1 citations · 3 across the 14 of their papers we have counts for
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Flow Density Control: Generative Optimization Beyond Entropy-Regularized Fine-Tuning
Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh +3
Adapting large-scale foundation flow and diffusion generative models to optimize task-specific objectives while preserving prior information is crucial for real-world applications…
Scalable Neural Incentive Design with Parameterized Mean-Field Approximation
Nathan Corecco, Batuhan Yardim, Vinzenz Thoma +2
Designing incentives for a multi-agent system to induce a desirable Nash equilibrium is both a crucial and challenging problem appearing in many decision-making domains, especially…
When Scores Learn Geometry: Rate Separations under the Manifold Hypothesis
Xiang Li, Zebang Shen, Ya-Ping Hsieh +1
Score-based methods, such as diffusion models and Bayesian inverse problems, are often interpreted as learning the data distribution in the low-noise limit (). In this work…
Landing with the Score: Riemannian Optimization through Denoising
Andrey Kharitenko, Zebang Shen, Riccardo de Santi +2
Under the data manifold hypothesis, high-dimensional data are concentrated near a low-dimensional manifold. We study the problem of Riemannian optimization over such manifolds when…
GS-Occ3D: Scaling Vision-only Occupancy Reconstruction with Gaussian Splatting
Baijun Ye, Minghui Qin, Saining Zhang +7
Occupancy is crucial for autonomous driving, providing essential geometric priors for perception and planning. However, existing methods predominantly rely on LiDAR-based occupancy…
Adjoint-Based Aerodynamic Shape Optimization with a Manifold Constraint Learned by Diffusion Models
Long Chen, Emre Oezkaya, Jan Rottmayer +3
We introduce an adjoint-based aerodynamic shape optimization framework that integrates a diffusion model trained on existing designs to learn a smooth manifold of aerodynamically v…
Provable Maximum Entropy Manifold Exploration via Diffusion Models
Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh +3
Exploration is critical for solving real-world decision-making problems such as scientific discovery, where the objective is to generate truly novel designs rather than mimic exist…
Superquantile-Gibbs Relaxation for Minima-selection in Bilevel Optimization
Saeed Masiha, Zebang Shen, Negar Kiyavash +1
Bilevel optimization (BLO) becomes more challenging when the lower-level objective admits multiple minimizers. Compared with the commonly studied unique-minimizer setting, this int…
Poincaré Inequality for Local Log-Polyak-Lojasiewicz Measures : Non-asymptotic Analysis in Low-temperature Regime
Yun Gong, Zebang Shen, Niao He
Potential functions in highly pertinent applications, such as deep learning in over-parameterized regime, are empirically observed to admit non-isolated minima. To understand the c…
Poincare Inequality for Local Log-Polyak-Łojasiewicz Measures: Non-asymptotic Analysis in Low-temperature Regime
Yun Gong, Niao He, Zebang Shen
Potential functions in highly pertinent applications, such as deep learning in over-parameterized regime, are empirically observed to admit non-isolated minima. To understand the c…