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Found 8 papers, 0 papers with code
AdaStop: adaptive statistical testing for sound comparisons of Deep RL agents
When comparing several RL algorithms, a major question is how many executions must be made and how can we ensure that the results of such a comparison are theoretically sound.
Stochastic Online Instrumental Variable Regression: Regrets for Endogeneity and Bandit Feedback
Endogeneity, i. e. the dependence of noise and covariates, is a common phenomenon in real data due to omitted variables, strategic behaviours, measurement errors etc.
Entropy Regularized Reinforcement Learning with Cascading Networks
Compared to these learning frameworks, one of the major difficulties of RL is the absence of i. i. d.
Cooperative Online Learning with Feedback Graphs
We study the interplay between feedback and communication in a cooperative online learning setting where a network of agents solves a task in which the learners' feedback is determined by an arbitrary graph.
Finding Stable Matchings in PhD Markets with Consistent Preferences and Cooperative Partners
Our setting is motivated by a PhD market of students, advisors, and co-advisors, and can be generalized to supply chain networks viewed as $n$-sided markets.
Computer Science and Game Theory Theoretical Economics Combinatorics
Two-Sided Matching Markets in the ELLIS 2020 PhD Program
In this work we summarize the procedure that, in its final step, matches students to advisors in the ELLIS 2020 PhD program.
Computer Science and Game Theory Theoretical Economics Combinatorics
An Efficient Algorithm for Cooperative Semi-Bandits
Furthermore, we prove that this is only $\sqrt$ k log k-away from the best achievable rate and that Coop-FTPL has a state-of-the-art T 3/2 worst-case computational complexity.
Clustering of solutions in the symmetric binary perceptron
The geometrical features of the (non-convex) loss landscape of neural network models are crucial in ensuring successful optimization and, most importantly, the capability to generalize well.
