17 citations · 28 across the 18 of their papers we have counts for
10 papers · 1 filter
Identifying General Mechanism Shifts in Linear Causal Representations
Tianyu Chen, Kevin Bello, Francesco Locatello +2
We consider the linear causal representation learning setting where we observe a linear mixing of unknown latent factors, which follow a linear structural causal model. Recent…
Global Optimality in Bivariate Gradient-based DAG Learning
Chang Deng, Kevin Bello, Bryon Aragam +1
Recently, a new class of non-convex optimization problems motivated by the statistical problem of learning an acyclic directed graphical model from data has attracted significant i…
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models
Tianyu Chen, Kevin Bello, Bryon Aragam +1
Structural causal models (SCMs) are widely used in various disciplines to represent causal relationships among variables in complex systems. Unfortunately, the underlying causal st…
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization
Kevin Bello, Bryon Aragam, Pradeep Ravikumar
The combinatorial problem of learning directed acyclic graphs (DAGs) from data was recently framed as a purely continuous optimization problem by leveraging a differentiable acycli…
A Thorough View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy
Kevin Bello, Chuyang Ke, Jean Honorio
Performing inference in graphs is a common task within several machine learning problems, e.g., image segmentation, community detection, among others. For a given undirected connec…
Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees
Gregory Dexter, Kevin Bello, Jean Honorio
Inverse Reinforcement Learning (IRL) is the problem of finding a reward function which describes observed/known expert behavior. The IRL setting is remarkably useful for automated…
Exact inference in structured prediction
Kevin Bello, Jean Honorio
Structured prediction can be thought of as a simultaneous prediction of multiple labels. This is often done by maximizing a score function on the space of labels, which decomposes…
Minimax bounds for structured prediction
Kevin Bello, Asish Ghoshal, Jean Honorio
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. One…
Learning latent variable structured prediction models with Gaussian perturbations
Kevin Bello, Jean Honorio
The standard margin-based structured prediction commonly uses a maximum loss over all possible structured outputs. The large-margin formulation including latent variables not only…
Computationally and statistically efficient learning of causal Bayes nets using path queries
Kevin Bello, Jean Honorio
Causal discovery from empirical data is a fundamental problem in many scientific domains. Observational data allows for identifiability only up to Markov equivalence class. In this…