Tootfinder

Worst-Case Complexity of High-Order Algorithms for Pareto-Front Reconstruction
Andrea Cristofari, Marianna De Santis, Stefano Lucidi, Giampaolo Liuzzi
https://arxiv.org/abs/2506.11929

Worst-Case Complexity of High-Order Algorithms for Pareto-Front Reconstruction
In this paper, we are concerned with a worst-case complexity analysis of a-posteriori algorithms for unconstrained multiobjective optimization. Specifically, we propose an algorithmic framework that generates sets of points by means of $p$th-order models regularized with a power $p+1$ of the norm of the step. Through a tailored search procedure, several trial points are generated at each iteration and they can be added to the current set if a decrease is obtained for at least one objective func…

What Pareto-Efficiency Adjustments Cannot Fix
Josue Ortega, Gabriel Ziegler, R. Pablo Arribillaga, Geng Zhao
https://arxiv.org/abs/2506.11660 https://

What Pareto-Efficiency Adjustments Cannot Fix
The Deferred Acceptance (DA) algorithm is stable and strategy-proof, but can produce outcomes that are Pareto-inefficient for students, and thus several alternative mechanisms have been proposed to correct this inefficiency. However, we show that these mechanisms cannot correct DA's rank-inefficiency and inequality, because these shortcomings can arise even in cases where DA is Pareto-efficient. We also examine students' segregation in settings with advantaged and marginalized students. We pr…

Environmental extreme risk modeling via sub-sampling block maxima
Tuoyuan Cheng, Xiao Peng, Achmad Choiruddin, Xiaogang He, Kan Chen
https://arxiv.org/abs/2506.14556

Environmental extreme risk modeling via sub-sampling block maxima
This paper introduces a novel sub-sampling block maxima technique to model and characterize environmental extreme risks. We examine the relationships between block size and block maxima statistics derived from the Gaussian and generalized Pareto distributions. We introduce a weighted least square estimator for extreme value index (EVI) and evaluate its performance using simulated auto-correlated data. We employ the second moment of block maxima for plateau finding in EVI and extremal index (EI)…

Voter model on heterogeneous directed networks
Luca Avena, Federico Capannoli, Rajat Subhra Hazra, Diego Garlaschelli
https://arxiv.org/abs/2506.12169 http…

Voter model on heterogeneous directed networks
We investigate the consensus dynamics of the voter model on large random graphs with heterogeneous and directed features, focusing in particular on networks with power-law degree distributions. By extending recent results on sparse directed graphs, we derive exact first-order asymptotics for the expected consensus time in directed configuration models with i.i.d. Pareto-distributed in- and out-degrees. For any tail exponent α>0, we derive the mean consensus time scaling depending on the networ…

Constrained Pareto Set Identification with Bandit Feedback
Cyrille Kone, Emilie Kaufmann, Laura Richert
https://arxiv.org/abs/2506.08127 https://

Constrained Pareto Set Identification with Bandit Feedback
In this paper, we address the problem of identifying the Pareto Set under feasibility constraints in a multivariate bandit setting. Specifically, given a $K$-armed bandit with unknown means $μ_1, \dots, μ_K \in \mathbb{R}^d$, the goal is to identify the set of arms whose mean is not uniformly worse than that of another arm (i.e., not smaller for all objectives), while satisfying some known set of linear constraints, expressing, for example, some minimal performance on each objective. Our focu…

This https://arxiv.org/abs/2505.11864 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Learning Pareto-Optimal Rewards from Noisy Preferences: A Framework for Multi-Objective Inverse Reinforcement Learning
As generative agents become increasingly capable, alignment of their behavior with complex human values remains a fundamental challenge. Existing approaches often simplify human intent through reduction to a scalar reward, overlooking the multi-faceted nature of human feedback. In this work, we introduce a theoretical framework for preference-based Multi-Objective Inverse Reinforcement Learning (MO-IRL), where human preferences are modeled as latent vector-valued reward functions. We formalize …

A New Lifetime Distribution: Exponentiated Exponential-Pareto-HalfNormal Mixture Model for Biomedical Applications
Oriyomi Ahmad Hassan, Aisha Tunrayo Maradesa, Abdulazeez Toyosi Alabi, Oyejide Surajudeen Salam, Ajani Busari, Akinwale Victor Famotire, Habeeb Abiodun Afolabi, Solomon Adeleke, Abayomi Ayodele Akomolafe
https://arx…

