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Found 7 papers, 0 papers with code
Variance-reduced Zeroth-Order Methods for Fine-Tuning Language Models
Evaluated across a range of both masked and autoregressive LMs on benchmark GLUE tasks, MeZO-SVRG outperforms MeZO with up to 20% increase in test accuracies in both full- and partial-parameter fine-tuning settings.
Soft Convex Quantization: Revisiting Vector Quantization with Convex Optimization
SCQ works like a differentiable convex optimization (DCO) layer: in the forward pass, we solve for the optimal convex combination of codebook vectors that quantize the inputs.
Meta-Learning Parameterized First-Order Optimizers using Differentiable Convex Optimization
Conventional optimization methods in machine learning and controls rely heavily on first-order update rules.
An Overview and Prospective Outlook on Robust Training and Certification of Machine Learning Models
In this discussion paper, we survey recent research surrounding robustness of machine learning models.
Efficient Global Optimization of Two-layer ReLU Networks: Quadratic-time Algorithms and Adversarial Training
We apply the robust convex optimization theory to convex training and develop convex formulations that train ANNs robust to adversarial inputs.
Safe Reinforcement Learning with Chance-constrained Model Predictive Control
Real-world reinforcement learning (RL) problems often demand that agents behave safely by obeying a set of designed constraints.
Practical Convex Formulation of Robust One-hidden-layer Neural Network Training
Recent work has shown that the training of a one-hidden-layer, scalar-output fully-connected ReLU neural network can be reformulated as a finite-dimensional convex program.
