Publications

Mitigating Disparate Impact of Differential Privacy in Federated Learning through Robust Clustering
Saber Malekmohammadi
Afaf Taïk
Golnoosh Farnadi
Federated Learning (FL) is a decentralized machine learning (ML) approach that keeps data localized and often incorporates Differential Priv… (voir plus)acy (DP) to enhance privacy guarantees. Similar to previous work on DP in ML, we observed that differentially private federated learning (DPFL) introduces performance disparities, particularly affecting minority groups. Recent work has attempted to address performance fairness in vanilla FL through clustering, but this method remains sensitive and prone to errors, which are further exacerbated by the DP noise in DPFL. To fill this gap, in this paper, we propose a novel clustered DPFL algorithm designed to effectively identify clients' clusters in highly heterogeneous settings while maintaining high accuracy with DP guarantees. To this end, we propose to cluster clients based on both their model updates and training loss values. Our proposed approach also addresses the server's uncertainties in clustering clients' model updates by employing larger batch sizes along with Gaussian Mixture Model (GMM) to alleviate the impact of noise and potential clustering errors, especially in privacy-sensitive scenarios. We provide theoretical analysis of the effectiveness of our proposed approach. We also extensively evaluate our approach across diverse data distributions and privacy budgets and show its effectiveness in mitigating the disparate impact of DP in FL settings with a small computational cost.
2025-10-06
TMLR (accepté)
doi.org
openreview.net
RoboArena: Distributed Real-World Evaluation of Generalist Robot Policies
Pranav Atreya
Karl Pertsch
Tony Lee
Moo Jin Kim
Arhan Jain
Artur Kuramshin
Cyrus Neary
Edward S. Hu
Kanav Arora
Kirsty Ellis
Luca Macesanu
Matthew Leonard
Meedeum Cho
Özgür Aslan
Shivin Dass
Tony Wang
Xingfang Yuan
Abhishek Gupta
Dinesh Jayaraman
Glen Berseth … (voir 6 de plus)
Kostas Daniilidis
Roberto Martín-Martín
Youngwoon Lee
Percy Liang
Chelsea Finn
Sergey Levine
2025-10-06
Proceedings of The 8th Conference on Robot Learning (publié)
proceedings.mlr.press
proceedings.mlr.press
In silico neutron relative biological effectiveness estimations for Pre-DNA repair and post-DNA repair endpoints
Nicolas Desjardins
J. Kildea
A comprehensive understanding of the energy-dependent stochastic risks associated with neutron exposure is crucial to develop robust radiopr… (voir plus)otection systems. However, the scarcity of experimental data presents significant challenges in this domain. Track-structure Monte Carlo (TSMC) simulations with DNA models have demonstrated their potential to further our fundamental understanding of neutron-induced stochastic risks. To date, most TSMC studies on the relative biological effectiveness (RBE) of neutrons have focused on various types of DNA damage clusters defined using base pair distances. In this study, we extend these methodologies by incorporating the simulation of non-homologous end joining DNA repair in order to evaluate the RBE of neutrons for misrepairs. To achieve this, we adapted our previously published Monte Carlo DNA damage simulation pipeline, which combines condensed-history and TSMC methods, to support the standard DNA damage data format. This adaptation enabled seamless integration of neutron-induced DNA damage results with the DNA mechanistic repair simulator toolkit. Additionally, we developed a clustering algorithm that reproduces pre-repair endpoints studied in prior works, as well as novel damage clusters based on Euclidean distances. The neutron RBE for misrepairs obtained in this study exhibits a qualitatively similar shape as the RBE obtained for previously reported pre-repair endpoints. However, it peaks higher, reaching a maximum RBE value of 23(1) at a neutron energy of 0.5 MeV. Furthermore, we found that misrepair outcomes were better reproduced using the pre-repair endpoint defined with the Euclidean distance between double-strand breaks rather than with previously published pre-repair endpoints based on base-pair distances. The optimal maximal Euclidean distances were 18 nm for 0.5 MeV neutrons and 60 nm for 250 keV photons. Although this may indicate that Euclidean-distance-based clustering more accurately reflects the DNA damage configurations that lead to misrepairs, the fact that neutrons and photons require different distances raises doubts on whether a single, universal pre-repair endpoint can used as a stand-in for larger-scale aberrations across all radiation qualities.
