55 citations · 84 across the 14 of their papers we have counts for
8 papers · 1 filter
Modified Gauss-Newton Algorithms under Noise
Krishna Pillutla, Vincent Roulet, Sham Kakade +1
Gauss-Newton methods and their stochastic version have been widely used in machine learning and signal processing. Their nonsmooth counterparts, modified Gauss-Newton or prox-linea…
Superquantiles at Work: Machine Learning Applications and Efficient Subgradient Computation
Yassine Laguel, Krishna Pillutla, Jérôme Malick +1
R. Tyrell Rockafellar and collaborators introduced, in a series of works, new regression modeling methods based on the notion of superquantile (or conditional value-at-risk). These…
Superquantile-based learning: a direct approach using gradient-based optimization
Yassine Laguel, Jérôme Malick, Zaid Harchaoui
We consider a formulation of supervised learning that endows models with robustness to distributional shifts from training to testing. The formulation hinges upon the superquantile…
First-order Optimization for Superquantile-based Supervised Learning
Yassine Laguel, Jérôme Malick, Zaid Harchaoui
Classical supervised learning via empirical risk (or negative log-likelihood) minimization hinges upon the assumption that the testing distribution coincides with the training dist…
On the Convergence of the Iterative Linear Exponential Quadratic Gaussian Algorithm to Stationary Points
Vincent Roulet, Maryam Fazel, Siddhartha Srinivasa +1
A classical method for risk-sensitive nonlinear control is the iterative linear exponential quadratic Gaussian algorithm. We present its convergence analysis from a first-order opt…
Iterative Linearized Control: Stable Algorithms and Complexity Guarantees
Vincent Roulet, Siddhartha Srinivasa, Dmitriy Drusvyatskiy +1
We examine popular gradient-based algorithms for nonlinear control in the light of the modern complexity analysis of first-order optimization algorithms. The examination reveals th…