create_nocite.ipynb

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 "cells": [
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   "cell_type": "code",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "@3blue1brown_how_2024, @agresti_foundations_2015, @albon_machine_2024, @albon_machine_2024-1, @amazon_what_2024, @andrej_karpathy_lets_2024, @angelopoulos_gentle_2022, @arel-bundock_marginal_2024, @bai_understanding_2021, @barrett_causal_2024, @belkin_reconciling_2019, @bergmann_what_2023, @biecek_explanatory_2020, @bischl_applied_2024, @bishop_pattern_2006, @boehmke_hands-machine_2020, @boykis_what_2023, @breiman_statistical_2001, @brownlee_gentle_2016, @brownlee_gentle_2019, @brownlee_gradient_2021, @brownlee_how_2020, @burges_learning_2005, @burges_ranknet_2016, @burkner_ordinal_2019, @bycroft_llm_2023, @carpenter_prior_2023, @causalml_causalml_2023, @cawley_over-fitting_2010, @chawla_smote_2002, @chernozhukov_applied_2024, @clark_bayesian_2022, @clark_deep_2022, @clark_generalized_2022, @clark_graphical_2018, @clark_mixed_2023, @clark_model_2021, @clark_practical_2020, @clark_thinking_2018, @clark_this_2021, @cohen_statistical_2009, @cross_validated_answer_2011, @cross_validated_answer_2020, @cross_validated_answer_2021, @cross_validated_why_2016, @cunningham_causal_2023, @dahabreh_causal_2024, @databricks_what_2019, @davison_bootstrap_1997, @dobson_introduction_2018, @dunn_distribution-free_2020, @dunn_generalized_2018, @efron_introduction_1994, @elor_smote_2022, @facure_alves_causal_2022, @fahrmeir_regression_2021, @faraway_extending_2016, @faraway_linear_2014, @ferrari_beta_2004, @fleuret_little_2023, @fortuner_machine_2023, @fox_applied_2015, @gelman_advanced_2024, @gelman_bayesian_2013, @gelman_data_2006, @gelman_garden_2013, @gelman_regression_2020, @gelman_what_2013, @goodfellow_deep_2016, @google_classification_2024, @google_imbalanced_2023, @google_introduction_2023, @google_machine_2023, @google_mlops_2024, @google_reducing_2024, @google_what_2024, @gorishniy_tabr_2023, @greene_econometric_2017, @grolemund_welcome_2023, @hardin_generalized_2018, @harrell_regression_2015, @hastie_elements_2017, @heiss_marginalia_2022, @hernan_c-word_2018, @hernan_causal_2012, @howard_practical_2024, @hugging_face_byte-pair_2024, @hvitfeldt_feature_2024, @hyndman_forecasting_2021, @ivanova_comprehension_2020, @james_introduction_2021, @jiang_visual_2020, @jordan_introduction_2018, @kirillov_segment_2023, @koenker_galton_2000, @koenker_quantile_2005, @kruschke_doing_2010, @kuhn_applied_2023, @kuhn_tidy_2023, @kunzel_metalearners_2019, @lang_mlr3_2019, @lecun_self-supervised_2021, @lee_deep_2017, @leech_questionable_2024, @lones_how_2024, @mahr_random_2021, @masis_interpretable_2023, @mayo_error_2019, @mccullagh_generalized_2019, @mcculloch_logical_1943, @mcelreath_statistical_2020, @mckinney_python_2023, @microsoft_generative_2024, @mit_opencourseware_6_2017, @molnar_interpretable_2023, @molnar_introduction_2024, @monroe_imbalanced_2025, @morgan_counterfactuals_2014, @murphy_machine_2012, @murphy_probabilistic_2023, @navarro_learning_2018, @neal_priors_1996, @nelder_generalized_1972, @niculescu-mizil_predicting_2005, @pearl_causal_2009, @pearl_causal_2022, @pedregosa_scikit-learn_2011, @peng_r_2022, @penn_state_54_2018, @pochinkov_llm_2023, @pok_how_2020, @power_grokking_2022, @prince_understanding_2023, @quantmetry_mapie_2024, @raschka_about_2014, @raschka_build_2023, @raschka_losses_2022, @raschka_machine_2022, @raschka_machine_2023, @rasmussen_gaussian_2005, @ripley_pattern_1996, @roback_beyond_2021, @roberts_neural_2000, @robins_marginal_2000, @rocca_handling_2019, @rovine_peirce_2004, @schmidhuber_annotated_2022, @scikit-learn_116_2023, @scikit-learn_nested_2023, @sen_decoder-only_2024, @shalizi_f-tests_2015, @shevchenko_types_2023, @shorten_text_2021, @simpson_using_2021, @stackexchange_are_2015, @statquest_with_josh_starmer_bootstrapping_2021, @statquest_with_josh_starmer_gradient_2019, @statquest_with_josh_starmer_stochastic_2019, @turrell_python_2024, @ucla_advanced_research_computing_faq_2023, @ucla_advanced_research_computing_faq_2023-1, @ushey_arrays_2024, @vanderplas_python_2016, @vanderweele_invited_2012, @vaswani_attention_2017, @vig_deconstructing_2019, @walker_analyzing_2023, @weed_learning_2021, @welchowski_techniques_2022, @wikipedia_cross-entropy_2024, @wikipedia_exponential_2024, @wikipedia_gradient_2024, @wikipedia_relationships_2023, @wikipedia_replication_2024, @witten_bias-variance_2020, @wood_generalized_2017, @wooldridge_introductory_2012, @ye_modern_2024, @yeh_ai_2024, @zhang_dive_2023\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "# Specify the path to the \"references.bib\" file\n",
    "file_path = \"references.bib\"\n",
    "\n",
    "# Read the contents of the file\n",
    "with open(file_path, \"r\") as file:\n",
    "    file_contents = file.read()\n",
    "\n",
    "# Use regular expressions to extract the names of the BibTeX references\n",
    "references = re.findall(r'@.*?{(.*?),', file_contents)\n",
    "\n",
    "# Create a comma-separated list of the reference names with \"@\" symbol\n",
    "reference_list = [f\"@{ref}\" for ref in references]\n",
    "reference_list.sort()\n",
    "\n",
    "\n",
    "# Concatenate the list into a single string\n",
    "reference_string = ', '.join(reference_list)\n",
    "\n",
    "# Print the resulting string\n",
    "\n",
    "print(reference_string)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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