---
OA_place: publisher
OA_type: hybrid
PlanS_conform: '1'
_id: '12662'
abstract:
- lang: eng
text: 'Modern machine learning tasks often require considering not just one but
multiple objectives. For example, besides the prediction quality, this could be
the efficiency, robustness or fairness of the learned models, or any of their
combinations. Multi-objective learning offers a natural framework for handling
such problems without having to commit to early trade-offs. Surprisingly, statistical
learning theory so far offers almost no insight into the generalization properties
of multi-objective learning. In this work, we make first steps to fill this gap:
We establish foundational generalization bounds for the multi-objective setting
as well as generalization and excess bounds for learning with scalarizations.
We also provide the first theoretical analysis of the relation between the Pareto-optimal
sets of the true objectives and the Pareto-optimal sets of their empirical approximations
from training data. In particular, we show a surprising asymmetry: All Pareto-optimal
solutions can be approximated by empirically Pareto-optimal ones, but not vice
versa.'
acknowledgement: Open access funding provided by Institute of Science and Technology
(IST Austria).
article_processing_charge: Yes (via OA deal)
article_type: original
arxiv: 1
author:
- first_name: Peter
full_name: Súkeník, Peter
id: d64d6a8d-eb8e-11eb-b029-96fd216dec3c
last_name: Súkeník
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: Súkeník P, Lampert C. Generalization in multi-objective machine learning. Neural
Computing and Applications. 2025;37:24669–24683. doi:10.1007/s00521-024-10616-1
apa: Súkeník, P., & Lampert, C. (2025). Generalization in multi-objective machine
learning. Neural Computing and Applications. Springer Nature. https://doi.org/10.1007/s00521-024-10616-1
chicago: Súkeník, Peter, and Christoph Lampert. “Generalization in Multi-Objective
Machine Learning.” Neural Computing and Applications. Springer Nature,
2025. https://doi.org/10.1007/s00521-024-10616-1.
ieee: P. Súkeník and C. Lampert, “Generalization in multi-objective machine learning,”
Neural Computing and Applications, vol. 37. Springer Nature, pp. 24669–24683,
2025.
ista: Súkeník P, Lampert C. 2025. Generalization in multi-objective machine learning.
Neural Computing and Applications. 37, 24669–24683.
mla: Súkeník, Peter, and Christoph Lampert. “Generalization in Multi-Objective Machine
Learning.” Neural Computing and Applications, vol. 37, Springer Nature,
2025, pp. 24669–24683, doi:10.1007/s00521-024-10616-1.
short: P. Súkeník, C. Lampert, Neural Computing and Applications 37 (2025) 24669–24683.
corr_author: '1'
date_created: 2023-02-20T08:23:06Z
date_published: 2025-10-01T00:00:00Z
date_updated: 2025-12-30T06:39:56Z
day: '01'
ddc:
- '004'
department:
- _id: ChLa
doi: 10.1007/s00521-024-10616-1
external_id:
arxiv:
- '2208.13499'
file:
- access_level: open_access
checksum: 61ad4591aee16b1e02daf6c164321a42
content_type: application/pdf
creator: dernst
date_created: 2025-12-30T06:39:11Z
date_updated: 2025-12-30T06:39:11Z
file_id: '20877'
file_name: 2025_NeuralCompApplic_Sukenik.pdf
file_size: 500213
relation: main_file
success: 1
file_date_updated: 2025-12-30T06:39:11Z
has_accepted_license: '1'
intvolume: ' 37'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
page: 24669–24683
publication: Neural Computing and Applications
publication_identifier:
eissn:
- 1433-3058
issn:
- 0941-0643
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Generalization in multi-objective machine learning
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 37
year: '2025'
...
---
_id: '14451'
abstract:
- lang: eng
text: 'We investigate the potential of Multi-Objective, Deep Reinforcement Learning
for stock and cryptocurrency single-asset trading: in particular, we consider
a Multi-Objective algorithm which generalizes the reward functions and discount
factor (i.e., these components are not specified a priori, but incorporated in
the learning process). Firstly, using several important assets (BTCUSD, ETHUSDT,
XRPUSDT, AAPL, SPY, NIFTY50), we verify the reward generalization property of
the proposed Multi-Objective algorithm, and provide preliminary statistical evidence
showing increased predictive stability over the corresponding Single-Objective
strategy. Secondly, we show that the Multi-Objective algorithm has a clear edge
over the corresponding Single-Objective strategy when the reward mechanism is
sparse (i.e., when non-null feedback is infrequent over time). Finally, we discuss
the generalization properties with respect to the discount factor. The entirety
of our code is provided in open-source format.'
