17 citations · 28 across the 18 of their papers we have counts for
18 papers
Markov Equivalence and Consistency in Differentiable Structure Learning
Chang Deng, Kevin Bello, Pradeep Ravikumar +1
Existing approaches to differentiable structure learning of directed acyclic graphs (DAGs) rely on strong identifiability assumptions in order to guarantee that global minimizers o…
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…
: Featuring Large Language Models with Causal Reasoning
Abdolmahdi Bagheri, Matin Alinejad, Mahdi Dehshiri +2
Causal reasoning is one of the primary bottlenecks that Large Language Models (LLMs) must overcome to attain human-level intelligence. Recent studies indicate that LLMs display nea…
Discovering Dynamic Effective Connectome of Brain with Bayesian Dynamic DAG Learning
Abdolmahdi Bagheri, Mohammad Pasande, Kevin Bello +2
Understanding the complex mechanisms of the brain can be unraveled by extracting the Dynamic Effective Connectome (DEC). Recently, score-based Directed Acyclic Graph (DAG) discover…
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…
Optimizing NOTEARS Objectives via Topological Swaps
Chang Deng, Kevin Bello, Bryon Aragam +1
Recently, an intriguing class of non-convex optimization problems has emerged in the context of learning directed acyclic graphs (DAGs). These problems involve minimizing a given l…
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…
On the Fundamental Limits of Exact Inference in Structured Prediction
Hanbyul Lee, Kevin Bello, Jean Honorio
Inference is a main task in structured prediction and it is naturally modeled with a graph. In the context of Markov random fields, noisy observations corresponding to nodes and ed…
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…
A Le Cam Type Bound for Adversarial Learning and Applications
Qiuling Xu, Kevin Bello, Jean Honorio
Robustness of machine learning methods is essential for modern practical applications. Given the arms race between attack and defense methods, one may be curious regarding the fund…
Fairness constraints can help exact inference in structured prediction
Kevin Bello, Jean Honorio
Many inference problems in structured prediction can be modeled as maximizing a score function on a space of labels, where graphs are a natural representation to decompose the tota…
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…
Direct Learning with Guarantees of the Difference DAG Between Structural Equation Models
Asish Ghoshal, Kevin Bello, Jean Honorio
Discovering cause-effect relationships between variables from observational data is a fundamental challenge in many scientific disciplines. However, in many situations it is desira…
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…