3 citations · 4 across the 3 of their papers we have counts for
5 papers
Optimistic and Adaptive Lagrangian Hedging
Ryan D'Orazio, Ruitong Huang
In online learning an algorithm plays against an environment with losses possibly picked by an adversary at each round. The generality of this framework includes problems that are…
Solving Common-Payoff Games with Approximate Policy Iteration
Samuel Sokota, Edward Lockhart, Finbarr Timbers +6
For artificially intelligent learning systems to have widespread applicability in real-world settings, it is important that they be able to operate decentrally. Unfortunately, dece…
Alternative Function Approximation Parameterizations for Solving Games: An Analysis of -Regression Counterfactual Regret Minimization
Ryan D'Orazio, Dustin Morrill, James R. Wright +1
Function approximation is a powerful approach for structuring large decision problems that has facilitated great achievements in the areas of reinforcement learning and game playin…
Bounds for Approximate Regret-Matching Algorithms
Ryan D'Orazio, Dustin Morrill, James R. Wright
A dominant approach to solving large imperfect-information games is Counterfactural Regret Minimization (CFR). In CFR, many regret minimization problems are combined to solve the g…
Simultaneous Prediction Intervals for Patient-Specific Survival Curves
Samuel Sokota, Ryan D'Orazio, Khurram Javed +2
Accurate models of patient survival probabilities provide important information to clinicians prescribing care for life-threatening and terminal ailments. A recently developed clas…