8 citations · 8 across the 5 of their papers we have counts for
5 papers
Stable GFlowNets with TV Monitoring and Probabilistic Guarantees
Zengxiang Lei, Ananth Shreekumar, Jonathan Rosenthal +6
Generative Flow Networks (GFlowNets) sample diverse structured objects in proportion to reward and have been applied to molecular discovery and biological-sequence design, where fi…
Adaptive Blockwise Search: Inference-Time Alignment for Large Language Models
Mohammad Atif Quamar, Mohammad Areeb, Nishant Sharma +5
LLM alignment remains a critical challenge. Inference-time methods provide a flexible alternative to fine-tuning, but their uniform computational effort often yields suboptimal ali…
CaBaGe: Data-Free Model Extraction using ClAss BAlanced Generator Ensemble
Jonathan Rosenthal, Shanchao Liang, Kevin Zhang +1
Machine Learning as a Service (MLaaS) is often provided as a pay-per-query, black-box system to clients. Such a black-box approach not only hinders open replication, validation, an…
Self-Play Learning Without a Reward Metric
Dan Schmidt, Nick Moran, Jonathan S. Rosenfeld +2
The AlphaZero algorithm for the learning of strategy games via self-play, which has produced superhuman ability in the games of Go, chess, and shogi, uses a quantitative reward fun…
The Boundary Forest Algorithm for Online Supervised and Unsupervised Learning
Charles Mathy, Nate Derbinsky, José Bento +2
We describe a new instance-based learning algorithm called the Boundary Forest (BF) algorithm, that can be used for supervised and unsupervised learning. The algorithm builds a for…