People
Huyen Khanh Vo
Saarland Informatics Campus
Building E1.1, Room 2.22.1
About me
I am a PhD student at the CS@Max Planck PhD program, advised by Prof. Isabel Valera. My research interest focuses on the optimization challenges in training probabilistic multimodal generative models.
Previously, I was a Research Resident at FPT Software AI Center, one of the only two AI residency programs in Vietnam, working with Prof. Tan M. Nguyen and Dr. Thieu N. Vo.
In 2023, I graduated from the Honors Program at School of Information and Communication Technology, Hanoi University of Science & Technology (HUST) with a Computer Science Bachelor’s degree. During my time here, I was a research student at Data Science Laboratory, advised by Dr. Linh V. Ngo and Prof. Khoat Q. Than.
Publications
2026
Gupta, Kavya; Kalampalikis, Nektarios; Heitz, Christoph; Valera, Isabel
First-See-Then-Design: A Multi-Stakeholder View for Optimal Performance-Fairness Trade-Offs Proceedings Article
In: Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency, pp. 218–248, ACM, 2026.
Abstract | Links | BibTeX | Tags: isabel, kavya, nektarios, saml
@inproceedings{Gupta_2026,
title = {First-See-Then-Design: A Multi-Stakeholder View for Optimal Performance-Fairness Trade-Offs},
author = {Kavya Gupta and Nektarios Kalampalikis and Christoph Heitz and Isabel Valera},
url = {http://dx.doi.org/10.1145/3805689.3806541},
doi = {10.1145/3805689.3806541},
year = {2026},
date = {2026-06-01},
urldate = {2026-06-01},
booktitle = {Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency},
pages = {218–248},
publisher = {ACM},
series = {FAccT ’26},
abstract = {Fairness in algorithmic decision-making is often defined in the predictive space, where predictive performance - used as a proxy for decision-maker (DM) utility - is traded off against prediction-based fairness notions, such as demographic parity or equality of opportunity. This perspective, however, ignores how predictions translate into decisions and ultimately into utilities and welfare for both DM and decision subjects (DS), as well as their allocation across social-salient groups.
In this paper, we propose a multi-stakeholder framework for fair algorithmic decision-making grounded in welfare economics and distributive justice, explicitly modeling the utilities of both the DM and DS, and defining fairness via a social planner's utility that captures inequalities in DS utilities across groups under different justice-based fairness notions (e.g., Egalitarian, Rawlsian). We formulate fair decision-making as a post-hoc multi-objective optimization problem, characterizing the achievable performance-fairness trade-offs in the two-dimensional utility space of DM utility and the social planner's utility, under different decision policy classes (deterministic vs. stochastic, shared vs. group-specific). Using the proposed framework, we then identify conditions (in terms of the stakeholders' utilities) under which stochastic policies are more optimal than deterministic ones, and empirically demonstrate that simple stochastic policies can yield superior performance-fairness trade-offs by leveraging outcome uncertainty. Overall, we advocate a shift from prediction-centric fairness to a transparent, justice-based, multi-stakeholder approach that supports the collaborative design of decision-making policies.},
keywords = {isabel, kavya, nektarios, saml},
pubstate = {published},
tppubtype = {inproceedings}
}
Fairness in algorithmic decision-making is often defined in the predictive space, where predictive performance - used as a proxy for decision-maker (DM) utility - is traded off against prediction-based fairness notions, such as demographic parity or equality of opportunity. This perspective, however, ignores how predictions translate into decisions and ultimately into utilities and welfare for both DM and decision subjects (DS), as well as their allocation across social-salient groups.
In this paper, we propose a multi-stakeholder framework for fair algorithmic decision-making grounded in welfare economics and distributive justice, explicitly modeling the utilities of both the DM and DS, and defining fairness via a social planner's utility that captures inequalities in DS utilities across groups under different justice-based fairness notions (e.g., Egalitarian, Rawlsian). We formulate fair decision-making as a post-hoc multi-objective optimization problem, characterizing the achievable performance-fairness trade-offs in the two-dimensional utility space of DM utility and the social planner's utility, under different decision policy classes (deterministic vs. stochastic, shared vs. group-specific). Using the proposed framework, we then identify conditions (in terms of the stakeholders' utilities) under which stochastic policies are more optimal than deterministic ones, and empirically demonstrate that simple stochastic policies can yield superior performance-fairness trade-offs by leveraging outcome uncertainty. Overall, we advocate a shift from prediction-centric fairness to a transparent, justice-based, multi-stakeholder approach that supports the collaborative design of decision-making policies.
In this paper, we propose a multi-stakeholder framework for fair algorithmic decision-making grounded in welfare economics and distributive justice, explicitly modeling the utilities of both the DM and DS, and defining fairness via a social planner's utility that captures inequalities in DS utilities across groups under different justice-based fairness notions (e.g., Egalitarian, Rawlsian). We formulate fair decision-making as a post-hoc multi-objective optimization problem, characterizing the achievable performance-fairness trade-offs in the two-dimensional utility space of DM utility and the social planner's utility, under different decision policy classes (deterministic vs. stochastic, shared vs. group-specific). Using the proposed framework, we then identify conditions (in terms of the stakeholders' utilities) under which stochastic policies are more optimal than deterministic ones, and empirically demonstrate that simple stochastic policies can yield superior performance-fairness trade-offs by leveraging outcome uncertainty. Overall, we advocate a shift from prediction-centric fairness to a transparent, justice-based, multi-stakeholder approach that supports the collaborative design of decision-making policies.
