8 citations · 8 across the 11 of their papers we have counts for
8 papers · 1 filter
Distortion of AI Alignment Revisited: RLHF is a Decent Utilitarian Aligner
Kazusato Oko, Annie Ulichney, Nika Haghtalab +1
While Reinforcement Learning from Human Feedback (RLHF) is the standard paradigm for aligning large language models with human preferences, its effectiveness in pluralistic setting…
Direct Distributional Optimization for Provable Alignment of Diffusion Models
Ryotaro Kawata, Kazusato Oko, Atsushi Nitanda +1
We introduce a novel alignment method for diffusion models from distribution optimization perspectives while providing rigorous convergence guarantees. We first formulate the probl…
A Statistical Theory of Contrastive Pre-training and Multimodal Generative AI
Kazusato Oko, Licong Lin, Yuhang Cai +1
Multi-modal generative AI systems, such as those combining vision and language, rely on contrastive pre-training to learn representations across different modalities. While their p…
Pretrained transformer efficiently learns low-dimensional target functions in-context
Kazusato Oko, Yujin Song, Taiji Suzuki +1
Transformers can efficiently learn in-context from example demonstrations. Most existing theoretical analyses studied the in-context learning (ICL) ability of transformers for line…
Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinations
Kazusato Oko, Yujin Song, Taiji Suzuki +1
We study the computational and sample complexity of learning a target function with additive structure, that is, $f_*(x) = \frac{1}{\sqrt{M}}\sum_{m…
Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit
Jason D. Lee, Kazusato Oko, Taiji Suzuki +1
We study the problem of gradient descent learning of a single-index target function unde…
Flow matching achieves almost minimax optimal convergence
Kenji Fukumizu, Taiji Suzuki, Noboru Isobe +2
Flow matching (FM) has gained significant attention as a simulation-free generative model. Unlike diffusion models, which are based on stochastic differential equations, FM employs…
Versatile Single-Loop Method for Gradient Estimator: First and Second Order Optimality, and its Application to Federated Learning
Kazusato Oko, Shunta Akiyama, Tomoya Murata +1
While variance reduction methods have shown great success in solving large scale optimization problems, many of them suffer from accumulated errors and, therefore, should periodica…