Oceania
Equivariant Neural Operator Learning with Graphon Convolution
It is common that these data themselves have inherently complicated 3D geometric structures.
Equivariant Neural Operator Learning with Graphon Convolution
It is common that these data themselves have inherently complicated 3D geometric structures.
Approximation-Generalization Trade-offs under (Approximate) Group Equivariance
A prevalent intuition about such models is that the integration of relevant symmetry results in enhanced generalization.
Pairwise GUI Dataset Construction Between Android Phones and Tablets
GUIs development to enhance developers' productivity.
'I'm a composer. Am I staring extinction in the face?': classical music and AI
Riding a wave means surrendering to the pull. Riding a wave means surrendering to the pull. Technology is radically reshaping how we make music. As I dug deeper into this for a radio 3 documentary I began to wonder if creative organisations are right to be so upbeat about AI. Are we riding the wave or will the wave destroy us?
Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback
In this work, we study the low-rank MDPs with adversarially changed losses in the full-information feedback setting.
Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback
In this work, we study the low-rank MDPs with adversarially changed losses in the full-information feedback setting.
Fine-Grained Human Feedback Gives Better Rewards for Language Model Training
Additionally, we show that LM behaviors can be customized using different combinations of fine-grained reward models.
Supplement to " Maximum Average Randomly Sampled: A Scale Free and Non-parametric Algorithm for Stochastic Bandits "
The following lemma given in [2] is useful for the proof of Theorem 1. Lemma 1. [2] Given a stochastic matrix H = 0 0 0 h The following propositions are used to prove this theorem. In this case, there is not enough observations to achieve an upper confidence bound using Proposition 2. The randomized UCB for this case has also an exact confidence as illustrated below: Pr{UCB In the second equality, the boundedness of the means of the arms and Proposition 1 were utilized. The steps in this proof closely follows the proof of Theorem 7.1 in [3]. Let us define a'good' event as G We are going to show 1. The next step is to bound the probability of the second set in (3).