Samira Ebrahimi Kahou

2025-06-20

rl-conference.cc/RLC/2025/Workshop/RLVG (accepté)

Towards Fair In-Context Learning with Tabular Foundation Models

Patrik Joslin Kenfack

Tabular foundational models have shown promising in-context learning capabilities on structured data by using training examples as context w… (voir plus)ithout further parameter adjustments. This emerging approach positions itself as a competitive alternative to traditional gradient-boosted tree methods. However, while biases in conventional machine learning models are well documented, it remains unclear how these biases manifest in Tabular ICL. The paper investigates the fairness implications of Tabular ICL and explores three preprocessing strategies—correlation removal, group-balanced demonstration selection, and uncertainty-based demonstration selection—to address bias. Comprehensive experiments indicate that uncertainty-based demonstration selection consistently enhances group fairness in the predictions. The source code for reproducing the results of this work can be found at https://anonymous.4open.science/r/Fair-TabICL-DD84.

2025-06-09

ICML.cc/2025/Workshop/FMSD (publié)

Towards Fair In-Context Learning with Tabular Foundation Models

Patrik Joslin Kenfack

Tabular foundational models have shown promising in-context learning capabilities on structured data by using training examples as context w… (voir plus)ithout further parameter adjustments. This emerging approach positions itself as a competitive alternative to traditional gradient-boosted tree methods. However, while biases in conventional machine learning models are well documented, it remains unclear how these biases manifest in Tabular ICL. The paper investigates the fairness implications of Tabular ICL and explores three preprocessing strategies—correlation removal, group-balanced demonstration selection, and uncertainty-based demonstration selection—to address bias. Comprehensive experiments indicate that uncertainty-based demonstration selection consistently enhances group fairness in the predictions. The source code for reproducing the results of this work can be found at https://anonymous.4open.science/r/Fair-TabICL-DD84.

2025-06-09

ICML.cc/2025/Workshop/FMSD (publié)

GradTune: Last-layer Fine-tuning for Group Robustness Without Group Annotation

Patrik Joslin Kenfack

This work addresses the limitations of deep neural networks (DNNs) in generalizing beyond training data due to spurious correlations. Recent… (voir plus) research has demonstrated that models trained with empirical risk minimization learn both core and spurious features, often upweighting spurious ones in the final classification, which can frequently lead to poor performance on minority groups. Deep Feature Reweighting alleviates this issue by retraining the model's last classification layer using a group-balanced held-out validation set. However, relying on spurious feature labels during training or validation limits practical application, as spurious features are not always known or costly to annotate. Our preliminary experiments reveal that ERM-trained models exhibit higher gradient norms on minority group samples in the hold-out dataset. Leveraging these insights, we propose an alternative approach called GradTune, which fine-tunes the last classification layer using high-gradient norm samples. Our results on four well-established benchmarks demonstrate that the proposed method can achieve competitive performance compared to existing methods without requiring group labels during training or validation.

2025-03-06

ICLR.cc/2025/Workshop/SCSL (publié)

GradTune: Last-layer Fine-tuning for Group Robustness Without Group Annotation

Patrik Joslin Kenfack

This work addresses the limitations of deep neural networks (DNNs) in generalizing beyond training data due to spurious correlations. Recent… (voir plus) research has demonstrated that models trained with empirical risk minimization learn both core and spurious features, often upweighting spurious ones in the final classification, which can frequently lead to poor performance on minority groups. Deep Feature Reweighting alleviates this issue by retraining the model's last classification layer using a group-balanced held-out validation set. However, relying on spurious feature labels during training or validation limits practical application, as spurious features are not always known or costly to annotate. Our preliminary experiments reveal that ERM-trained models exhibit higher gradient norms on minority group samples in the hold-out dataset. Leveraging these insights, we propose an alternative approach called GradTune, which fine-tunes the last classification layer using high-gradient norm samples. Our results on four well-established benchmarks demonstrate that the proposed method can achieve competitive performance compared to existing methods without requiring group labels during training or validation.

2025-03-06

ICLR.cc/2025/Workshop/SCSL (publié)

Towards personalized healthcare without harm via bias modulation

Frank Ngaha

Patrik Joslin Kenfack

Clinical prediction models are often personalized to target heterogeneous sub-groups by using demographic attributes such as race and gender… (voir plus) to train the model. Traditional personalization approaches involve using demographic attributes in input features or training multiple sub-models for different population subgroups (decoupling model). However, these methods often harm the performance at the subgroup level compared to non-personalized models. This paper presents a novel personalization method to improve model performance at the sub-group level. Our method involves a two-step process: first, we train a model to predict group attributes, and then we use this model to learn data-dependent biases to modulate a second model for diagnosis prediction. Our results demonstrate that this joint architecture achieves consistent performance gains across all sub-groups in the Heart dataset. Furthermore, in the mortality dataset, it improves performance in two of the four sub-groups. A comparison of our method with the traditional decoupled personalization method demonstrated a greater performance gain in the sub-groups with less harm. This approach offers a more effective and scalable solution for personalized models, which could have a positive impact in healthcare and other areas that require predictive models that take sub-group information into account.

