no code implementations • 7 Sep 2023 • Abdolmahdi Bagheri, Mohammad Pasande, Kevin Bello, Babak Nadjar Araabi, Alireza Akhondi-Asl
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.
1 code implementation • NeurIPS 2023 • Tianyu Chen, Kevin Bello, Bryon Aragam, Pradeep Ravikumar
Structural causal models (SCMs) are widely used in various disciplines to represent causal relationships among variables in complex systems.
1 code implementation • 26 May 2023 • Chang Deng, Kevin Bello, Bryon Aragam, Pradeep Ravikumar
In this work, we delve into the optimization challenges associated with this class of non-convex programs.
3 code implementations • 16 Sep 2022 • Kevin Bello, Bryon Aragam, Pradeep Ravikumar
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.
no code implementations • 17 Feb 2021 • Hanbyul Lee, Kevin Bello, Jean Honorio
Inference is a main task in structured prediction and it is naturally modeled with a graph.
no code implementations • 16 Feb 2021 • 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.
no code implementations • NeurIPS 2021 • Gregory Dexter, Kevin Bello, Jean Honorio
Inverse Reinforcement Learning (IRL) is the problem of finding a reward function which describes observed/known expert behavior.
no code implementations • NeurIPS 2020 • Kevin Bello, Jean Honorio
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.
no code implementations • 1 Jul 2020 • QiuLing Xu, Kevin Bello, Jean Honorio
Robustness of machine learning methods is essential for modern practical applications.
no code implementations • 28 Jun 2019 • Asish Ghoshal, Kevin Bello, Jean Honorio
Discovering cause-effect relationships between variables from observational data is a fundamental challenge in many scientific disciplines.
no code implementations • 2 Jun 2019 • 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.
no code implementations • NeurIPS 2019 • Kevin Bello, Jean Honorio
Our results show that exact recovery is possible and achievable in polynomial time for a large class of graphs.
no code implementations • NeurIPS 2018 • Kevin Bello, Jean Honorio
The standard margin-based structured prediction commonly uses a maximum loss over all possible structured outputs.
no code implementations • NeurIPS 2018 • Kevin Bello, Jean Honorio
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.