[PDF] Top 20 Algorithms for Average Regret Minimization
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Algorithms for Average Regret Minimization
... bounded regret. Thereby, regret measures the unhap- piness of users which would like to select their favorite ob- ject from set S but now can only select their favorite object from the subset S ′ ...maximum ... See full document
8
Internal Regret with Partial Monitoring: Calibration-Based Optimal Algorithms
... of regret in repeated games: a player (that will be referred as a decision maker or also a forecaster) has no external regret if, asymptotically, his average payoff could not have been greater if he ... See full document
29
Response-Based Approachability with Applications to Generalized No-Regret Problems
... learning algorithms in the adversarial ...the average payoff vector converges to S , no matter what the opponent ...approachability algorithms rely on the primal condition, which is a geometric ... See full document
27
Online Convex Optimization for Sequential Decision Processes and Extensive-Form Games
... no algorithms based on local re- gret minimization at each decision point have been known for this ...any regret-minimization algorithm that al- lows convex functions over the simplex, this ... See full document
9
Routing Without Regret: On Convergence to Nash Equilibria of Regret-Minimizing Algorithms in Routing Games
... follows. Regret-minimizing algorithms are very compelling from the point of view of individuals: if you use a regret-minimizing algorithm to drive to work each day, you will get a good guarantee on ... See full document
21
Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games Using Baselines
... The two fields of RL and computational game theory have largely grown independently. However, there has been re- cent work that relates approaches within these two com- munities. Fictitious self-play uses RL to compute ... See full document
8
Average Stability is Invariant to Data Preconditioning. Implications to Exp-concave Empirical Risk Minimization
... online minimization of exp-concave functions, achieves improved (logarithmic) regret bounds that resemble the regret bounds for strongly convex functions Hazan et ...simpler algorithms that ... See full document
13
Beyond the Regret Minimization Barrier: Optimal Algorithms for Stochastic Strongly-Convex Optimization
... Our analysis deviates from the common template of designing a regret minimization algorithm and then using online-to-batch conversion. In fact, we show that the latter approach is inherently suboptimal by ... See full document
24
Solving Imperfect-Information Games via Discounted Regret Minimization
... iterative algorithms are used to approximate an ...iterative algorithms exist (Nesterov 2005; Hoda et ...counterfactual regret minimization (CFR) (Zinkevich et ... See full document
8
Gains and Losses are Fundamentally Different in Regret Minimization: The Sparse Case
... our algorithms to be adaptive over the sparsity level of the observed gain/loss ...The algorithms are proved to essentially achieve the same regret bounds as in the case where s is ... See full document
32
User’s Gender Prediction Based on Smartphone Applications Installed: Analysis from Real World Data to Simulation
... weighted average feature really improves learning algorithms since all learning algorithms with weighted average features performance better than algorithms that are not: they have ... See full document
8
regret
... of regret aversion require that agents can make an ex-post comparison between their choice and a foregone ...wherein regret-neutral agents would unequivocally ...insures regret-averse agents against ... See full document
9
Incremental Algorithms for Hierarchical Classification
... We tested the empirical performance of our on-line algorithm on data sets extracted from two pop- ular corpora of free-text documents. The first data set consists of the first (in chronological order) 100,000 newswire ... See full document
24
Alternate Iterative Algorithms for Minimization of Non-linear Functions
... alternative algorithms for minimization of non linear functions and comparative study is established among the new seven algorithms with Newton’s algorithm by means of ... See full document
9
Comparison of the Asynchronous Differential Evolution and JADE Minimization Algorithms
... In this work we have compared the performance of the recently proposed minimization algorithm of Asynchronous Differential Evolution with Adaptive Correlation Matrix to the widely used JADE method. By using ... See full document
6
On Perturbed Proximal Gradient Algorithms
... solutions with slightly more stable sparsity structure than Solver 1 (less variance on the red curves). Whether such subtle differences exist between the two algorithms (a diminishing step-size and fixed Monte ... See full document
33
Approximation Algorithms for Problems in Makespan Minimization on Unrelated Parallel Machines
... Makespan minimization problems on parallel machines are some of the most studied problems in all of scheduling and combinatorial optimization. Parallel machine scheduling has numerous applications such as in mass ... See full document
112
From External to Internal Regret
... Before presenting these bounds, we first mention one subtle issue. For a given stochastic adver- sary, the optimal policy for minimizing loss may not be the optimal policy for minimizing swap- regret. For example, ... See full document
18
A Review on Main Challenges of Disaster Relief Supply Chain to Reduce Casualties in Case of Natural Disasters
... Some authors [10, 18, 15], used minimization of average or total time of commodities delivery and/or, on average, the latest time of commodities delivery to the [r] ... See full document
12
Average case analysis of algorithms for the maximum subarray problem
... Problem algorithms. So Dantzig’s and Spira’s APSP Problem algorithms were implemented before we implemented ...Problem algorithms were implemented and compared with Dantzig, Spira and MT just for the ... See full document
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