A New Lifetime Distribution: Exponentiated Exponential-Pareto-HalfNormal Mixture Model for Biomedical Applications
This study introduces the Exponentiated-Exponential-Pareto-Half Normal Mixture Distribution (EEPHND), a novel hybrid model developed to overcome the limitations of classical distributions in modeling complex real-world data. By compounding the Exponentiated-Exponential-Pareto (EEP) and Half-Normal distributions through a mixture mechanism, EEPHND effectively captures both early-time symmetry and long-tail behavior, features which are commonly observed in survival and reliability data. The model…

This https://arxiv.org/abs/2406.17240 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLO_…

Pareto Fronts for Compositionally Solving String Diagrams of Parity Games
Open parity games are proposed as a compositional extension of parity games with algebraic operations, forming string diagrams of parity games. A potential application of string diagrams of parity games is to describe a large parity game with a given compositional structure and solve it efficiently as a divide-and-conquer algorithm by exploiting its compositional structure. Building on our recent progress in open Markov decision processes, we introduce Pareto fronts of open parity games, offeri…

This https://arxiv.org/abs/2505.01323 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Proven Approximation Guarantees in Multi-Objective Optimization: SPEA2 Beats NSGA-II
Together with the NSGA-II and SMS-EMOA, the strength Pareto evolutionary algorithm 2 (SPEA2) is one of the most prominent dominance-based multi-objective evolutionary algorithms (MOEAs). Different from the NSGA-II, it does not employ the crowding distance (essentially the distance to neighboring solutions) to compare pairwise non-dominating solutions but a complex system of $σ$-distances that builds on the distances to all other solutions. In this work, we give a first mathematical proof showi…

Beyond the Buzz: A Pragmatic Take on Inference Disaggregation
Tiyasa Mitra, Ritika Borkar, Nidhi Bhatia, Ramon Matas, Shivam Raj, Dheevatsa Mudigere, Ritchie Zhao, Maximilian Golub, Arpan Dutta, Sailaja Madduri, Dharmesh Jani, Brian Pharris, Bita Darvish Rouhani
https://arxiv.org/abs/2506.05508

Beyond the Buzz: A Pragmatic Take on Inference Disaggregation
As inference scales to multi-node deployments, disaggregation - splitting inference into distinct phases - offers a promising path to improving the throughput-interactivity Pareto frontier. Despite growing enthusiasm and a surge of open-source efforts, practical deployment of disaggregated serving remains limited due to the complexity of the optimization search space and system-level coordination. In this paper, we present the first systematic study of disaggregated inference at scale, evaluati…

This https://arxiv.org/abs/2505.03768 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

From Concept to Measurement: A Survey of How the Blockchain Trilemma Can Be Analyzed
To meet non-functional requirements, practitioners must identify Pareto-optimal configurations of the degree of decentralization, scalability, and security of blockchain systems. Maximizing all of these subconcepts is, however, impossible due to the trade-offs highlighted by the blockchain trilemma. We reviewed analysis approaches to identify constructs and their operationalization through metrics for analyzing the blockchain trilemma subconcepts and to assess the applicability of the operation…

Constraining Nuclear Mass Models Using r-process Observables with Multi-objective Optimization
Mengke Li, Matthew Mumpower, Nicole Vassh, William Samuel Porter, Rebecca Surman
https://arxiv.org/abs/2506.06464

Constraining Nuclear Mass Models Using r-process Observables with Multi-objective Optimization
Predicting nuclear masses is a longstanding challenge. One path forward is machine learning (ML) which trains on experimental data, but can suffer large errors when extrapolating toward neutron-rich species. In nature, such masses shape observables for the rapid neutron capture process (r-process), which in principle could inform ML models. Here we introduce a multi-objective optimization approach using the Pareto Front algorithm. We show that this technique, capable of identifying models which…

Characterization based Goodness-of-Fit for Generalized Pareto Distribution: A Blend of Stein's Identity and Dynamic Survival Extropy
Gaurav Kandpal, Nitin Gupta
https://arxiv.org/abs/2506.01473

Characterization based Goodness-of-Fit for Generalized Pareto Distribution: A Blend of Stein's Identity and Dynamic Survival Extropy
This paper proposes a goodness of fit test for the generalized Pareto distribution (GPD). Firstly, we provide two characterizations of GPD based on Stein's identity and dynamic survival extropy. These characterizations are used to test GPD separately for the positive and negative shape parameter cases. A Monte Carlo simulation is conducted to provide the critical values and power of the proposed test against a good number of alternatives. Our test is simple to use and it has asymptotic normalit…

I have just submitted a grant application to the Swedish Science Council with the title "Functional multi-objective optimization under uncertainty".
https://patrikja.owlstown.net/posts/3782
If granted it would pay for a PhD student supervision time.
Some prelimina…

