Going to ICML 2024 ? Here is a curated list of papers related to my research interests. Feel free to suggest additions !
Learning Theory
Asymmetry in Low-Rank Adapters of Foundation Models
How do Transformers Perform In-Context Autoregressive Learning ?
Keep the Momentum: Conservation Laws beyond Euclidean Gradient Flows
Differentiable Distributionally Robust Optimization Layers
DOGE: Domain Reweighting with Generalization Estimation
Slicing Mutual Information Generalization Bounds for Neural Networks
Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation
Generalization Bounds for Causal Regression: Insights, Guarantees and Sensitivity Analysis
From Generalization Analysis to Optimization Designs for State Space Models
Privacy
Privacy-Preserving Data Release Leveraging Optimal Transport and Particle Gradient Descent
Provable Privacy with Non-Private Pre-Processing
Privacy Preserving Adaptative Experiment Design
Shifted Interpolation for Differential Privacy
Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss
Privacy Attacks in Decentralized Learning
Applications
CARTE: Pretraining and Transfer for Tabular Learning
A New Linear Scaling Rule for Private Adaptive Hyperparameter Optimization
On The Fairness Impacts of Hardware Selection in Machine Learning