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Found 13 papers, 2 papers with code
Near-Optimal Solutions of Constrained Learning Problems
These requirements can be imposed (with generalization guarantees) by formulating constrained learning problems that can then be tackled by dual ascent algorithms.
Reply to 'Comments on Graphon Signal Processing' [arXiv:2310.14683]
This technical note addresses an issue [arXiv:2310. 14683] with the proof (but not the statement) of [arXiv:2003. 05030, Proposition 4].
Resilient Constrained Learning
This paper presents a constrained learning approach that adapts the requirements while simultaneously solving the learning task.
Learning Globally Smooth Functions on Manifolds
To do so, it shows that under typical conditions the problem of learning a Lipschitz continuous function on a manifold is equivalent to a dynamically weighted manifold regularization problem.
Automatic Data Augmentation via Invariance-Constrained Learning
Despite its ubiquity, its effectiveness depends on the choices of which transformations to apply, when to do so, and how often.
Ranked #1 on
Image Classification
on SVHN
(Percentage correct metric)
Probabilistically Robust Learning: Balancing Average- and Worst-case Performance
From a theoretical point of view, this framework overcomes the trade-offs between the performance and the sample-complexity of worst-case and average-case learning.
Transferability Properties of Graph Neural Networks
In this paper, we study the problem of training GNNs on graphs of moderate size and transferring them to large-scale graphs.
Adversarial Robustness with Semi-Infinite Constrained Learning
In particular, we leverage semi-infinite optimization and non-convex duality theory to show that adversarial training is equivalent to a statistical problem over perturbation distributions, which we characterize completely.
Constrained Learning with Non-Convex Losses
In this paper, we overcome this issue by learning in the empirical dual domain, where constrained statistical learning problems become unconstrained and deterministic.
Towards Safe Continuing Task Reinforcement Learning
However, to transfer from learning safety to learning safely, there are two hurdles that need to be overcome: (i) it has to be possible to learn the policy without having to re-initialize the system; and (ii) the rollouts of the system need to be in themselves safe.
State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning with Rewards
Thus, as we illustrate by an example, while previous methods can fail at finding optimal policies, running the dual dynamics while executing the augmented policy yields an algorithm that provably samples actions from the optimal policy.
Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning
Prediction credibility measures, in the form of confidence intervals or probability distributions, are fundamental in statistics and machine learning to characterize model robustness, detect out-of-distribution samples (outliers), and protect against adversarial attacks.
Graphon Signal Processing
Graphons are infinite-dimensional objects that represent the limit of convergent sequences of graphs as their number of nodes goes to infinity.
