Image Generation
Katherine M. Collins, Najoung Kim, Yonatan Bitton +15
Human feedback plays a critical role in learning and refining reward models for text-to-image generation, but the optimal form the feedback should take for learning an accurate rew…
Online Bandit Learning with Offline Preference Data for Improved RLHF
Akhil Agnihotri, Rahul Jain, Deepak Ramachandran +1
Reinforcement Learning with Human Feedback (RLHF) is at the core of fine-tuning methods for generative AI models for language and images. Such feedback is often sought as rank or p…
e-COP : Episodic Constrained Optimization of Policies
Akhil Agnihotri, Rahul Jain, Deepak Ramachandran +1
In this paper, we present the algorithm, the first policy optimization algorithm for constrained Reinforcement Learning (RL) in episodic (finite horizon) settings.…
Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic
Connor Pryor, Quan Yuan, Jeremiah Liu +4
Dialog Structure Induction (DSI) is the task of inferring the latent dialog structure (i.e., a set of dialog states and their temporal transitions) of a given goal-oriented dialog.…
Understanding Subjectivity through the Lens of Motivational Context in Model-Generated Image Satisfaction
Senjuti Dutta, Sherol Chen, Sunny Mak +7
Image generation models are poised to become ubiquitous in a range of applications. These models are often fine-tuned and evaluated using human quality judgments that assume a univ…