Jakob Heiss

19 papers A* 5Journal 14
YearRankTypeTitle / Venue / Authors
2026 J jnl
JUCAL: Jointly Calibrating Aleatoric and Epistemic Uncertainty in Classification Tasks.
CoRR
Jakob Heiss, Sören Lambrecht, Jakob Weissteiner, Hanna Wutte, Zan Zuric, Josef Teichmann, Bin Yu
2025 J jnl
CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk.
CoRR
Ilia Azizi, Juraj Bodik, Jakob Heiss, Bin Yu
2025 A* conf
Prices, Bids, Values: One ML-Powered Combinatorial Auction to Rule Them All.
ICML
Ermis Soumalias, Jakob Heiss, Jakob Weissteiner, Sven Seuken
2025 J jnl
Revealing the temporal dynamics of antibiotic anomalies in the infant gut microbiome with neural jump ODEs.
CoRR
Anja Adamov, Markus Chardonnet, Florian Krach, Jakob Heiss, Josef Teichmann, Nicholas A. Bokulich
2024 J jnl
Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework.
Trans. Mach. Learn. Res.
William Andersson, Jakob Heiss, Florian Krach, Josef Teichmann
2024 A* conf
Machine Learning-Powered Combinatorial Clock Auction.
AAAI
Ermis Nikiforos Soumalias, Jakob Weissteiner, Jakob Heiss, Sven Seuken
2024 J jnl
Nonparametric Filtering, Estimation and Classification using Neural Jump ODEs.
CoRR
Jakob Heiss, Florian Krach, Thorsten Schmidt, Félix B. Tambe-Ndonfack
2024 J jnl
Prices, Bids, Values: Everything, Everywhere, All at Once.
CoRR
Ermis Soumalias, Jakob Heiss, Jakob Weissteiner, Sven Seuken
2023 A* conf
Bayesian Optimization-Based Combinatorial Assignment.
AAAI
Jakob Weissteiner, Jakob Heiss, Julien Siems, Sven Seuken
2023 J jnl
Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework.
CoRR
William Andersson, Jakob Heiss, Florian Krach, Josef Teichmann
2023 J jnl
How (Implicit) Regularization of ReLU Neural Networks Characterizes the Learned Function - Part II: the Multi-D Case of Two Layers with Random First Layer.
CoRR
Jakob Heiss, Josef Teichmann, Hanna Wutte
2023 J jnl
Machine Learning-powered Combinatorial Clock Auction.
CoRR
Ermis Soumalias, Jakob Weissteiner, Jakob Heiss, Sven Seuken
2022 J jnl
Bayesian Optimization-based Combinatorial Assignment.
CoRR
Jakob Weissteiner, Jakob Heiss, Julien Siems, Sven Seuken
2022 A* conf
Monotone-Value Neural Networks: Exploiting Preference Monotonicity in Combinatorial Assignment.
IJCAI
Jakob Weissteiner, Jakob Heiss, Julien Siems, Sven Seuken
2022 A* conf
NOMU: Neural Optimization-based Model Uncertainty.
ICML
Jakob Heiss, Jakob Weissteiner, Hanna S. Wutte, Sven Seuken, Josef Teichmann
2021 J jnl
Infinite wide (finite depth) Neural Networks benefit from multi-task learning unlike shallow Gaussian Processes - an exact quantitative macroscopic characterization.
CoRR
Jakob Heiss, Josef Teichmann, Hanna Wutte
2021 J jnl
Monotone-Value Neural Networks: Exploiting Preference Monotonicity in Combinatorial Assignment.
CoRR
Jakob Weissteiner, Jakob Heiss, Julien Siems, Sven Seuken
2021 J jnl
NOMU: Neural Optimization-based Model Uncertainty.
CoRR
Jakob Heiss, Jakob Weissteiner, Hanna Wutte, Sven Seuken, Josef Teichmann
2019 J jnl
How implicit regularization of Neural Networks affects the learned function - Part I.
CoRR
Jakob Heiss, Josef Teichmann, Hanna Wutte
redb/extractors/js_extractors/scripts/package.json
← Index redb/extractors/js_extractors/scripts/package.json json 
{
  "name": "redb-js-xray-runner",
  "private": true,
  "description": "Node bridge that runs @nodesecure/js-x-ray for the Python JS pipeline. Run `npm install` here once, then point JS_XRAY_RUNNER_PATH at js-xray-runner.js (it defaults to this directory).",
  "version": "0.1.0",
  "main": "js-xray-runner.js",
  "scripts": {
    "postinstall": "patch-package"
  },
  "dependencies": {
    "@nodesecure/js-x-ray": "^7.0.0",
    "patch-package": "^8.0.1"
  }
}