16 citations · 69 across the 33 of their papers we have counts for
12 papers · 1 filter
The Implicit Bias of Hyperbolic Representation Learning for Multiclass Data: A Busemann Risk Perspective
Xingrun Li, Sho Kuno, Yusuke Mukuta +2
We study the implicit bias of Riemannian gradient flow for hyperbolic multiclass classification with fixed class prototypes in hyperbolic space . Our framework accomm…
Representation Learning: An Intrinsic Mixed-Curvature Space with Higher Curvature Capacities and Deeper Order-Aware Composition
Xingrun Li, Yusuke Mukuta, Xin Yang +2
Mixed-curvature representation learning seeks to capture rich geometric structures that cannot be adequately modeled by a single curvature regime. Existing approaches largely rely…
Offline Reinforcement Learning with Wasserstein Regularization via Optimal Transport Maps
Motoki Omura, Yusuke Mukuta, Kazuki Ota +2
Offline reinforcement learning (RL) aims to learn an optimal policy from a static dataset, making it particularly valuable in scenarios where data collection is costly, such as rob…
Gradual Transition from Bellman Optimality Operator to Bellman Operator in Online Reinforcement Learning
Motoki Omura, Kazuki Ota, Takayuki Osa +2
For continuous action spaces, actor-critic methods are widely used in online reinforcement learning (RL). However, unlike RL algorithms for discrete actions, which generally model…
Symmetric Q-learning: Reducing Skewness of Bellman Error in Online Reinforcement Learning
Motoki Omura, Takayuki Osa, Yusuke Mukuta +1
In deep reinforcement learning, estimating the value function to evaluate the quality of states and actions is essential. The value function is often trained using the least square…
HiPerformer: Hierarchically Permutation-Equivariant Transformer for Time Series Forecasting
Ryo Umagami, Yu Ono, Yusuke Mukuta +1
It is imperative to discern the relationships between multiple time series for accurate forecasting. In particular, for stock prices, components are often divided into groups with…