Search Results for author:
Found 14 papers, 3 papers with code
Discovering Dynamic Effective Connectome of Brain with Bayesian Dynamic DAG Learning
However, learning DEC through these methods still faces two main challenges: one with the fundamental impotence of high-dimensional dynamic DAG discovery methods and the other with the low quality of fMRI data.
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models
Structural causal models (SCMs) are widely used in various disciplines to represent causal relationships among variables in complex systems.
Optimizing NOTEARS Objectives via Topological Swaps
In this work, we delve into the optimization challenges associated with this class of non-convex programs.
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization
From the optimization side, we drop the typically used augmented Lagrangian scheme and propose DAGMA ($\textit{DAGs via M-matrices for Acyclicity}$), a method that resembles the central path for barrier methods.
On the Fundamental Limits of Exact Inference in Structured Prediction
Inference is a main task in structured prediction and it is naturally modeled with a graph.
A Thorough View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy
Performing inference in graphs is a common task within several machine learning problems, e. g., image segmentation, community detection, among others.
Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees
Inverse Reinforcement Learning (IRL) is the problem of finding a reward function which describes observed/known expert behavior.
A Le Cam Type Bound for Adversarial Learning and Applications
Robustness of machine learning methods is essential for modern practical applications.
Fairness constraints can help exact inference in structured prediction
Given a generative model with an undirected connected graph $G$ and true vector of binary labels, it has been previously shown that when $G$ has good expansion properties, such as complete graphs or $d$-regular expanders, one can exactly recover the true labels (with high probability and in polynomial time) from a single noisy observation of each edge and node.
Direct Learning with Guarantees of the Difference DAG Between Structural Equation Models
Discovering cause-effect relationships between variables from observational data is a fundamental challenge in many scientific disciplines.
Exact inference in structured prediction
Our results show that exact recovery is possible and achievable in polynomial time for a large class of graphs.
Minimax bounds for structured prediction
Structured prediction can be considered as a generalization of many standard supervised learning tasks, and is usually thought as a simultaneous prediction of multiple labels.
Learning latent variable structured prediction models with Gaussian perturbations
The standard margin-based structured prediction commonly uses a maximum loss over all possible structured outputs.
Computationally and statistically efficient learning of causal Bayes nets using path queries
In this paper we first propose a polynomial time algorithm for learning the exact correctly-oriented structure of the transitive reduction of any causal Bayesian network with high probability, by using interventional path queries.
