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Found 8 papers, 3 papers with code
Avoiding $\mathbf{exp(R_{max})}$ scaling in RLHF through Preference-based Exploration
Reinforcement Learning from Human Feedback (RLHF) has emerged as a pivotal technique for large language model (LLM) alignment.
State-free Reinforcement Learning
In this work, we study the \textit{state-free RL} problem, where the algorithm does not have the states information before interacting with the environment.
Koopman-based Deep Learning for Nonlinear System Estimation
Nonlinear differential equations are encountered as models of fluid flow, spiking neurons, and many other systems of interest in the real world.
Scale-free Adversarial Reinforcement Learning
This paper initiates the study of scale-free learning in Markov Decision Processes (MDPs), where the scale of rewards/losses is unknown to the learner.
Improved Algorithms for Adversarial Bandits with Unbounded Losses
We consider the Adversarial Multi-Armed Bandits (MAB) problem with unbounded losses, where the algorithms have no prior knowledge on the sizes of the losses.
Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance
As large language models (LLMs) are continuously being developed, their evaluation becomes increasingly important yet challenging.
High $J_{\rm c}$ and low anisotropy of hydrogen doped NdFeAsO superconducting thin film
The anisotropic Ginzburg-Landau scaling for the angle dependence of $J_{\rm c}$ yielded temperature-dependent scaling parameters $\gamma_{\rm J}$ that decreased from 1. 6 at 30 K to 1. 3 at 5 K. This is opposite to the behaviour of NdFeAs(O, F).
Superconductivity
ICE-BA: Incremental, Consistent and Efficient Bundle Adjustment for Visual-Inertial SLAM
However, jointly using visual and inertial measurements to optimize SLAM objective functions is a problem of high computational complexity.
