Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision.
|
ICML |
2024 |
0 |
Let's Verify Step by Step.
|
ICLR |
2024 |
0 |
Training language models to follow instructions with human feedback.
|
NIPS/NeurIPS |
2022 |
0 |
Quantifying Differences in Reward Functions.
|
ICLR |
2021 |
0 |
Pitfalls of Learning a Reward Function Online.
|
IJCAI |
2020 |
14 |
Learning Human Objectives by Evaluating Hypothetical Behavior.
|
ICML |
2020 |
0 |
Reward learning from human preferences and demonstrations in Atari.
|
NIPS/NeurIPS |
2018 |
2 |
On Thompson Sampling and Asymptotic Optimality.
|
IJCAI |
2017 |
50 |
Universal Reinforcement Learning Algorithms: Survey and Experiments.
|
IJCAI |
2017 |
17 |
Generalised Discount Functions applied to a Monte-Carlo AI u Implementation.
|
AAMAS |
2017 |
4 |
Deep Reinforcement Learning from Human Preferences.
|
NIPS/NeurIPS |
2017 |
676 |
A Formal Solution to the Grain of Truth Problem.
|
UAI |
2016 |
14 |
Thompson Sampling is Asymptotically Optimal in General Environments.
|
UAI |
2016 |
37 |
Loss Bounds and Time Complexity for Speed Priors.
|
AISTATS |
2016 |
7 |
On the Computability of AIXI.
|
UAI |
2015 |
10 |
Sequential Extensions of Causal and Evidential Decision Theory.
|
ADT |
2015 |
14 |
Bad Universal Priors and Notions of Optimality.
|
COLT |
2015 |
36 |