This paper studies the problem of aggregating calibrated Bayesian experts into a new calibrated expert.

This paper investigates the convergence and stability of equilibria in bimatrix two-player games using the optimistic exponential weights method, allowing step sizes to differ.

The paper proves a stochastic comparison for Gaussian maxima, resolving the Weak Simplex Conjecture and proving the Simplex Mean Width Conjecture.

The paper presents an adaptive scaling algorithm with a competitive ratio of 1.373 for incremental submodular maximization under increasing cardinality constraint, improving upon the previous best res…

The paper introduces Context-Dependent Argumentation Frameworks (CDAFs) to model how an agent strategically manipulates the success of arguments by choosing the external evaluation context.

The paper proves XNLP-hardness of Directed Edge Geography and Undirected Edge Geography when parameterized by pathwidth, and shows their fixed-parameter tractability when parameterized by treewidth an…

The paper introduces FootsiesGym, an open-source environment for learning in a two-player, zero-sum, imperfect-information game, providing a vectorized simulator for efficient analysis.

This paper studies a variant of the quantum XOR game model, called tilted XOR games, and shows that approximating their quantum value to constant precision is RE-complete.

The paper provides tight bounds for the OR-rank and an upper bound for the SUM-rank of the unique disjointness matrix.

This paper proposes Primitive-Guided Tree Search (PGTS), a hybrid framework for computing Nash equilibrium policies in multi-agent Pursuit-Evasion games by integrating offline exact computation with o…

The paper proposes D-BOS, a novel differentiable method that shapes opponent behavior by directly manipulating the opponent's inferred belief state, outperforming existing techniques in multi-agent ga…

This paper studies ranking and aggregation under Kendall tau distance with matroid or flag matroid constraints on prefixes.

The paper introduces Global PSRO, a novel deep reinforcement learning framework that efficiently approximates Nash equilibria in large two-player zero-sum games by intelligently expanding the strategy…

This paper improves the theoretical bounds for estimating discrete probability distributions using the $\ell_\infty$ norm, resolving several open questions in the field.

This paper presents new polynomial-time algorithms for determining the winner of the unbiased triangle game on the edge set of general graphs using the edge-triangle incidence graph.

The study extends cooperative bias testing across diverse, next-generation LLMs, finding that provider identity is a stronger predictor of cooperative equilibrium than model generation, and that noise…