In this correspondence, we introduce a minimax regret criteria to the least squares problems with bounded data uncertainties and solve it using semi-definite programming. We investigate a robust minimax least squares approach that minimizes a worst case difference regret. The regret is defined as the difference between a squared data error and the smallest attainable squared data error of a lea...

A minimax regret test is proposed for deciding whether one of N populations has slipped to the right of the rest, under the null hypothesis that all populations are identical. The problem is formulated as a multiple decision problem under uncertainty relative to a priori distribution of hypotheses and incomplete knowledge of probability distributions of observations. For the problem with nuisan...

This paper introduces a new solution concept, a minimax regret equilibrium, which allows for the possibility that players are uncertain about the rationality and conjectures of their opponents. We provide several applications of our concept. In particular, we consider pricesetting environments and show that optimal pricing policy follows a non-degenerate distribution. The induced price dispersi...

We consider the pricing problem faced by a monopolist who sells a product to a population of consumers over a finite time horizon. Customers are heterogeneous along two dimensions: (i) willingness-to-pay for the product and (ii) arrival time during the selling season. We assume that the seller knows only the support of the customers’ valuations and do not make any other distributional assumptio...

I use the minimax-regret criterion to study choice between two treatments when some outcomes in the study population are unobservable and the distribution of missing data is unknown. I first assume that observable features of the study population are known and derive the treatment rule that minimizes maximum regret over all possible distributions of missing data. When no treatment is dominant, ...

In this thesis, we investigate the problem of decision-making in large two-player zero-sumgames using Monte Carlo sampling and regret minimization methods. We demonstrate fourmajor contributions. The first is Monte Carlo Counterfactual Regret Minimization (MC-CFR): a generic family of sample-based algorithms that compute near-optimal equilibriumstrategies. Secondly, we develop a...

[4] Igor Averbakh. Minmax regret solutions for minmax optimization problems with uncertainty. [5] Igor Averbakh. On the complexity of a class of combinatorial optimization problems with uncertainty. [7] Igor Averbakh and Oded Berman. Minimax regret p-center location on a network with demand uncertainty. [8] Igor Averbakh and Oded Berman. Minmax p-traveling salesman location problems on a tree.

We study the problem of online learning with a notion of regret defined with respect to a set of strategies. We develop tools for analyzing the minimax rates and for deriving regret-minimization algorithms in this scenario. While the standard methods for minimizing the usual notion of regret fail, through our analysis we demonstrate existence of regret-minimization methods that compete with suc...

To cope with changing environments, recent developments in online learning have introduced the concepts of adaptive regret and dynamic regret independently. In this paper, we illustrate an intrinsic connection between these two concepts by showing that the dynamic regret can be expressed in terms of the adaptive regret and the functional variation. This observation implies that strongly adaptiv...