arXiv AI
Aug 5
EFX Allocation In (Multi)Hypergraphs
arXiv:2608. 03171v1 Announce Type: cross Abstract: We study fair allocations of indivisible goods among agents with heterogeneous monotone valuations.
Pushing the Frontier on Approximate EFX Allocations
arXiv:2406. 12413v3 Announce Type: replace-cross Abstract: We study the problem of allocating a set of indivisible goods to a set of agents with additive valuation functions, aiming to achieve approximate envy-freeness up to any good ($\alpha$-EFX).
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arXiv AI
Jul 28
Fair Division with Strictly Increasing Valuations: A Tight Threshold for Two-Agent EF1 and PO
arXiv:2607. 23367v1 Announce Type: cross Abstract: We study whether strictly positive marginal values restore the compatibility of envy-freeness up to one good (EF1) and Pareto optimality (PO) for indivisible goods.
arXiv AI
Jul 28
Online Fair Division with Budget Constraints
arXiv:2607. 23310v1 Announce Type: cross Abstract: We study an online variant of discrete fair division under generalized assignment budget constraints.
arXiv AI
Jul 28
Formalizing Flag Algebras in Lean
arXiv:2607. 23500v1 Announce Type: cross Abstract: Razborov's flag algebra method is a powerful tool for proving asymptotic inequalities in extremal graph theory, often reducing the task to finding a finite certificate by semidefinite programming.
arXiv Machine Learning
Aug 7
Computationally Efficient Collaborative Communication Via Regularity-Based Coarsening
arXiv:2608. 05327v1 Announce Type: cross Abstract: Our results show that the existence of a short high-utility protocol already suffices for efficient communication.
Hugging Face Trending Papers
Aug 5
Computationally Efficient Collaborative Communication Via Regularity-Based Coarsening
Our results show that the existence of a short high-utility protocol already suffices for efficient communication. In particular, in a game with $n$ possible observations and $m$ actions: (1) For any achievable target utility $α$, we give an algorithm with $\mathrm{poly}(n, m, 1/ε)$ runtime that designs a protocol achieving utility at least $α-ε$ using only $2^{\mathcal O(CC_α(G))}/ε^2$ bits of communication.
arXiv AI
Aug 11
Dynamic Coalition Formation and Communication Pricing in Skill-Based Agentic AI Systems
arXiv:2608. 07532v1 Announce Type: new Abstract: Modern agentic AI systems combine multiple large language model agents with heterogeneous skills, yet most architectures either fix communication in advance or allow full broadcast.
arXiv Machine Learning
Jun 30
Learning to Bid in Discriminatory Auctions with Budget Constraints
arXiv:2606. 29252v1 Announce Type: new Abstract: We study repeated bidding in multi-unit discriminatory (pay-as-bid) auctions for a single bidder with per-round utility equal to value minus $\alpha$ times payment, where $\alpha\in[0,1]$ is a cost-of-capital parameter.
arXiv Machine Learning
Jul 30
Top-$k$ Pareto Bandits: Hypervolume Regret for Multi-Objective Slate Selection
arXiv:2607. 26273v1 Announce Type: new Abstract: We consider a stochastic multi-objective bandit problem where, at each round, the agent selects a slate of $k$ arms and observes their $d$-dimensional reward vectors under semi-bandit feedback.
arXiv Machine Learning
Jul 14
Precedence-Constrained Decision Trees and Coverings
arXiv:2602. 21312v4 Announce Type: replace-cross Abstract: This work considers a number of optimization problems and reductive relations between them.
arXiv AI
Jul 21
Content Creation with Spillovers: An Incentive Design Approach
arXiv:2603. 14372v2 Announce Type: replace Abstract: The rise of AI amplifies the economic phenomenon of \emph{positive spillovers}: when creators contribute content that can be reused and adapted by LLMs, one creator's effort may improve the content quality of others through recombination.
Hugging Face Trending Papers
Jul 2
Tight Lower Bounds for the Multi-Secretary Problem via Bellman Certificates
This paper studies additive regret in the multi-secretary problem, defined as the gap between the expected offline prophet reward and the reward of the best online policy. Prior work established \(O(\log T)\) regret for bounded-density distributions with connected support and \(O((\log T)^2)\) upper bounds for bounded-density distributions with support gaps.