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Found 12 papers, 3 papers with code
Double-Loop Unadjusted Langevin Algorithm
A well-known first-order method for sampling from log-concave probability distributions is the Unadjusted Langevin Algorithm (ULA).
Efficient proximal mapping of the path-norm regularizer of shallow networks
We demonstrate two new important properties of the path-norm regularizer for shallow neural networks.
Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning
While Reinforcement Learning (RL) aims to train an agent from a reward function in a given environment, Inverse Reinforcement Learning (IRL) seeks to recover the reward function from observing an expert's behavior.
Score matching enables causal discovery of nonlinear additive noise models
This paper demonstrates how to recover causal graphs from the score of the data distribution in non-linear additive (Gaussian) noise models.
The Effect of the Intrinsic Dimension on the Generalization of Quadratic Classifiers
We demonstrate this by deriving an upper bound on the Rademacher Complexity that depends on two key quantities: (i) the intrinsic dimension, which is a measure of isotropy, and (ii) the largest eigenvalue of the second moment (covariance) matrix of the distribution.
Linear Convergence of SGD on Overparametrized Shallow Neural Networks
Despite the non-convex landscape, first-order methods can be shown to reach global minima when training overparameterized neural networks, where the number of parameters far exceed the number of training data.
Efficient Proximal Mapping of the 1-path-norm of Shallow Networks
We demonstrate two new important properties of the 1-path-norm of shallow neural networks.
Lipschitz constant estimation of Neural Networks via sparse polynomial optimization
We introduce LiPopt, a polynomial optimization framework for computing increasingly tighter upper bounds on the Lipschitz constant of neural networks.
Robust Reinforcement Learning via Adversarial training with Langevin Dynamics
We introduce a sampling perspective to tackle the challenging task of training robust Reinforcement Learning (RL) agents.
Efficient learning of smooth probability functions from Bernoulli tests with guarantees
We study the fundamental problem of learning an unknown, smooth probability function via pointwise Bernoulli tests.
Mirrored Langevin Dynamics
We consider the problem of sampling from constrained distributions, which has posed significant challenges to both non-asymptotic analysis and algorithmic design.
High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups
In this paper, we consider the approach of Kandasamy et al. (2015), in which the high-dimensional function decomposes as a sum of lower-dimensional functions on subsets of the underlying variables.
