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06/04/2023
Resilient Constrained Learning
When deploying machine learning solutions, they must satisfy multiple re...
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09/29/2022
Automatic Data Augmentation via Invariance-Constrained Learning
Underlying data structures, such as symmetries or invariances to transfo...
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12/09/2021
Transferability Properties of Graph Neural Networks
Graph neural networks (GNNs) are deep convolutional architectures consis...
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03/08/2021
Constrained Learning with Non-Convex Losses
Though learning has become a core technology of modern information proce...
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02/24/2021
Towards Safe Continuing Task Reinforcement Learning
Safety is a critical feature of controller design for physical systems. ...
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02/23/2021
State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning with Rewards
Constrained reinforcement learning involves multiple rewards that must i...
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11/24/2020
Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning
Prediction credibility measures, in the form of confidence intervals or ...
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06/09/2020
Probably Approximately Correct Constrained Learning
As learning solutions reach critical applications in social, industrial,...
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06/05/2020
Graphon Neural Networks and the Transferability of Graph Neural Networks
Graph neural networks (GNNs) rely on graph convolutions to extract local...
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02/12/2020
The empirical duality gap of constrained statistical learning
This paper is concerned with the study of constrained statistical learni...
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12/06/2019
Risk-Aware MMSE Estimation
Despite the simplicity and intuitive interpretation of Minimum Mean Squa...
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11/20/2019
Safe Policies for Reinforcement Learning via Primal-Dual Methods
In this paper, we study the learning of safe policies in the setting of ...
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10/29/2019
Constrained Reinforcement Learning Has Zero Duality Gap
Autonomous agents must often deal with conflicting requirements, such as...
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09/16/2019
Source Seeking in Unknown Environments with Convex Obstacles
Navigation tasks often cannot be defined in terms of a target, either be...
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05/07/2019
Sparse multiresolution representations with adaptive kernels
Reproducing kernel Hilbert spaces (RKHSs) are key elements of many non-p...
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11/01/2018
Functional Nonlinear Sparse Models
Signal processing in inherently continuous and often nonlinear applicati...
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07/21/2018
Learning Optimal Resource Allocations in Wireless Systems
This paper considers the design of optimal resource allocation policies ...
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11/04/2017
Approximate Supermodularity Bounds for Experimental Design
This work provides performance guarantees for the greedy solution of exp...
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04/05/2017