{"url":"/task/discrete-choice-models","name":"Discrete Choice Models","slug":"discrete-choice-models","description_markdown":null,"categories":[{"name":"Reasoning","url":"/area/reasoning"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"derived"},"counts":{"papers_tagged":104,"papers_with_code":16,"benchmarks":0,"benchmark_tables_in_archive":0,"benchmark_tables_shown":0,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":0,"subtasks":0,"parent_tasks":0},"benchmarks":[],"datasets":[],"subtasks":[],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":16,"of":16,"tagged_in_all":104,"items":[{"url":"/paper/representing-random-utility-choice-models","title":"Representing Random Utility Choice Models with Neural Networks","date":"2022-07-26","arxiv_id":"2207.12877","repositories_listed":2,"syntology":null},{"url":"/paper/a-neural-embedded-choice-model-tastenet-mnl","title":"A Neural-embedded Choice Model: TasteNet-MNL Modeling Taste Heterogeneity with Flexibility and Interpretability","date":"2020-02-03","arxiv_id":"2002.00922","repositories_listed":2,"syntology":null},{"url":"/paper/let-me-not-lie-learning-multinomial-logit","title":"Enhancing Discrete Choice Models with Representation Learning","date":"2018-12-23","arxiv_id":"1812.09747","repositories_listed":2,"syntology":null},{"url":"/paper/choice-learn-large-scale-choice-modeling-for","title":"Choice-Learn: Large-scale choice modeling for operational contexts through the lens of machine learning","date":"2024-09-10","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/the-heterogeneous-aggregate-valence-analysis","title":"The Mixed Aggregate Preference Logit Model: A Machine Learning Approach to Modeling Unobserved Heterogeneity in Discrete Choice Analysis","date":"2024-01-31","arxiv_id":"2402.00184","repositories_listed":1,"syntology":null},{"url":"/paper/a-prediction-and-behavioural-analysis-of","title":"A prediction and behavioural analysis of machine learning methods for modelling travel mode choice","date":"2023-01-11","arxiv_id":"2301.04404","repositories_listed":1,"syntology":null},{"url":"/paper/graph-based-methods-for-discrete-choice","title":"Graph-Based Methods for Discrete Choice","date":"2022-05-23","arxiv_id":"2205.11365","repositories_listed":1,"syntology":null},{"url":"/paper/fast-learning-of-mnl-model-from-general","title":"Fast Learning of MNL Model from General Partial Rankings with Application to Network Formation Modeling","date":"2021-12-31","arxiv_id":"2112.15575","repositories_listed":1,"syntology":null},{"url":"/paper/equality-of-opportunity-in-travel-behavior","title":"Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models","date":"2021-09-25","arxiv_id":"2109.12422","repositories_listed":1,"syntology":null},{"url":"/paper/combining-discrete-choice-models-and-neural","title":"Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance","date":"2021-09-24","arxiv_id":"2109.12042","repositories_listed":1,"syntology":null},{"url":"/paper/identification-of-incomplete-preferences","title":"Identification of Incomplete Preferences","date":"2021-08-13","arxiv_id":"2108.06282","repositories_listed":1,"syntology":null},{"url":"/paper/choice-set-confounding-in-discrete-choice","title":"Choice Set Confounding in Discrete Choice","date":"2021-05-17","arxiv_id":"2105.07959","repositories_listed":1,"syntology":null},{"url":"/paper/semi-discrete-optimal-transport-hardness","title":"Semi-Discrete Optimal Transport: Hardness, Regularization and Numerical Solution","date":"2021-03-10","arxiv_id":"2103.06263","repositories_listed":1,"syntology":null},{"url":"/paper/robust-discrete-choice-models-with-t","title":"Robust discrete choice models with t-distributed kernel errors","date":"2020-09-14","arxiv_id":"2009.06383","repositories_listed":1,"syntology":null},{"url":"/paper/choice-set-optimization-under-discrete-choice","title":"Choice Set Optimization Under Discrete Choice Models of Group Decisions","date":"2020-02-02","arxiv_id":"2002.00421","repositories_listed":1,"syntology":null},{"url":"/paper/a-branch-and-cut-algorithm-for-the-latent","title":"A branch-and-cut algorithm for the latent-class logit assortment problem","date":"2014-02-14","arxiv_id":null,"repositories_listed":1,"syntology":null}],"syntology_records":0,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}