Kalampalikis, Nektarios; Gupta, Kavya; Vitanov, Georgi; Valera, Isabel
Towards Reasonable Concept Bottleneck Models Journal Article
In: Transactions on Machine Learning Research, 2026, ISSN: 2835-8856.
Abstract | Links | BibTeX | Tags: isabel, kavya, nektarios, saml
@article{<LineBreak>kalampalikis2026towards,
title = {Towards Reasonable Concept Bottleneck Models},
author = {Nektarios Kalampalikis and Kavya Gupta and Georgi Vitanov and Isabel Valera},
url = {https://openreview.net/forum?id=0TxwJMhXBY},
issn = {2835-8856},
year = {2026},
date = {2026-01-01},
urldate = {2026-01-01},
journal = {Transactions on Machine Learning Research},
abstract = {In this paper, we propose textbf{C}oncept textbf{REA}soning textbf{M}odels (CREAM), a novel family of Concept Bottleneck Models (CBMs) that: (i) explicitly encodes concept-concept ({texttt{C-C}}) and concept-task ({texttt{C$rightarrow$Y}}) relationships to enforce a desired model reasoning; and (ii) use a regularized side-channel to achieve competitive task performance, while keeping high concept importance. Specifically, CREAM architecturally embeds (bi)directed concept-concept, and concept to task relationships specified by a human expert, while severing undesired information flows (e.g., to handle mutually exclusive concepts). Moreover, CREAM integrates a black-box side-channel that is regularized to encourage task predictions to be grounded in the relevant concepts, thereby utilizing the side-channel only when necessary to enhance performance. Our experiments show that: (i) CREAM mainly relies on concepts while achieving task performance on par with black-box models; and (ii) the embedded {texttt{C-C}} and {texttt{C$rightarrow$Y}} relationships ease model interventions and mitigate concept leakage.},
keywords = {isabel, kavya, nektarios, saml},
pubstate = {published},
tppubtype = {article}
}
In this paper, we propose textbf{C}oncept textbf{REA}soning textbf{M}odels (CREAM), a novel family of Concept Bottleneck Models (CBMs) that: (i) explicitly encodes concept-concept ({texttt{C-C}}) and concept-task ({texttt{C$rightarrow$Y}}) relationships to enforce a desired model reasoning; and (ii) use a regularized side-channel to achieve competitive task performance, while keeping high concept importance. Specifically, CREAM architecturally embeds (bi)directed concept-concept, and concept to task relationships specified by a human expert, while severing undesired information flows (e.g., to handle mutually exclusive concepts). Moreover, CREAM integrates a black-box side-channel that is regularized to encourage task predictions to be grounded in the relevant concepts, thereby utilizing the side-channel only when necessary to enhance performance. Our experiments show that: (i) CREAM mainly relies on concepts while achieving task performance on par with black-box models; and (ii) the embedded {texttt{C-C}} and {texttt{C$rightarrow$Y}} relationships ease model interventions and mitigate concept leakage.
Kalampalikis, Nektarios; Olmos, Pablo M.; Valera, Isabel
Calibration in One Go: Jointly Learning Accurate and Calibrated Neural Networks Miscellaneous
2026.
Abstract | Links | BibTeX | Tags: isabel, nektarios, pablo
@misc{kalampalikis2026calibrationgojointlylearning,
title = {Calibration in One Go: Jointly Learning Accurate and Calibrated Neural Networks},
author = {Nektarios Kalampalikis and Pablo M. Olmos and Isabel Valera},
url = {https://arxiv.org/abs/2610.13693},
year = {2026},
date = {2026-01-01},
urldate = {2026-01-01},
abstract = {Neural networks (NNs), while highly flexible and versatile, are often poorly calibrated. Post-hoc calibration methods adjust the outputs of an already trained model using a separate calibration dataset and additional calibration parameters. This process adds overhead that requires separate optimization of the calibrator and subsequent model selection. In this paper, we show that standard (post-hoc) calibrators can be trained end-to-end with the backbone via a bilevel Stackelberg formulation. This formulation eliminates the need for a separate calibration set or a dedicated calibration loss by jointly optimizing accuracy and calibration using cross-entropy, introducing only one additional hyperparameter: the calibrator learning rate. Although our framework supports calibrators ranging from temperature and matrix scaling to NN layers, our results show that using the final classification layer itself as the calibrator yields accurate, well-calibrated models with no additional parameters. Our approach outperforms SOTA calibrators with minimal added complexity, while also avoiding the multiplicative overhead of separate calibration components in settings such as multi-task learning},
keywords = {isabel, nektarios, pablo},
pubstate = {published},
tppubtype = {misc}
}
Neural networks (NNs), while highly flexible and versatile, are often poorly calibrated. Post-hoc calibration methods adjust the outputs of an already trained model using a separate calibration dataset and additional calibration parameters. This process adds overhead that requires separate optimization of the calibrator and subsequent model selection. In this paper, we show that standard (post-hoc) calibrators can be trained end-to-end with the backbone via a bilevel Stackelberg formulation. This formulation eliminates the need for a separate calibration set or a dedicated calibration loss by jointly optimizing accuracy and calibration using cross-entropy, introducing only one additional hyperparameter: the calibrator learning rate. Although our framework supports calibrators ranging from temperature and matrix scaling to NN layers, our results show that using the final classification layer itself as the calibrator yields accurate, well-calibrated models with no additional parameters. Our approach outperforms SOTA calibrators with minimal added complexity, while also avoiding the multiplicative overhead of separate calibration components in settings such as multi-task learning