8 papers
Intrinsic Wasserstein Rates for Score-Based Generative Models on Smooth Manifolds
Guoji Fu, Taiji Suzuki, Wee Sun Lee +1
Score-based generative models are trained in high-dimensional ambient spaces, yet many data distributions are supported on low-dimensional nonlinear structures. We prove that, for…
Transformers as Measure-Theoretic Associative Memory: A Statistical Perspective and Minimax Optimality
Ryotaro Kawata, Taiji Suzuki
Transformers excel through content-addressable retrieval and the ability to exploit contexts of, in principle, unbounded length. We recast associative memory at the level of probab…
From Shortcut to Induction Head: How Data Diversity Shapes Algorithm Selection in Transformers
Ryotaro Kawata, Yujin Song, Alberto Bietti +4
Transformers can implement both generalizable algorithms (e.g., induction heads) and simple positional shortcuts (e.g., memorizing fixed output positions). In this work, we study h…
Mixture of Experts Provably Detect and Learn the Latent Cluster Structure in Gradient-Based Learning
Ryotaro Kawata, Kohsei Matsutani, Yuri Kinoshita +2
Mixture of Experts (MoE), an ensemble of specialized models equipped with a router that dynamically distributes each input to appropriate experts, has achieved successful results i…
Degrees of Freedom for Linear Attention: Distilling Softmax Attention with Optimal Feature Efficiency
Naoki Nishikawa, Rei Higuchi, Taiji Suzuki
Linear attention has attracted interest as a computationally efficient approximation to softmax attention, especially for long sequences. Recent studies have explored distilling so…
Direct Density Ratio Optimization: A Statistically Consistent Approach to Aligning Large Language Models
Rei Higuchi, Taiji Suzuki
Aligning large language models (LLMs) with human preferences is crucial for safe deployment, yet existing methods assume specific preference models like Bradley-Terry model. This a…