This https://arxiv.org/abs/2501.19277 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

On Pareto Optimality for the Multinomial Logistic Bandit
We provide a new online learning algorithm for tackling the Multinomial Logit Bandit (MNL-Bandit) problem. Despite the challenges posed by the combinatorial nature of the MNL model, we develop a novel Upper Confidence Bound (UCB)-based method that achieves Pareto optimality by balancing regret minimization and estimation error of the assortment revenues and the MNL parameters. We develop theoretical guarantees characterizing the tradeoff between regret and estimation error for the MNL-Bandit pr…

This https://arxiv.org/abs/2312.10290 has been replaced.
link: https://scholar.google.com/scholar?q=a

Runtime Analysis of the SMS-EMOA for Many-Objective Optimization
This paper conducts the first rigorous runtime analysis of the SMS-EMOA for many-objective optimization. To this aim, we first propose a many-objective counterpart of the bi-objective OJZJ benchmark. We prove that SMS-EMOA computes the full Pareto front of this benchmark in an expected number of $O(μM n^k)$ iterations, where $n$ denotes the problem size (length of the bit-string representation), $k$ the gap size (a difficulty parameter of the problem), $M=(2n/m-2k+3)^{m/2}$ the size of the Par…

This https://arxiv.org/abs/2505.07501 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

The Complexity of Pure Strategy Relevant Equilibria in Concurrent Games
We study rational synthesis problems for concurrent games with $ω$-regular objectives. Our model of rationality considers only pure strategy Nash equilibria that satisfy either a social welfare or Pareto optimality condition with respect to an $ω$-regular objective for each agent. This extends earlier work on equilibria in concurrent games, without consideration about their quality. Our results show that the existence of Nash equilibria satisfying social welfare conditions can be computed as …

Non-linear Multi-objective Optimization with Probabilistic Branch and Bound
Hao Huang, Zelda B. Zabinsky
https://arxiv.org/abs/2506.04554 https://

Non-linear Multi-objective Optimization with Probabilistic Branch and Bound
A multiple objective simulation optimization algorithm named Multiple Objective Probabilistic Branch and Bound with Single Observation (MOPBnB(so)) is presented for approximating the Pareto optimal set and the associated efficient frontier for stochastic multi-objective optimization problems. MOPBnB(so) evaluates a noisy function exactly once at any solution and uses neighboring solutions to estimate the objective functions, in contrast to a variant that uses multiple replications at a solution…

This https://arxiv.org/abs/2407.13958 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Fast and flexible inference for spatial extremes
Statistical modelling of spatial extreme events has gained increasing attention over the last few decades with max-stable processes, and more recently $r$-Pareto processes, becoming the reference tools for the statistical analysis of asymptotically dependent data. Although inference for r-Pareto processes is easier than for max-stable processes, there remain major hurdles for their application to high dimensional datasets within a reasonable timeframe. In addition, both approaches have almost e…

This https://arxiv.org/abs/2502.13697 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Linear programming for finite-horizon vector-valued Markov decision processes
We propose a vector linear programming formulation for a non-stationary, finite-horizon Markov decision process with vector-valued rewards. Pareto efficient policies are shown to correspond to efficient solutions of the linear program, and vector linear programming theory allows us to fully characterize deterministic efficient policies. An algorithm for enumerating all efficient deterministic policies is presented then tested numerically in an engineering application.

This https://arxiv.org/abs/2411.09097 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

On the Selection Stability of Stability Selection and Its Applications
Stability selection is a widely adopted resampling-based framework for high-dimensional variable selection. This paper seeks to broaden the use of an established stability estimator to evaluate the overall stability of the stability selection results, moving beyond single-variable analysis. We suggest that the stability estimator offers two advantages: it can serve as a reference to reflect the robustness of the results obtained, and it can help identify a Pareto optimal regularization value to…

Factorized Tail Volatility Model: Augmenting Excess-over-Threshold Method for High-Dimensional Hevay-Tailed Data
Yifan Hu, Yanxi Hou
https://arxiv.org/abs/2506.00840

Factorized Tail Volatility Model: Augmenting Excess-over-Threshold Method for High-Dimensional Hevay-Tailed Data
Ecess-over-Threshold method is a crucial technique in extreme value analysis, which approximately models larger observations over a threshold using a Generalized Pareto Distribution. This paper presents a comprehensive framework for analyzing tail risk in high-dimensional data by introducing the Factorized Tail Volatility Model (FTVM) and integrating it with central quantile models through the EoT method. This integrated framework is termed the FTVM-EoT method. In this framework, a quantile-rel…