Statistical Learning
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
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.
Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures
We present improved algorithms with worst-case regret guarantees for the stochastic linear bandit problem. The widely used "optimism in the face of uncertainty"
On the Gini-impurity Preservation For Privacy Random Forests
This work takes one step towards data encryption by incorporating some crucial ingredients of learning algorithm.
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities
This paper delves into stochastic optimization problems that involve Markovian noise. We present a unified approach for the theoretical analysis of first-order gradient methods for stochastic optimization and variational inequalities.