The Convex Landscape of Neural Networks: Characterizing Global Optima and Stationary Points via Lasso Models

The Convex Landscape of Neural Networks: Characterizing Global Optima and Stationary Points via Lasso Models

The paper "The Convex Landscape of Neural Networks: Characterizing Global Optima and Stationary Points via Lasso Models" by Tolga Ergen and Mert Pilanci addresses a prominent challenge in machine learning: the non-convex nature of training deep neural networks (DNNs). Traditional methods for training DNNs rely on non-convex optimization heuristics, which often require empirical methods and lack a solid theoretical foundation. The authors present a novel approach utilizing convex optimization theory and sparse recovery models to refine neural network training, particularly focusing on two-layer neural networks with piecewise linear activations.

Major Contributions

1. Convex Formulations for Neural Networks: The paper introduces a convex analytical framework that transforms the training of two-layer neural networks with piecewise linear activations such as ReLU, leaky ReLU, and absolute value activation into finite-dimensional convex programs. These convex programs incorporate a regularization term promoting sparsity through a variant of group Lasso. The approach leverages semi-infinite programming to establish strong duality for finite-width neural networks, equating them to high-dimensional convex sparse recovery models.

2. Computational Complexity: Notably, the worst-case complexity of solving this convex program is polynomial in the number of samples and neurons, given that the data matrix rank is bounded. For datasets with arbitrary ranks, the authors propose a polynomial-time approximation scheme based on zonotope subsampling, ensuring an approximation ratio. This scheme allows the characterization of all stationary points of the non-convex objective as the global optimum of a subsampled convex program.

3. Convex Models and Hyperparameters: The convex models train using standard convex solvers without requiring non-convex heuristics or extensive hyperparameter tuning. This robustness results from the convex nature of the problem, making the solutions insensitive to hyperparameters such as initialization, batch sizes, and step size schedules.

4. Empirical Validation: Through extensive numerical experiments, the authors demonstrate that convex models can outperform traditional non-convex methods, showing superior performance while being less sensitive to hyperparameter choices.

Theoretical Insights and Extensions

Extension to CNNs:

The study also extends the proposed convex approach to convolutional neural networks (CNNs). For CNNs with global average pooling, the authors show that the training problem can be formulated as a standard fully connected network training problem. They also derive a semi-definite program (SDP) formulation for linear CNNs, optimizing them via nuclear norm regularization.

Handling Bias Terms and Different Regularizations:

The theoretical framework is extended to accommodate bias terms in neurons and diverse regularization mechanisms, including $\ell_p$-norm regularization. This adaptability illustrates the framework's broad applicability across various neural network architectures.

Practical Implications and Future Directions

The ability to train neural networks using convex optimization opens new avenues for ensuring global optimality and robustness in model training. The paper proposes that convex models inherently possess a bias towards simpler solutions, promoting both sparsity and interpretability through the equivalent Lasso formulations. Future research might explore deeper neural networks, recurrent architectures, and transformer models, leveraging the strong duality and convex representations highlighted by this study.

Conclusion

The insights from "The Convex Landscape of Neural Networks" challenge prevailing methods for neural network training. By framing the problem within a convex optimization context, the paper not only guarantees global optimality, provided that the network adheres to the derived constraints, but it also provides valuable interpretative mechanisms. The implications for both theoretical advancements and practical applications in AI signal a critical step forward in making deep learning frameworks more robust, efficient, and explainable.