2025-03-06

ICLR.cc/2025/Workshop/SCSL (publié)

Towards personalized healthcare without harm via bias modulation

Frank Ngaha

Patrik Joslin Kenfack

Personalized machine learning models have gained significant importance in various domains, including healthcare. However, designing efficie… (voir plus)nt personalized models remains a challenge. Traditional approaches often involve training multiple sub-models for different population sub-groups, which can be costly and does not always guarantee improved performance across all sub-groups. This paper presents a novel approach to improving model performance at the sub-group level by leveraging bias and training a joint model. Our method involves a two-step process: first, we train a model to predict group attributes, and then we use this model to learn data-dependent biases to modulate a second model for diagnosis prediction. Our results demonstrate that this joint architecture achieves consistent performance gains across all sub-groups in the Heart dataset. Furthermore, in the mortality dataset, it improves performance in two of the four sub-groups. A comparison of our method with the traditional decoupled personalization method demonstrated a greater performance gain in the sub-groups with less harm. This approach offers a more effective and scalable solution for personalization of models, which could have positive impact in healthcare and other areas that require predictive models which take sub-group information into account.

2025-03-06

ICLR.cc/2025/Workshop/SCSL (publié)

Learning Multi-agent Multi-machine Tending by Mobile Robots

Abdalwhab Abdalwhab

Giovanni Beltrame

David St-Onge

Robotics can help address the growing worker shortage challenge of the manufacturing industry. As such, machine tending is a task collaborat… (voir plus)ive robots can tackle that can also highly boost productivity. Nevertheless, existing robotics systems deployed in that sector rely on a fixed single-arm setup, whereas mobile robots can provide more flexibility and scalability. In this work, we introduce a multi-agent multi-machine tending learning framework by mobile robots based on Multi-agent Reinforcement Learning (MARL) techniques with the design of a suitable observation and reward. Moreover, an attention-based encoding mechanism is developed and integrated into Multi-agent Proximal Policy Optimization (MAPPO) algorithm to boost its performance for machine tending scenarios. Our model (AB-MAPPO) outperformed MAPPO in this new challenging scenario in terms of task success, safety, and resources utilization. Furthermore, we provided an extensive ablation study to support our various design decisions.

2025-02-25

AAAI.org/2025/Workshop/MARW (publié)

Handling Delay in Real-Time Reinforcement Learning

Ivan Anokhin

Rishav

Matthew D Riemer

Stephen Chung

Irina Rish

2025-01-22

ICLR.cc/2025/Conference (poster)

Handling Delay in Real-Time Reinforcement Learning

Ivan Anokhin

Rishav

Matthew D Riemer

Stephen Chung

Irina Rish

Real-time reinforcement learning (RL) introduces several challenges. First, policies are constrained to a fixed number of actions per second… (voir plus) due to hardware limitations. Second, the environment may change while the network is still computing an action, leading to observational delay. The first issue can partly be addressed with pipelining, leading to higher throughput and potentially better policies. However, the second issue remains: if each neuron operates in parallel with an execution time of

2025-01-22

ICLR.cc/2025/Conference (poster)

Prediction of Final Phosphorus Content of Steel in a Scrap-Based Electric Arc Furnace Using Artificial Neural Networks

Riadh Azzaz

Valentin Hurel

Patrice Ménard

M. Jahazi

Elmira Moosavi-Khoonsari

2024-10-25

ArXiv (prépublication)

arxiv.org

Empowering Clinicians with Medical Decision Transformers: A Framework for Sepsis Treatment

Aamer Abdul Rahman

Pranav Agarwal

Rita Noumeir

Philippe Jouvet

Vincent Michalski

Offline reinforcement learning has shown promise for solving tasks in safety-critical settings, such as clinical decision support. Its appli… (voir plus)cation, however, has been limited by the lack of interpretability and interactivity for clinicians. To address these challenges, we propose the medical decision transformer (MeDT), a novel and versatile framework based on the goal-conditioned reinforcement learning paradigm for sepsis treatment recommendation. MeDT uses the decision transformer architecture to learn a policy for drug dosage recommendation. During offline training, MeDT utilizes collected treatment trajectories to predict administered treatments for each time step, incorporating known treatment outcomes, target acuity scores, past treatment decisions, and current and past medical states. This analysis enables MeDT to capture complex dependencies among a patient's medical history, treatment decisions, outcomes, and short-term effects on stability. Our proposed conditioning uses acuity scores to address sparse reward issues and to facilitate clinician-model interactions, enhancing decision-making. Following training, MeDT can generate tailored treatment recommendations by conditioning on the desired positive outcome (survival) and user-specified short-term stability improvements. We carry out rigorous experiments on data from the MIMIC-III dataset and use off-policy evaluation to demonstrate that MeDT recommends interventions that outperform or are competitive with existing offline reinforcement learning methods while enabling a more interpretable, personalized and clinician-directed approach.

2024-07-28

ArXiv (prépublication)