Diffusion models power a vast majority of text-to-audio (TTA) generation...
Imitation learning suffers from causal confusion. This phenomenon occurs...
Conventional optimization methods in machine learning and controls rely
...
This paper studies the role of over-parametrization in solving non-conve...
Recent works have introduced input-convex neural networks (ICNNs) as lea...
While it is shown in the literature that simultaneously accurate and rob...
In multi-agent dynamic games, the Nash equilibrium state trajectory of e...
Many fundamental low-rank optimization problems, such as matrix completi...
In this discussion paper, we survey recent research surrounding robustne...
In this paper, we consider the infinite-horizon reach-avoid zero-sum gam...
This paper is concerned with low-rank matrix optimization, which has fou...
The non-convexity of the artificial neural network (ANN) training landsc...
Real-world reinforcement learning (RL) problems often demand that agents...
It is well-known that the Burer-Monteiro (B-M) factorization approach ca...
Graph neural networks (GNNs) have been successfully employed in a myriad...
Recent work has shown that the training of a one-hidden-layer, scalar-ou...
In this paper, we study a general low-rank matrix recovery problem with
...
In this paper, we study certifying the robustness of ReLU neural network...
Supervised learning models have been increasingly used for making decisi...
There have been major advances on the design of neural networks, but sti...
Methods to certify the robustness of neural networks in the presence of ...
When using deep neural networks to operate safety-critical systems, asse...
Graph Neural Networks (GNNs) are widely used deep learning models that l...
In this paper, we consider the problem of certifying the robustness of n...
Variants of Graph Neural Networks (GNNs) for representation learning hav...
In this work, we propose a robust approach to design distributed control...
The operation of power grids is becoming increasingly data-centric. Whil...
In this paper, we consider the problem of unsupervised video object
segm...
Graph convolutional networks (GCNs) are powerful tools for graph-structu...
This paper addresses the problem of identifying sparse linear time-invar...
Nonconvex matrix recovery is known to contain no spurious local minima u...
This work is concerned with the non-negative robust principal component
...
Cloud Robotics is a paradigm where distributed robots are connected to c...
When the linear measurements of an instance of low-rank matrix recovery
...
In this paper, we study the system identification porblem for sparse lin...
The sparse inverse covariance estimation problem is commonly solved usin...
The sparse inverse covariance estimation problem is commonly solved usin...
In this paper, we consider the Graphical Lasso (GL), a popular optimizat...
Graphical Lasso (GL) is a popular method for learning the structure of a...