41 citations · 42 across the 2 of their papers we have counts for
6 papers
On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks
Maximilian Seitzer, Arash Tavakoli, Dimitrije Antic +1
Capturing aleatoric uncertainty is a critical part of many machine learning systems. In deep learning, a common approach to this end is to train a neural network to estimate the pa…
Orchestrated Value Mapping for Reinforcement Learning
Mehdi Fatemi, Arash Tavakoli
We present a general convergent class of reinforcement learning algorithms that is founded on two distinct principles: (1) mapping value estimates to a different space using arbitr…
A neural network oracle for quantum nonlocality problems in networks
Tamás Kriváchy, Yu Cai, Daniel Cavalcanti +3
Characterizing quantum nonlocality in networks is a challenging, but important problem. Using quantum sources one can achieve distributions which are unattainable classically. A ke…
Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning
Harm van Seijen, Mehdi Fatemi, Arash Tavakoli
In an effort to better understand the different ways in which the discount factor affects the optimization process in reinforcement learning, we designed a set of experiments to st…
Exploring Restart Distributions
Arash Tavakoli, Vitaly Levdik, Riashat Islam +2
We consider the generic approach of using an experience memory to help exploration by adapting a restart distribution. That is, given the capacity to reset the state with those cor…
Multiplayer Games for Learning Multirobot Coordination Algorithms
Arash Tavakoli, Haig Nalbandian, Nora Ayanian
Humans have an impressive ability to solve complex coordination problems in a fully distributed manner. This ability, if learned as a set of distributed multirobot coordination str…