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Found 39 papers, 2 papers with code
Arbitrage-Free Combinatorial Market Making via Integer Programming
We present a new combinatorial market maker that operates arbitrage-free combinatorial prediction markets specified by integer programs.
Theoretical and Practical Advances on Smoothing for Extensive-Form Games
By introducing a new weighting scheme for the dilated entropy function, we develop the first distance-generating function for the strategy spaces of sequential games that has no dependence on the branching factor of the player.
Multiplicative Pacing Equilibria in Auction Markets
Although we show that computing either a social-welfare-maximizing or a revenue-maximizing pacing equilibrium is NP-hard, we present a mixed-integer program (MIP) that can be used to find equilibria optimizing several relevant objectives.
Computer Science and Game Theory
Regret Minimization in Behaviorally-Constrained Zero-Sum Games
We use an instantiation of the CFR framework to develop algorithms for solving behaviorally-constrained (and, as a special case, perturbed in the Selten sense) extensive-form games, which allows us to compute approximate Nash equilibrium refinements.
Robust Stackelberg Equilibria in Extensive-Form Games and Extension to Limited Lookahead
We then extend the program to the robust setting for Stackelberg equilibrium under unlimited and under limited lookahead by the opponent.
Online Convex Optimization for Sequential Decision Processes and Extensive-Form Games
Experiments show that our framework leads to algorithms that scale at a rate comparable to the fastest variants of counterfactual regret minimization for computing Nash equilibrium, and therefore our approach leads to the first algorithm for computing quantal response equilibria in extremely large games.
Solving Large Sequential Games with the Excessive Gap Technique
We present, to our knowledge, the first GPU implementation of a first-order method for extensive-form games.
Regret Circuits: Composability of Regret Minimizers
We show that local regret minimizers for the simpler sets can be combined with additional regret minimizers into an aggregate regret minimizer for the composite set.
A Unified Framework for Extensive-Form Game Abstraction with Bounds
In this paper we present a unified framework for analyzing abstractions that can express all types of abstractions and solution concepts used in prior papers with performance guarantees---while maintaining comparable bounds on abstraction quality.
Computing large market equilibria using abstractions
Computing market equilibria is an important practical problem for market design, for example in fair division of items.
Stable-Predictive Optimistic Counterfactual Regret Minimization
The CFR framework has been a powerful tool for solving large-scale extensive-form games in practice.
Limited Lookahead in Imperfect-Information Games
We characterize the hardness of finding a Nash equilibrium or an optimal commitment strategy for either player, showing that in some of these variations the problem can be solved in polynomial time while in others it is PPAD-hard, NP-hard, or inapproximable.
Increasing Iterate Averaging for Solving Saddle-Point Problems
In particular, the increasing averages consistently outperform the uniform averages in all test problems by orders of magnitude.
Robust Multi-agent Counterfactual Prediction
We consider the problem of using logged data to make predictions about what would happen if we changed the `rules of the game' in a multi-agent system.
Fair Division Without Disparate Impact
We consider the problem of dividing items between individuals in a way that is fair both in the sense of distributional fairness and in the sense of not having disparate impact across protected classes.
Optimistic Regret Minimization for Extensive-Form Games via Dilated Distance-Generating Functions
Our algorithms provably converge at a rate of $T^{-1}$, which is superior to prior counterfactual regret minimization algorithms.
Stochastic Regret Minimization in Extensive-Form Games
Our framework allows us to instantiate several new stochastic methods for solving sequential games.
Scalable First-Order Methods for Robust MDPs
Our framework is also the first one to solve robust MDPs with $s$-rectangular KL uncertainty sets.
Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions
We apply our methods to study teams of artificial RL agents as well as real world teams from professional sports.
Dominant Resource Fairness with Meta-Types
Inspired by the recent COVID-19 pandemic, we study a generalization of the multi-resource allocation problem with heterogeneous demands and Leontief utilities.
Faster Game Solving via Predictive Blackwell Approachability: Connecting Regret Matching and Mirror Descent
In spite of this prevalence, the regret matching (RM) and regret matching+ (RM+) algorithms have been preferred in the practice of solving large-scale games (as the local regret minimizers within the counterfactual regret minimization framework).
