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Related papers: On Rational Inattention with Arbitrary Choice Sets

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In the context of strategic games, we provide an axiomatic proof of the statement Common knowledge of rationality implies that the players will choose only strategies that survive the iterated elimination of strictly dominated strategies.…

Computer Science and Game Theory · Computer Science 2010-06-28 Jonathan A. Zvesper , Krzysztof R. Apt

We study multi-marginal optimal transport problems from a probabilistic graphical model perspective. We point out an elegant connection between the two when the underlying cost for optimal transport allows a graph structure. In particular,…

Optimization and Control · Mathematics 2020-06-26 Isabel Haasler , Rahul Singh , Qinsheng Zhang , Johan Karlsson , Yongxin Chen

We consider a path-planning scenario for a mobile robot traveling in a configuration space with obstacles under the presence of stochastic disturbances. A novel path length metric is proposed on the uncertain configuration space and then…

Robotics · Computer Science 2020-03-02 Jeb Stefan , Ali Reza Pedram , Riku Funada , Takashi Tanaka

In this paper, we address the problem of estimating transport surplus (a.k.a. matching affinity) in high dimensional optimal transport problems. Classical optimal transport theory specifies the matching affinity and determines the optimal…

Methodology · Statistics 2017-01-02 Arnaud Dupuy , Alfred Galichon , Yifei Sun

In this paper, we reveal the relationship between entropy rate and the congestion in complex network and solve it analytically for special cases. Finding maximizing entropy rate will lead to an improvement of traffic efficiency, we propose…

Physics and Society · Physics 2017-09-15 Yuhang Fan , Hanyuan Liu , Shibo He

We study the problem of safe and intention-aware robot navigation in dense and interactive crowds. Most previous reinforcement learning (RL) based methods fail to consider different types of interactions among all agents or ignore the…

Distributionally robust optimization tackles out-of-sample issues like overfitting and distribution shifts by adopting an adversarial approach over a range of possible data distributions, known as the ambiguity set. To balance conservatism…

Machine Learning · Computer Science 2025-10-02 Ahmad-Reza Ehyaei , Golnoosh Farnadi , Samira Samadi

We consider a novel application of inverse reinforcement learning with behavioral economics constraints to model, learn and predict the commenting behavior of YouTube viewers. Each group of users is modeled as a rationally inattentive…

Machine Learning · Computer Science 2020-04-07 William Hoiles , Vikram Krishnamurthy , Kunal Pattanayak

Emerging edge computing paradigms enable heterogeneous devices to collaborate on complex computation applications. However, for arbitrary heterogeneous edge networks, delay-optimal forwarding and computation offloading remains an open…

Networking and Internet Architecture · Computer Science 2025-06-17 Jinkun Zhang , Yuezhou Liu , Edmund Yeh

We consider a slotted wireless network in an infrastructure setup with a base station (or an access point) and N users. The wireless channel gain between the base station and the users is assumed to be i.i.d., and the base station seeks to…

Information Theory · Computer Science 2013-08-01 Venkatesh Ramaiyan

Continuity of the value of the martingale optimal transport problem on the real line w.r.t. its marginals was recently established in Backhoff-Veraguas and Pammer [2] and Wiesel [21]. We present a new perspective of this result using the…

Probability · Mathematics 2021-04-23 Ariel Neufeld , Julian Sester

Inverse optimal control can be used to characterize behavior in sequential decision-making tasks. Most existing work, however, is limited to fully observable or linear systems, or requires the action signals to be known. Here, we introduce…

Machine Learning · Computer Science 2023-10-31 Dominik Straub , Matthias Schultheis , Heinz Koeppl , Constantin A. Rothkopf

Selecting input features of top relevance has become a popular method for building self-explaining models. In this work, we extend this selective rationalization approach to text matching, where the goal is to jointly select and align text…

Machine Learning · Computer Science 2020-05-28 Kyle Swanson , Lili Yu , Tao Lei

Reliable automated driving technology is challenged by various sources of uncertainties, in particular, behavioral uncertainties of traffic agents. It is common for traffic agents to have intentions that are unknown to others, leaving an…

Robotics · Computer Science 2025-06-10 David Isele , Alexandre Miranda Anon , Faizan M. Tariq , Goro Yeh , Avinash Singh , Sangjae Bae

Traditional maximum entropy and sparsity-based algorithms for analytic continuation often suffer from the ill-posed kernel matrix or demand tremendous computation time for parameter tuning. Here we propose a neural network method by convex…

Machine Learning · Computer Science 2022-02-07 Dongchen Huang , Yi-feng Yang

In this paper, we establish a regularity theory for the optimal transport problem when the target is composed of two disjoint convex domains. This is an important model in which singularities arise. Even though the singular set does not…

Analysis of PDEs · Mathematics 2025-07-22 Shibing Chen , Jiakun Liu

Compared with only pursuing recommendation accuracy, the explainability of a recommendation model has drawn more attention in recent years. Many graph-based recommendations resort to informative paths with the attention mechanism for the…

Information Retrieval · Computer Science 2024-03-05 Yicong Li , Xiangguo Sun , Hongxu Chen , Sixiao Zhang , Yu Yang , Guandong Xu

We study the notion of debiasability for cost functions arising in optimal transport. We call a symmetric cost function $c:\mathscr{X}\times\mathscr{X}\to\mathbb{R}\cup\{+\infty\}$ debiasable if it satisfies $c(x,y)\ge…

Optimization and Control · Mathematics 2026-04-23 Pierre-Cyril Aubin-Frankowski , Virginie Ehrlacher , Gabriele Todeschi

Attention is the crucial cognitive ability that limits and selects what information we observe. Previous work by Bolander et al. (2016) proposes a model of attention based on dynamic epistemic logic (DEL) where agents are either fully…

Artificial Intelligence · Computer Science 2023-05-19 Gaia Belardinelli , Thomas Bolander

This paper connects discrete optimal transport to a certain class of multi-objective optimization problems. In both settings, the decision variables can be organized into a matrix. In the multi-objective problem, the notion of Pareto…

Optimization and Control · Mathematics 2017-12-04 Johannes M. Schumacher