2 citations · 2 across the 7 of their papers we have counts for
10 papers
Testing Replica Symmetry Breaking in the three-dimensional Edwards-Anderson model by using vanishing fields
Miguel Aguilar-Janita, Silvio Franz, Victor Martin-Mayor +4
Replica Symmetry Breaking (RSB) theory for spin glasses, which is exact in infinite dimensions, provides detailed predictions for the behavior of these systems. One of these predic…
On the Complexity of Finding Stationary Points of p-th Order Hölder Smooth Functions in One Dimension
Huanjian Zhou, Taiji Suzuki
We study the query complexity of finding an an -stationary points of a one-dimensional non-convex function whose -th derivative is -Hölder continuous. We deter…
Finding Stationary Points under Higher-Order Smoothness: Trapping Algorithms and First-Order Lower Bounds
Huanjian Zhou, Masashi Sugiyama, Taiji Suzuki
We study the query complexity of finding approximate stationary points under higher-order smoothness using only function values or only gradients. Our results cover different dimen…
Accelerating Discrete Diffusion Models with Parallel-In-Time Sampling
Yu Yao, Huanjian Zhou, Andi Han +2
Discrete diffusion models are widely used for learning and generating discrete distributions. As the generation process is inherently sequential, the acceleration of sampling is of…
Provably Learning Diffusion Models under the Manifold Hypothesis: Collapse and Refine
Wei Huang, Andi Han, Mingyuan Bai +4
Diffusion models generate high-dimensional data with remarkable quality, yet how their training efficiently learns the score function, bypassing the curse of dimensionality when da…
The Adaptive Complexity of Finding a Stationary Point
Huanjian Zhou, Andi Han, Akiko Takeda +1
In large-scale applications, such as machine learning, it is desirable to design non-convex optimization algorithms with a high degree of parallelization. In this work, we study th…
Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models
Huanjian Zhou, Masashi Sugiyama
Sampling from high-dimensional probability distributions is fundamental in machine learning and statistics. As datasets grow larger, computational efficiency becomes increasingly i…
The adaptive complexity of parallelized log-concave sampling
Huanjian Zhou, Baoxiang Wang, Masashi Sugiyama
In large-data applications, such as the inference process of diffusion models, it is desirable to design sampling algorithms with a high degree of parallelization. In this work, we…
Efficient Submodular Optimization under Noise: Local Search is Robust
Lingxiao Huang, Yuyi Wang, Chunxue Yang +1
The problem of monotone submodular maximization has been studied extensively due to its wide range of applications. However, there are cases where one can only access the objective…
On Optimal Approximations for -Submodular Maximization via Multilinear Extension
Lingxiao Huang, Baoxiang Wang, Huanjian Zhou
We investigate a more generalized form of submodular maximization, referred to as -submodular maximization, with applications across social networks and machine learning domains…