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Found 7 papers, 7 papers with code
Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data
Our main finding is that, in general, approaches that use on-policy sampling or attempt to push down the likelihood on certain responses (i. e., employ a "negative gradient") outperform offline and maximum likelihood objectives.
Offline Retraining for Online RL: Decoupled Policy Learning to Mitigate Exploration Bias
Can we leverage offline RL to recover better policies from online interaction?
Conservative Prediction via Data-Driven Confidence Minimization
Taking inspiration from this result, we present data-driven confidence minimization (DCM), which minimizes confidence on an uncertainty dataset containing examples that the model is likely to misclassify at test time.
Surgical Fine-Tuning Improves Adaptation to Distribution Shifts
A common approach to transfer learning under distribution shift is to fine-tune the last few layers of a pre-trained model, preserving learned features while also adapting to the new task.
When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning
A long-term goal of reinforcement learning is to design agents that can autonomously interact and learn in the world.
Do Deep Networks Transfer Invariances Across Classes?
Based on this analysis, we show how a generative approach for learning the nuisance transformations can help transfer invariances across classes and improve performance on a set of imbalanced image classification benchmarks.
Ranked #21 on
Long-tail Learning
on CIFAR-10-LT (ρ=100)
No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasets
Out-of-distribution detection is an important component of reliable ML systems.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
