Statistical Learning
Robust Matrix Sensing in the Semi-Random Model
Low-rank matrix recovery is a fundamental problem in machine learning with numerous applications.
FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models
Empirically, we verify the computational efficiency of our methods.
Learning Diffusion Priors from Observations by Expectation Maximization
Diffusion models recently proved to be remarkable priors for Bayesian inverse problems.
Explore to Generalize in Zero-Shot RL
Recent developments in reinforcement learning (RL) led to algorithms that surpass human experts in a broad range of tasks [Mnih et al., 2015, Vinyals et al., 2019, Schrittwieser et al., 2020, Wurman et al.,
Going beyond persistent homology using persistent homology Johanna Immonen University of Helsinki
Augmenting these graph models with topological features via persistent homology (PH) has gained prominence, but identifying the class of attributed graphs that PH can recognize remains open. We introduce a novel concept of color-separating sets to provide a complete resolution to this important problem.
Finding Local Minima Efficiently in Decentralized Optimization
In this paper we study the second-order optimality of decentralized stochastic algorithm that escapes saddle point efficiently for nonconvex optimization problems.
Certified Minimax Unlearning with Generalization Rates and Deletion Capacity
Gaussian mechanism borrowed from differential privacy.
