158 citations · 280 across the 27 of their papers we have counts for
6 papers · 1 filter
Post-hoc Uncertainty Learning using a Dirichlet Meta-Model
Maohao Shen, Yuheng Bu, Prasanna Sattigeri +3
It is known that neural networks have the problem of being over-confident when directly using the output label distribution to generate uncertainty measures. Existing methods mainl…
On Convergence of Gradient Descent Ascent: A Tight Local Analysis
Haochuan Li, Farzan Farnia, Subhro Das +1
Gradient Descent Ascent (GDA) methods are the mainstream algorithms for minimax optimization in generative adversarial networks (GANs). Convergence properties of GDA have drawn sig…
Practical Skills Demand Forecasting via Representation Learning of Temporal Dynamics
Maysa M. Garcia de Macedo, Wyatt Clarke, Eli Lucherini +4
Rapid technological innovation threatens to leave much of the global workforce behind. Today's economy juxtaposes white-hot demand for skilled labor against stagnant employment pro…
An alternative approach for distributed parameter estimation under Gaussian settings
Subhro Das
This paper takes a different approach for the distributed linear parameter estimation over a multi-agent network. The parameter vector is considered to be stochastic with a Gaussia…
On observability and optimal gain design for distributed linear filtering and prediction
Subhro Das
This paper presents a new approach to distributed linear filtering and prediction. The problem under consideration consists of a random dynamical system observed by a multi-agent n…
Attacking c-MARL More Effectively: A Data Driven Approach
Nhan H. Pham, Lam M. Nguyen, Jie Chen +3
In recent years, a proliferation of methods were developed for cooperative multi-agent reinforcement learning (c-MARL). However, the robustness of c-MARL agents against adversarial…