5 papers
Discovering High-Quality Chess Puzzles with Offline Reinforcement Learning
Allen Nie, Anirudhan Badrinath, Nicholas Tomlin +5
Learning and skill mastery require extensive and deliberate practice. In many learning settings, producing high-quality pedagogical materials can require a high level of domain exp…
PinRec: Unified Generative Retrieval for Pinterest Recommender Systems
Edoardo Botta, Jaewon Yang, Yi-Ping Hsu +6
Generative retrieval methods employ sequential modeling techniques, like transformers, to generate candidate items for recommender systems. These methods have demonstrated promisin…
OmniSage: Large Scale, Multi-Entity Heterogeneous Graph Representation Learning
Anirudhan Badrinath, Alex Yang, Kousik Rajesh +5
Representation learning, a task of learning latent vectors to represent entities, is a key task in improving search and recommender systems in web applications. Various representat…
Unified Preference Optimization: Language Model Alignment Beyond the Preference Frontier
Anirudhan Badrinath, Prabhat Agarwal, Jiajing Xu
For aligning large language models (LLMs), prior work has leveraged reinforcement learning via human feedback (RLHF) or variations of direct preference optimization (DPO). While DP…
OPERA: Automatic Offline Policy Evaluation with Re-weighted Aggregates of Multiple Estimators
Allen Nie, Yash Chandak, Christina J. Yuan +3
Offline policy evaluation (OPE) allows us to evaluate and estimate a new sequential decision-making policy's performance by leveraging historical interaction data collected from ot…