Publications

Different Horses for Different Courses: Comparing Bias Mitigation Algorithms in ML

Prakhar Ganeesh

Usman Gohar

Lu Cheng

With fairness concerns gaining significant attention in Machine Learning (ML), several bias mitigation techniques have been proposed, often … (voir plus)compared against each other to find the best method. These benchmarking efforts tend to use a common setup for evaluation under the assumption that providing a uniform environment ensures a fair comparison. However, bias mitigation techniques are sensitive to hyperparameter choices, random seeds, feature selection, etc., meaning that comparison on just one setting can unfairly favour certain algorithms. In this work, we show significant variance in fairness achieved by several algorithms and the influence of the learning pipeline on fairness scores. We highlight that most bias mitigation techniques can achieve comparable performance, given the freedom to perform hyperparameter optimization, suggesting that the choice of the evaluation parameters-rather than the mitigation technique itself-can sometimes create the perceived superiority of one method over another. We hope our work encourages future research on how various choices in the lifecycle of developing an algorithm impact fairness, and trends that guide the selection of appropriate algorithms.

2025-04-23

Proceedings of the Algorithmic Fairness Through the Lens of Metrics and Evaluation (publié)

doi.org

arxiv.org

Distilling semantically aware orders for autoregressive image generation

Rishav Pramanik

Antoine Poupon

Juan A. Rodriguez

Masih Aminbeidokhti

David Vazquez

Chris Pal

Zhaozheng Yin

Marco Pedersoli

2025-04-23

ArXiv (prépublication)

arxiv.org

Distilling semantically aware orders for autoregressive image generation

Rishav Pramanik

Antoine Poupon

Juan A. Rodriguez

Masih Aminbeidokhti

David Vazquez

Chris Pal

Zhaozheng Yin

Marco Pedersoli

2025-04-23

ArXiv (prépublication)

doi.org

arxiv.org

Fair Resource Allocation in Weakly Coupled Markov Decision Processes

Xiaohui Tu

Yossiri Adulyasak

Nima Akbarzadeh

Erick Delage

We consider fair resource allocation in sequential decision-making environments modeled as weakly coupled Markov decision processes, where r… (voir plus)esource constraints couple the action spaces of

2025-04-23

Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (publié)

proceedings.mlr.press

openreview.net

Feasible Learning

Juan Ramirez

Ignacio Hounie

Juan Elenter

Jose Gallego-Posada

Meraj Hashemizadeh

Alejandro Ribeiro

Simon Lacoste-Julien

We introduce Feasible Learning (FL), a sample-centric learning paradigm where models are trained by solving a feasibility problem that bound… (voir plus)s the loss for each training sample. In contrast to the ubiquitous Empirical Risk Minimization (ERM) framework, which optimizes for average performance, FL demands satisfactory performance \emph{on every individual data point}. Since any model that meets the prescribed performance threshold is a valid FL solution, the choice of optimization algorithm and its dynamics play a crucial role in shaping the properties of the resulting solutions. In particular, we study a primal-dual approach which dynamically re-weights the importance of each sample during training. To address the challenge of setting a meaningful threshold in practice, we introduce a relaxation of FL that incorporates slack variables of minimal norm. Our empirical analysis, spanning image classification, age regression, and preference optimization in large language models, demonstrates that models trained via FL can learn from data while displaying improved tail behavior compared to ERM, with only a marginal impact on average performance.

2025-04-23

Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (publié)

proceedings.mlr.press

A flaw in using pre-trained pLLMs in protein-protein interaction inference models

Joseph Szymborski

Amin Emad

With the growing pervasiveness of pre-trained protein large language models (pLLMs), pLLM-based methods are increasingly being put forward f… (voir plus)or the protein-protein interaction (PPI) inference task. Here, we identify and confirm that existing pre-trained pLLMs are a source of data leakage for the downstream PPI task. We characterize the extent of the data leakage problem by training and comparing small and efficient pLLMs on a dataset that controls for data leakage (“strict”) with one that does not (“non-strict”). While data leakage from pre-trained pLLMs cause measurable inflation of testing scores, we find that this does not necessarily extend to other, non-paired biological tasks such as protein keyword annotation. Further, we find no connection between the context-lengths of pLLMs and the performance of pLLM-based PPI inference methods on proteins with sequence lengths that surpass it. Furthermore, we show that pLLM-based and non-pLLM-based models fail to generalize in tasks such as prediction of the human-SARS-CoV-2 PPIs or the effect of point mutations on binding-affinities. This study demonstrates the importance of extending existing protocols for the evaluation of pLLM-based models applied to paired biological datasets and identifies areas of weakness of current pLLM models.

2025-04-23

bioRxiv (prépublication)

doi.org

Multilingual Hallucination Gaps

Cléa Chataigner

Afaf Taïk

Golnoosh Farnadi

2025-04-23

Proceedings of the Algorithmic Fairness Through the Lens of Metrics and Evaluation (publié)

proceedings.mlr.press

Performative Prediction on Games and Mechanism Design

Antonio Gois

Mehrnaz Mofakhami

Fernando P. Santos

Gauthier Gidel

Simon Lacoste-Julien

2025-04-23

Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (publié)

doi.org

openreview.net

Planning and Learning in Risk-Aware Restless Multi-Arm Bandits

Nima Akbarzadeh

Yossiri Adulyasak

Erick Delage

2025-04-23

Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (publié)

proceedings.mlr.press

openreview.net

Privacy-Preserving Group Fairness in Cross-Device Federated Learning

Sikha Pentyala

Nicola Neophytou

Anderson Nascimento

Martine De Cock

Golnoosh Farnadi

Group fairness ensures that the outcome of machine learning (ML) based decision making systems are notbiased towards a certain group of peop… (voir plus)le defined by a sensitive attribute such as gender or ethnicity. Achievinggroup fairness in Federated Learning (FL) is challenging because mitigating bias inherently requires usingthe sensitive attribute values of all clients, while FL is aimed precisely at protecting privacy by not givingaccess to the clients’ data. As we show in this paper, this conflict between fairness and privacy in FL can beresolved by combining FL with Secure Multiparty Computation (MPC) and Differential Privacy (DP). Tothis end, we propose a privacy-preserving approach to calculate group fairness notions in the cross-device FLsetting. Then, we propose two bias mitigation pre-processing and post-processing techniques in cross-deviceFL under formal privacy guarantees, without requiring the clients to disclose their sensitive attribute values.Empirical evaluations on real world datasets demonstrate the effectiveness of our solution to train fair andaccurate ML models in federated cross-device setups with privacy guarantees to the users.

2025-04-23

Proceedings of the Algorithmic Fairness Through the Lens of Metrics and Evaluation (publié)

proceedings.mlr.press

Q-learning for Quantile MDPs: A Decomposition, Performance, and Convergence Analysis

Jia Lin Hau

Erick Delage

Esther Derman

Mohammad Ghavamzadeh

Marek Petrik

2025-04-23

Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (publié)

proceedings.mlr.press