acknowledgement: Open access funding provided by Università degli Studi di Trieste
within the CRUI-CARE Agreement. Funding was provided by Austrian Science Fund (Grant
No. F65), Horizon 2020 (Grant No. 754411) and Österreichische Forschungsförderungsgesellschaft.
article_processing_charge: Yes (via OA deal)
article_type: original
arxiv: 1
author:
- first_name: Federico
full_name: Cornalba, Federico
id: 2CEB641C-A400-11E9-A717-D712E6697425
last_name: Cornalba
orcid: 0000-0002-6269-5149
- first_name: Constantin
full_name: Disselkamp, Constantin
last_name: Disselkamp
- first_name: Davide
full_name: Scassola, Davide
last_name: Scassola
- first_name: Christopher
full_name: Helf, Christopher
last_name: Helf
citation:
ama: 'Cornalba F, Disselkamp C, Scassola D, Helf C. Multi-objective reward generalization:
Improving performance of Deep Reinforcement Learning for applications in single-asset
trading. Neural Computing and Applications. 2024;36(2):617-637. doi:10.1007/s00521-023-09033-7'
apa: 'Cornalba, F., Disselkamp, C., Scassola, D., & Helf, C. (2024). Multi-objective
reward generalization: Improving performance of Deep Reinforcement Learning for
applications in single-asset trading. Neural Computing and Applications.
Springer Nature. https://doi.org/10.1007/s00521-023-09033-7'
chicago: 'Cornalba, Federico, Constantin Disselkamp, Davide Scassola, and Christopher
Helf. “Multi-Objective Reward Generalization: Improving Performance of Deep Reinforcement
Learning for Applications in Single-Asset Trading.” Neural Computing and Applications.
Springer Nature, 2024. https://doi.org/10.1007/s00521-023-09033-7.'
ieee: 'F. Cornalba, C. Disselkamp, D. Scassola, and C. Helf, “Multi-objective reward
generalization: Improving performance of Deep Reinforcement Learning for applications
in single-asset trading,” Neural Computing and Applications, vol. 36, no.
2. Springer Nature, pp. 617–637, 2024.'
ista: 'Cornalba F, Disselkamp C, Scassola D, Helf C. 2024. Multi-objective reward
generalization: Improving performance of Deep Reinforcement Learning for applications
in single-asset trading. Neural Computing and Applications. 36(2), 617–637.'
mla: 'Cornalba, Federico, et al. “Multi-Objective Reward Generalization: Improving
Performance of Deep Reinforcement Learning for Applications in Single-Asset Trading.”
Neural Computing and Applications, vol. 36, no. 2, Springer Nature, 2024,
pp. 617–37, doi:10.1007/s00521-023-09033-7.'
short: F. Cornalba, C. Disselkamp, D. Scassola, C. Helf, Neural Computing and Applications
36 (2024) 617–637.
corr_author: '1'
date_created: 2023-10-22T22:01:16Z
date_published: 2024-01-01T00:00:00Z
date_updated: 2025-04-23T07:39:14Z
day: '01'
ddc:
- '000'
department:
- _id: JuFi
doi: 10.1007/s00521-023-09033-7
ec_funded: 1
external_id:
arxiv:
- '2203.04579'
pmid:
- '38187995'
file:
- access_level: open_access
checksum: 04573d8e74c6119b97c2ca0a984e19a1
content_type: application/pdf
creator: dernst
date_created: 2024-07-16T08:08:54Z
date_updated: 2024-07-16T08:08:54Z
file_id: '17251'
file_name: 2024_NeuralCompApplications_Cornalba.pdf
file_size: 4412285
relation: main_file
success: 1
file_date_updated: 2024-07-16T08:08:54Z
has_accepted_license: '1'
intvolume: ' 36'
issue: '2'
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
page: 617-637
pmid: 1
project:
- _id: fc31cba2-9c52-11eb-aca3-ff467d239cd2
grant_number: F6504
name: Taming Complexity in Partial Differential Systems
- _id: 260C2330-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '754411'
name: ISTplus - Postdoctoral Fellowships
publication: Neural Computing and Applications
publication_identifier:
eissn:
- 1433-3058
issn:
- 0941-0643
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Multi-objective reward generalization: Improving performance of Deep Reinforcement
Learning for applications in single-asset trading'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 36
year: '2024'
...