275 citations · 624 across the 32 of their papers we have counts for
23 papers · 1 filter
Learned End-to-End Guidance Schedules for Diffusion Models
Aneesh Barthakur, Mathias Niepert, Luiz F. O. Chamon
Diffusion models are a powerful generative paradigm used across multimedia and scientific applications. Guided diffusion methods impose requirements on the generation by adding the…
Everywhere Learning: Artificial Intelligence with Pointwise Constraints
Ignacio Boero, Ignacio Hounie, Luiz F. O. Chamon +2
Everywhere learning is a new paradigm whereby Artificial Intelligence (AI) systems are trained to satisfy loss constraints with probability one over the data distribution. This is…
Learning with Statistical Equality Constraints
Aneesh Barthakur, Luiz F. O. Chamon
As machine learning applications grow increasingly ubiquitous and complex, they face an increasing set of requirements beyond accuracy. The prevalent approach to handle this challe…
Adaptive Symmetrization of the KL Divergence
Omri Ben-Dov, Luiz F. O. Chamon
The forward Kullback-Leibler (KL) divergence is a ubiquitous objective for fitting a parameterized distribution to samples due to its tractability and equivalence to maximum likeli…
Learning (Approximately) Equivariant Networks via Constrained Optimization
Andrei Manolache, Luiz F. O. Chamon, Mathias Niepert
Equivariant neural networks are designed to respect symmetries through their architecture, boosting generalization and sample efficiency when those symmetries are present in the da…
Solving Differential Equations with Constrained Learning
Viggo Moro, Luiz F. O. Chamon
(Partial) differential equations (PDEs) are fundamental tools for describing natural phenomena, making their solution crucial in science and engineering. While traditional methods,…
Near-Optimal Solutions of Constrained Learning Problems
Juan Elenter, Luiz F. O. Chamon, Alejandro Ribeiro
With the widespread adoption of machine learning systems, the need to curtail their behavior has become increasingly apparent. This is evidenced by recent advancements towards deve…
Resilient Constrained Learning
Ignacio Hounie, Alejandro Ribeiro, Luiz F. O. Chamon
When deploying machine learning solutions, they must satisfy multiple requirements beyond accuracy, such as fairness, robustness, or safety. These requirements are imposed during t…
Learning Globally Smooth Functions on Manifolds
Juan Cervino, Luiz F. O. Chamon, Benjamin D. Haeffele +2
Smoothness and low dimensional structures play central roles in improving generalization and stability in learning and statistics. This work combines techniques from semi-infinite…
Automatic Data Augmentation via Invariance-Constrained Learning
Ignacio Hounie, Luiz F. O. Chamon, Alejandro Ribeiro
Underlying data structures, such as symmetries or invariances to transformations, are often exploited to improve the solution of learning tasks. However, embedding these properties…
Probabilistically Robust Learning: Balancing Average- and Worst-case Performance
Alexander Robey, Luiz F. O. Chamon, George J. Pappas +1
Many of the successes of machine learning are based on minimizing an averaged loss function. However, it is well-known that this paradigm suffers from robustness issues that hinder…
Transferability Properties of Graph Neural Networks
Luana Ruiz, Luiz F. O. Chamon, Alejandro Ribeiro
Graph neural networks (GNNs) are composed of layers consisting of graph convolutions and pointwise nonlinearities. Due to their invariance and stability properties, GNNs are provab…
Constrained Learning with Non-Convex Losses
Luiz F. O. Chamon, Santiago Paternain, Miguel Calvo-Fullana +1
Though learning has become a core component of modern information processing, there is now ample evidence that it can lead to biased, unsafe, and prejudiced systems. The need to im…
Towards Safe Continuing Task Reinforcement Learning
Miguel Calvo-Fullana, Luiz F. O. Chamon, Santiago Paternain
Safety is a critical feature of controller design for physical systems. When designing control policies, several approaches to guarantee this aspect of autonomy have been proposed,…
State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning with Rewards
Miguel Calvo-Fullana, Santiago Paternain, Luiz F. O. Chamon +1
A common formulation of constrained reinforcement learning involves multiple rewards that must individually accumulate to given thresholds. In this class of problems, we show a sim…
Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning
Luiz F. O. Chamon, Santiago Paternain, Alejandro Ribeiro
Prediction credibility measures, in the form of confidence intervals or probability distributions, are fundamental in statistics and machine learning to characterize model robustne…
Probably Approximately Correct Constrained Learning
Luiz F. O. Chamon, Alejandro Ribeiro
As learning solutions reach critical applications in social, industrial, and medical domains, the need to curtail their behavior has become paramount. There is now ample evidence t…
Graphon Neural Networks and the Transferability of Graph Neural Networks
Luana Ruiz, Luiz F. O. Chamon, Alejandro Ribeiro
Graph neural networks (GNNs) rely on graph convolutions to extract local features from network data. These graph convolutions combine information from adjacent nodes using coeffici…
The empirical duality gap of constrained statistical learning
Luiz F. O. Chamon, Santiago Paternain, Miguel Calvo-Fullana +1
This paper is concerned with the study of constrained statistical learning problems, the unconstrained version of which are at the core of virtually all of modern information proce…
Constrained Reinforcement Learning Has Zero Duality Gap
Santiago Paternain, Luiz F. O. Chamon, Miguel Calvo-Fullana +1
Autonomous agents must often deal with conflicting requirements, such as completing tasks using the least amount of time/energy, learning multiple tasks, or dealing with multiple o…
Functional Nonlinear Sparse Models
Luiz F. O. Chamon, Yonina C. Eldar, Alejandro Ribeiro
Signal processing is rich in inherently continuous and often nonlinear applications, such as spectral estimation, optical imaging, and super-resolution microscopy, in which sparsit…
Learning Optimal Resource Allocations in Wireless Systems
Mark Eisen, Clark Zhang, Luiz F. O. Chamon +2
This paper considers the design of optimal resource allocation policies in wireless communication systems which are generically modeled as a functional optimization problem with st…
Approximate Supermodularity Bounds for Experimental Design
Luiz F. O. Chamon, Alejandro Ribeiro
This work provides performance guarantees for the greedy solution of experimental design problems. In particular, it focuses on A- and E-optimal designs, for which typical guarante…