Tootfinder

Multi-Agent Inverse Reinforcement Learning for Identifying Pareto-Efficient Coordination -- A Distributionally Robust Approach
Luke Snow, Vikram Krishnamurthy
https://arxiv.org/abs/2509.08956

Multi-Agent Inverse Reinforcement Learning for Identifying Pareto-Efficient Coordination -- A Distributionally Robust Approach
Multi-agent inverse reinforcement learning (IRL) aims to identify Pareto-efficient behavior in a multi-agent system, and reconstruct utility functions of the individual agents. Motivated by the problem of detecting UAV coordination, how can we construct a statistical detector for Pareto-efficient behavior given noisy measurements of the decisions of a multi-agent system? This paper approaches this IRL problem by deriving necessary and sufficient conditions for a dataset of multi-agent system dy…

Proportional and Pareto-Optimal Allocation of Chores with Subsidy
Jugal Garg, Eklavya Sharma, Xiaowei Wu
https://arxiv.org/abs/2510.10335 https://arxiv.org…

Proportional and Pareto-Optimal Allocation of Chores with Subsidy
We consider the problem of allocating $m$ indivisible chores among $n$ agents with possibly different weights, aiming for a solution that is both fair and efficient. Specifically, we focus on the classic fairness notion of proportionality and efficiency notion of Pareto-optimality. Since proportional allocations may not always exist in this setting, we allow the use of subsidies (monetary compensation to agents) to ensure agents are proportionally-satisfied, and aim to minimize the total subsid…

Using utility graphs to search for Pareto-optimal outcomes in complex, interdependent issue negotiations
Valentin Robu, Mark Klein
https://arxiv.org/abs/2509.10885 https://

Using utility graphs to search for Pareto-optimal outcomes in complex, interdependent issue negotiations
This paper studies how utility graphs decomposition algorithms can be used to effectively search for Pareto-efficient outcomes in complex automated negotiation. We propose a number of algorithms that can efficiently handle high-dimensional utility graphs, and test them on a variety of utility graph topologies, generated based on state of the art methods for analysing complex graphs. We show that we can achieve exponential speed-up, for many structures, even for very large utility graphs. To our…

Causal-Guided Dimension Reduction for Efficient Pareto Optimization
Dinithi Jayasuriya, Divake Kumar, Sureshkumar Senthilkumar, Devashri Naik, Nastaran Darabi, Amit Ranjan Trivedi
https://arxiv.org/abs/2510.09941

Causal-Guided Dimension Reduction for Efficient Pareto Optimization
Multi-objective optimization of analog circuits is hindered by high-dimensional parameter spaces, strong feedback couplings, and expensive transistor-level simulations. Evolutionary algorithms such as Non-dominated Sorting Genetic Algorithm II (NSGA-II) are widely used but treat all parameters equally, thereby wasting effort on variables with little impact on performance, which limits their scalability. We introduce CaDRO, a causal-guided dimensionality reduction framework that embeds causal di…

When Can Communication Lead to Efficiency?
Itai Arieli, Yakov Babichenko, Atulya Jain, Rann Smorodinsky
https://arxiv.org/abs/2510.12508 https://arxiv.org/…

When Can Communication Lead to Efficiency?
We study games with incomplete information and characterize when a feasible outcome is Pareto efficient. We show that any outcome with excessive randomization over actions is inefficient. Generically, efficiency requires that the total number of actions taken across states be strictly less than the sum of the number of players and states. We then examine the efficiency of equilibrium outcomes in communication models. Generically, a cheap talk outcome is efficient only if it is pure. When the se…

FraPPE: Fast and Efficient Preference-based Pure Exploration
Udvas Das, Apurv Shukla, Debabrota Basu
https://arxiv.org/abs/2508.16487 https://arxiv.org/pdf…

FraPPE: Fast and Efficient Preference-based Pure Exploration
Preference-based Pure Exploration (PrePEx) aims to identify with a given confidence level the set of Pareto optimal arms in a vector-valued (aka multi-objective) bandit, where the reward vectors are ordered via a (given) preference cone $\mathcal{C}$. Though PrePEx and its variants are well-studied, there does not exist a computationally efficient algorithm that can optimally track the existing lower bound for arbitrary preference cones. We successfully fill this gap by efficiently solving the …

Crosslisted article(s) found for eess.SP. https://arxiv.org/list/eess.SP/new
[1/1]:
- Multi-Agent Inverse Reinforcement Learning for Identifying Pareto-Efficient Coordination -- A Dis...
Luke Snow, Vikram Krishnamurthy

A Possibility Frontier Approach to Diverse Talent Selection
Neil Natarajan, Kadeem Noray
https://arxiv.org/abs/2510.06119 https://arxiv.org/pdf/2510.06119

A Possibility Frontier Approach to Diverse Talent Selection
Organizations (e.g., talent investment programs, schools, firms) are perennially interested in selecting cohorts of talented people. And organizations are increasingly interested in selecting diverse cohorts. Except in trivial cases, measuring the tradeoff between cohort diversity and talent is computationally difficult. Thus, organizations are presently unable to make Pareto-efficient decisions about these tradeoffs. We introduce an algorithm that approximates upper bounds on cohort talent and…

Achieving Pareto Optimality in Games via Single-bit Feedback
Seref Taha Kiremitci, Ahmed Said Donmez, Muhammed O. Sayin
https://arxiv.org/abs/2509.25921 https://

Achieving Pareto Optimality in Games via Single-bit Feedback
Efficient coordination in multi-agent systems often incurs high communication overhead or slow convergence rates, making scalable welfare optimization difficult. We propose Single-Bit Coordination Dynamics for Pareto-Efficient Outcomes (SBC-PE), a decentralized learning algorithm requiring only a single-bit satisfaction signal per agent each round. Despite this extreme efficiency, SBC-PE guarantees convergence to the exact optimal solution in arbitrary finite games. We establish explicit regret…

Biobjective optimization with M-convex functions
Ellen H. Fukuda, Satoru Iwata, Itsuki Nakagawa
https://arxiv.org/abs/2507.23423 https://arxiv.org/pdf/2507…

Biobjective optimization with M-convex functions
In this paper, we deal with two ingredients that, as far as we know, have not been combined until now: multiobjective optimization and discrete convex analysis. First, we show that the entire Pareto optimal value set can be obtained in polynomial time for biobjective optimization problems with discrete convex functions, in particular, involving an M$^\natural$-convex function and a linear function with binary coefficients. We also observe that a more efficient algorithm can be obtained in the s…

Proximately Envy-Free and Efficient Allocation of Mixed Manna
Siddharth Barman, Paritosh Verma
https://arxiv.org/abs/2509.18673 https://arxiv.org/pdf/2509.…