Pillaud-Vivien, Loucas

1. Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity
   2021/06/17 by Scott Pesme, Loucas Pillaud‐Vivien, Pesme, Scott +3 · 6 citations
   Computer Science · Mathematics · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and ELM #Markov Chains and Monte Carlo Methods #Neural Networks and Applications #Privacy-Preserving Technologies in Data #Stochastic Gradient Optimization Techniques
2. Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs
   2022/06/02 by Étienne Boursier, Loucas Pillaud‐Vivien, Boursier, Etienne +3 · 7 citations
   Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and ELM #Model Reduction and Neural Networks #Stochastic Gradient Optimization Techniques
3. Computational-Statistical Gaps in Gaussian Single-Index Models
   2024/03/08 by Damian, Alex, Pillaud-Vivien, Loucas, Lee, Jason D. +1 · 9 citations
   #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
4. SGD with Large Step Sizes Learns Sparse Features
   2022/10/11 by Andriushchenko, Maksym, Varre, Aditya, Pillaud-Vivien, Loucas +1 · 6 citations
   #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
5. On Learning Gaussian Multi-index Models with Gradient Flow
   2023/10/30 by Alberto Bietti, Joan Bruna, Bietti, Alberto +3 · 5 citations
   Mathematics · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Optimization and Control (math.OC) #Statistical Mechanics and Entropy #Statistical Methods and Inference
6. Last iterate convergence of SGD for Least-Squares in the Interpolation regime
   2021/02/05 by Aditya Varre, Varre, Aditya, Loucas Pillaud‐Vivien +3 · 3 citations
   Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Markov Chains and Monte Carlo Methods #Optimization and Control (math.OC) #Sparse and Compressive Sensing Techniques #Stochastic Gradient Optimization Techniques
7. Label noise (stochastic) gradient descent implicitly solves the Lasso for quadratic parametrisation
   2022/06/20 by Pillaud-Vivien, Loucas, Reygner, Julien, Flammarion, Nicolas · 2 citations
   #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimization and Control (math.OC)
8. Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs
   2024/03/20 by Charles C. Margossian, Loucas Pillaud‐Vivien, Loucas Pillaud-Vivien +4 · 1 voice · 3 citations
   Computer Science · Physics and Astronomy · #Bayesian Methods and Mixture Models #Gaussian Processes and Bayesian Inference #Model Reduction and Neural Networks #cs.LG #stat.CO #stat.ML
9. Batch and match: black-box variational inference with a score-based divergence
   2024/02/22 by Cai, Diana, Modi, Chirag, Pillaud-Vivien, Loucas +4 · 4 citations
   #Artificial Intelligence (cs.AI) #Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
10. Statistical Estimation of the Poincar 'e constant and Application to\n Sampling Multimodal Distributions
    2019/10/22 by Loucas Pillaud‐Vivien, Pillaud-Vivien, Loucas, Francis Bach +7 · 1 citation
    Computer Science · Decision Sciences · Mathematics · Physics and Astronomy · #Bayesian Methods and Mixture Models #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Markov Chains and Monte Carlo Methods #Optimization and Control (math.OC) #Probabilistic and Robust Engineering Design #Probability (math.PR) #Statistical Mechanics and Entropy
11. On Single Index Models beyond Gaussian Data
    2023/07/28 by Joan Bruna, Bruna, Joan, Loucas Pillaud‐Vivien +3 · 1 citation
    Computer Science · Mathematics · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Statistical Methods and Inference #Stochastic Gradient Optimization Techniques