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3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Multitask Reinforcement Learning for Assisting Choice Model Specification
Sep 16, 2026Gabriel Nova, Stephane Hess, Sander Van CranenburghDiscrete Choice Modeling Deep Sets
CityAI Lab, Transport and Logistics Group, Delft University of Technology, The Netherlands · Institute for Transport Studies and Choice Modelling Centre, University of Leeds, UK
Learning the Geometry of Collider Events with Metric-Aware Deep Sets
Sep 14, 2026Lauren Hay, Rishabh Jain, Matt LeBlanc +1Neural Surrogate Modeling Representation Learning
Brown University / IAIFI · Brown University
Inductive Biases in Field-Level Cosmological Inference from Galaxy Catalogs
Sep 8, 2026James O. Baldwin, Shy Genel, Francisco Villaescusa-NavarroSimulation-Based Inference Graph Neural Networks
City University of New York Graduate Center, 365 5th Ave, New York, NY 10016 · Center for Computational Astrophysics, Flatiron Institute, 162 5th Avenue, New York, NY 10010, USA · Columbia Astrophysics Laboratory, Columbia University, 550 West 120th Street, New York, NY 10027, USA +1
Optimizing Sensor Placement for Hydrogen Leak Detection in Enclosed Infrastructure: A Comparative Study Using CFD-informed Genetic Algorithm and DeepSets Neural Surrogate
Jul 20, 2026Fangnian Wang, Nicholas Tan Jerome, Thomas Jordan +1Surrogate-Assisted Optimization Deep Sets
Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany.
Population-Aware Physics-Informed Neural Particle Flow for Bayesian Update
Jun 9, 2026Batu Candan, Simone ServadioBayesian Inference Deep Sets
Iowa State University, Ames, IA 50011, USA
S2M-Trek: From Single to Multi-Sphere Transport via Per-Frame Deep Sets on a Wheel-Legged Robot
May 31, 2026Zong Chen, Xuebin Li, Jinpeng Xiao +5Representation Learning Robot Skill Learning
School of Mechanical Science and Engineering, Huazhong University of Science and Technology · School of Mathematics, Harbin Institute of Technology
Bridging Chemists and AI: An Expert-Augmented Framework for Interpretable Route Evaluation
May 27, 2026Yujia Guo, Mikhail Kabeshov, Tat Hong Duong Le +6Human-in-the-Loop Evaluation Deep Sets
Department of Computer Science, Aalto University, Espoo, Finland. · Discovery Sciences R&D, AstraZeneca, Gothenburg, Sweden. · Department of Computer Science and Engineering,May Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden. +1
Embedding Dimension Lower Bounds for Universality of Deep Sets and Janossy Pooling
May 8, 2026Ali Syed, Aditya Nambiar, Jonathan W. SiegelRepresentation Learning Deep Sets
Department of Mathematics Texas A&M University College Station, TX 77843
It Just Takes Two: Scaling Amortized Inference to Large Sets
May 8, 2026Antoine Wehenkel, Michael Kagan, Lukas Heinrich +1Neural Posterior Estimation Representation Learning
Apple · SLAC National Accelerator Laboratory · TU München +1

Sep 16, 2026, econ.GNJ/K move · Enter open · S save

Multitask Reinforcement Learning for Assisting Choice Model Specification

Gabriel Nova, Stephane Hess, Sander Van Cranenburgh

CityAI Lab, Transport and Logistics Group, Delft University of Technology, The Netherlands · Institute for Transport Studies and Choice Modelling Centre, University of Leeds, UK

Discrete Choice Modeling Deep Sets Multi-Task RL

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Discrete choice model specification is a time-consuming task in which modellers often specify and estimate multiple models while balancing goodness-of-fit, parsimony, and behavioural plausibility. We present Delphos, a multitask reinforcement learning framework that learns transferable specification strategies across transport choice datasets. Delphos frames model specification as a sequential decision-making problem in which it applies a sequence of modelling actions and receives feedback from an estimation environment based on model performance and convergence. To transfer modelling decisions across datasets with different sets of variables, Delphos represents utility specifications as sets of modelling terms using a DeepSet-Q architecture, allowing a shared specification policy to learn across multiple datasets. Trained on nine transport choice datasets, Delphos consistently outperforms independently trained single-task agents, indicating that sharing modelling experience improves learning efficiency and helps identify promising sequences of modelling decisions with fewer unsuccessful estimation attempts. When applied without further training to the unseen Swissmetro and Decisions datasets, the same agent identifies competitive specifications in less than 20 minutes on a standard CPU. It achieves a higher log-likelihood per observation than the VNS metaheuristic on Swissmetro and performance comparable to a published MNL specification developed by expert modellers on Decisions. These findings show that accumulating and reusing modelling experience enables Delphos to function as an intelligent assistant for discrete choice model specification. It reduces manual trial-and-error while allowing modellers to retain control over model diagnosis, refinement, and final selection.

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