458. Optimization Techniques for Learning Multi-Index Models
Invited abstract in session MC-3: First-order methods in modern optimization (Part II), stream Large scale optimization: methods and algorithms.
Monday, 14:00-16:00Room: B100/4011
Authors (first author is the speaker)
| 1. | Hippolyte Labarrière
|
| Università di Genova | |
| 2. | Shuo Huang
|
| DIBRIS - Università di Genova | |
| 3. | Ernesto De Vito
|
| DIMA - Università di Genova | |
| 4. | Lorenzo Rosasco
|
| MaLGa, DIBRIS, Università di Genova | |
| 5. | Tomaso Poggio
|
| McDermott Professor in Brain Sciences, MIT |
Abstract
Neural networks naturally identify low-dimensional structures that improve generalization, a property often lacking in classical kernel methods. In this talk, I will present how we can bridge this gap by integrating the multi-index model (MIM) into kernel regression. By introducing a hyper-kernel that incorporates a learned projection matrix, we enhance the adaptability of kernel methods. I will discuss the theoretical guarantees of this approach and introduce an optimization framework based on alternating minimization techniques and Nyström approximations
Keywords
- First-order optimization
- Optimization for learning and data analysis
Status: accepted
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