Conic Blackwell Algorithm: Parameter-Free Convex-Concave Saddle-Point Solving
We develop new parameter-free and scale-free algorithms for solving convex-concave saddle-point problems.
Better Regularization for Sequential Decision Spaces: Fast Convergence Rates for Nash, Correlated, and Team Equilibria
The scaled extension operator is a way to recursively construct convex sets, which generalizes the decision polytope of extensive-form games, as well as the convex polytopes corresponding to correlated and team equilibria.
Last-iterate Convergence in Extensive-Form Games
Inspired by recent advances on last-iterate convergence of optimistic algorithms in zero-sum normal-form games, we study this phenomenon in sequential games, and provide a comprehensive study of last-iterate convergence for zero-sum extensive-form games with perfect recall (EFGs), using various optimistic regret-minimization algorithms over treeplexes.
Matching Algorithms for Blood Donation
Using the Facebook Blood Donation tool, we conduct the first large-scale algorithmic matching of blood donors with donation opportunities.
Faster No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium
A recent emerging trend in the literature on learning in games has been concerned with providing accelerated learning dynamics for correlated and coarse correlated equilibria in normal-form games.
Kernelized Multiplicative Weights for 0/1-Polyhedral Games: Bridging the Gap Between Learning in Extensive-Form and Normal-Form Games
In this paper we show that the Optimistic Multiplicative Weights Update (OMWU) algorithm -- the premier learning algorithm for NFGs -- can be simulated on the normal-form equivalent of an EFG in linear time per iteration in the game tree size using a kernel trick.
Single-Leg Revenue Management with Advice
Moreover, we provide an online algorithm that always achieves performance on this Pareto frontier.
Solving optimization problems with Blackwell approachability
We introduce the Conic Blackwell Algorithm$^+$ (CBA$^+$) regret minimizer, a new parameter- and scale-free regret minimizer for general convex sets.
Best of Many Worlds Guarantees for Online Learning with Knapsacks
We study online learning problems in which a decision maker wants to maximize their expected reward without violating a finite set of $m$ resource constraints.
Uncoupled Learning Dynamics with $O(\log T)$ Swap Regret in Multiplayer Games
In this paper we establish efficient and \emph{uncoupled} learning dynamics so that, when employed by all players in a general-sum multiplayer game, the \emph{swap regret} of each player after $T$ repetitions of the game is bounded by $O(\log T)$, improving over the prior best bounds of $O(\log^4 (T))$.
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum Games
This work studies an algorithm, which we call magnetic mirror descent, that is inspired by mirror descent and the non-Euclidean proximal gradient algorithm.
Near-Optimal No-Regret Learning Dynamics for General Convex Games
In this paper, we answer this in the positive by establishing the first uncoupled learning algorithm with $O(\log T)$ per-player regret in general \emph{convex games}, that is, games with concave utility functions supported on arbitrary convex and compact strategy sets.
Online Resource Allocation under Horizon Uncertainty
We go on to give a fast algorithm for computing a schedule of target consumption rates that leads to near-optimal performance in the unknown-horizon setting.
Computing the optimal distributionally-robust strategy to commit to
In the security setting, the goal is for the leader to compute an optimal strategy to commit to, in order to protect some asset.
Optimal Efficiency-Envy Trade-Off via Optimal Transport
We consider the problem of allocating a distribution of items to $n$ recipients where each recipient has to be allocated a fixed, prespecified fraction of all items, while ensuring that each recipient does not experience too much envy.
Statistical Inference for Fisher Market Equilibrium
In resource allocation, it is crucial to quantify the variability of the resource received by the agents (such as blood banks and food banks) in addition to fairness and efficiency properties of the systems.
Online Learning under Budget and ROI Constraints via Weak Adaptivity
Finally, we show how to instantiate the framework to optimally bid in various mechanisms of practical relevance, such as first- and second-price auctions.
Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games
Algorithms based on regret matching, specifically regret matching$^+$ (RM$^+$), and its variants are the most popular approaches for solving large-scale two-player zero-sum games in practice.
