Active learning strategies for preference-based MPC objective learning achieve better closed-loop alignment with human preferences using fewer queries than random sampling in numerical tests.
ACM computing surveys (CSUR) , volume=
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
A dataset-agnostic framework converts text tool-calling benchmarks to paired audio evaluations via TTS, speaker variation and noise, then evaluates seven omni-modal models showing model- and task-dependent performance with small text-to-voice gaps.
Active inference framework for U-statistics using augmented IPW to optimize label queries and minimize variance under budget constraints.
RADS applies reinforcement learning to pick informative samples for transfer learning, improving performance over uncertainty and diversity sampling in low-resource imbalanced clinical settings.
citing papers explorer
-
Active Learning MPC Objective Functions from Preferences
Active learning strategies for preference-based MPC objective learning achieve better closed-loop alignment with human preferences using fewer queries than random sampling in numerical tests.
-
From Text to Voice: A Reproducible and Verifiable Framework for Evaluating Tool Calling LLM Agents
A dataset-agnostic framework converts text tool-calling benchmarks to paired audio evaluations via TTS, speaker variation and noise, then evaluates seven omni-modal models showing model- and task-dependent performance with small text-to-voice gaps.
-
Learning U-Statistics with Active Inference
Active inference framework for U-statistics using augmented IPW to optimize label queries and minimize variance under budget constraints.
-
RADS: Reinforcement Learning-Based Sample Selection Improves Transfer Learning in Low-resource and Imbalanced Clinical Settings
RADS applies reinforcement learning to pick informative samples for transfer learning, improving performance over uncertainty and diversity sampling in low-resource imbalanced clinical settings.