Mallada, Enrique

10 publications

TMLR 2025 A Local Polyak-Łojasiewicz and Descent Lemma of Gradient Descent for Overparametrized Linear Models Ziqing Xu, Hancheng Min, Salma Tarmoun, Enrique Mallada, Rene Vidal

AISTATS 2025 Understanding the Learning Dynamics of LoRA: A Gradient Flow Perspective on Low-Rank Adaptation in Matrix Factorization Ziqing Xu, Hancheng Min, Lachlan Ewen MacDonald, Jinqi Luo, Salma Tarmoun, Enrique Mallada, Rene Vidal

AISTATS 2025 Variance-Aware Linear UCB with Deep Representation for Neural Contextual Bandits Ha Manh Bui, Enrique Mallada, Anqi Liu

ICLR 2024 Early Neuron Alignment in Two-Layer ReLU Networks with Small Initialization Hancheng Min, Enrique Mallada, Rene Vidal

L4DC 2023 Learning Coherent Clusters in Weakly-Connected Network Systems Hancheng Min, Enrique Mallada

AISTATS 2023 Linear Convergence of Gradient Descent for Finite Width Over-Parametrized Linear Networks with General Initialization Ziqing Xu, Hancheng Min, Salma Tarmoun, Enrique Mallada, Rene Vidal

ICML 2023 On the Convergence of Gradient Flow on Multi-Layer Linear Models Hancheng Min, Rene Vidal, Enrique Mallada

L4DC 2022 Reinforcement Learning with Almost Sure Constraints Agustin Castellano, Hancheng Min, Enrique Mallada, Juan Andrés Bazerque

ICML 2021 A Nullspace Property for Subspace-Preserving Recovery Mustafa D Kaba, Chong You, Daniel P Robinson, Enrique Mallada, Rene Vidal

ICML 2021 On the Explicit Role of Initialization on the Convergence and Implicit Bias of Overparametrized Linear Networks Hancheng Min, Salma Tarmoun, Rene Vidal, Enrique Mallada