Statistical Learning
Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies Oscar Li1, James Harrison, Jascha Sohl-Dickstein, Virginia Smith
First-order optimization methods are a foundational tool in machine learning.
Variance-Reduced Gradient Estimation via Noise-Reuse in Online Evolution Strategies Oscar Li1, James Harrison, Jascha Sohl-Dickstein, Virginia Smith
First-order optimization methods are a foundational tool in machine learning.
On the Power of Decision Trees in Auto-Regressive Language Modeling
Tree-based models, particularly favored for handling tabular data (Grinsztajn et al., 2022), continue
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing Simon Buchholz
Therefore, it's important to study whether we can and also how to
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models
We propose a novel loss function called Energy Discrepancy (ED) which does not rely on the computation of scores or expensive Markov chain Monte Carlo.
Facilitating Multimodal Classification via Dynamically Learning Modality Gap Y ang
Multimodal learning falls into the trap of the optimization dilemma due to the modality imbalance phenomenon, leading to unsatisfactory performance in real applications.