2025-10-06
bioRxiv (prépublication)
doi.org
Addressing Concept Mislabeling in Concept Bottleneck Models Through Preference Optimization
Emiliano Penaloza
Tianyue H. Zhang
Laurent Charlin
Mateo Espinosa Zarlenga
Concept Bottleneck Models (CBMs) propose to enhance the trustworthiness of AI systems by constraining their decisions on a set of human-unde… (voir plus)rstandable concepts. However, CBMs typically assume that datasets contain accurate concept labels-an assumption often violated in practice, which we show can significantly degrade performance (by 25% in some cases). To address this, we introduce the Concept Preference Optimization (CPO) objective, a new loss function based on Direct Preference Optimization, which effectively mitigates the negative impact of concept mislabeling on CBM performance. We provide an analysis of key properties of the CPO objective, showing it directly optimizes for the concept's posterior distribution, and contrast it against Binary Cross Entropy (BCE), demonstrating that CPO is inherently less sensitive to concept noise. We empirically confirm our analysis by finding that CPO consistently outperforms BCE on three real-world datasets, both with and without added label noise. We make our code available on Github.
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Aligning Protein Conformation Ensemble Generation with Physical Feedback
Jiarui Lu
Xiaoyin Chen
Stephen Z. Lu
Aurelie Lozano
Vijil Chenthamarakshan
Payel Das
Jian Tang
Protein dynamics play a crucial role in protein biological functions and properties, and their traditional study typically relies on time-co… (voir plus)nsuming molecular dynamics (MD) simulations conducted in silico. Recent advances in generative modeling, particularly denoising diffusion models, have enabled efficient accurate protein structure prediction and conformation sampling by learning distributions over crystallographic structures. However, effectively integrating physical supervision into these data-driven approaches remains challenging, as standard energy-based objectives often lead to intractable optimization. In this paper, we introduce Energy-based Alignment (EBA), a method that aligns generative models with feedback from physical models, efficiently calibrating them to appropriately balance conformational states based on their energy differences. Experimental results on the MD ensemble benchmark demonstrate that EBA achieves state-of-the-art performance in generating high-quality protein ensembles. By improving the physical plausibility of generated structures, our approach enhances model predictions and holds promise for applications in structural biology and drug discovery.
2025-10-05
International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Calibrated Value-Aware Model Learning with Probabilistic Environment Models
Claas Voelcker
Anastasiia Pedan
Arash Ahmadian
Romina Abachi
Igor Gilitschenski
Amir-massoud Farahmand
The idea of value-aware model learning, that models should produce accurate value estimates, has gained prominence in model-based reinforcem… (voir plus)ent learning. The MuZero loss, which penalizes a model's value function prediction compared to the ground-truth value function, has been utilized in several prominent empirical works in the literature. However, theoretical investigation into its strengths and weaknesses is limited. In this paper, we analyze the family of value-aware model learning losses, which includes the popular MuZero loss. We show that these losses, as normally used, are uncalibrated surrogate losses, which means that they do not always recover the correct model and value function. Building on this insight, we propose corrections to solve this issue. Furthermore, we investigate the interplay between the loss calibration, latent model architectures, and auxiliary losses that are commonly employed when training MuZero-style agents. We show that while deterministic models can be sufficient to predict accurate values, learning calibrated stochastic models is still advantageous.
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Categorical Distributional Reinforcement Learning with Kullback-Leibler Divergence: Convergence and Asymptotics
Tyler Kastner
Mark Rowland
Yunhao Tang
Murat A Erdogdu
Amir-massoud Farahmand
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (publié)
proceedings.mlr.press
proceedings.mlr.press
Compositional Risk Minimization
Divyat Mahajan
Mohammad Pezeshki
Charles Arnal
Ioannis Mitliagkas
Kartik Ahuja
Pascal Vincent
Compositional generalization is a crucial step towards developing data-efficient intelligent machines that generalize in human-like ways. In… (voir plus) this work, we tackle a challenging form of distribution shift, termed compositional shift, where some attribute combinations are completely absent at training but present in the test distribution. This shift tests the model's ability to generalize compositionally to novel attribute combinations in discriminative tasks. We model the data with flexible additive energy distributions, where each energy term represents an attribute, and derive a simple alternative to empirical risk minimization termed compositional risk minimization (CRM). We first train an additive energy classifier to predict the multiple attributes and then adjust this classifier to tackle compositional shifts. We provide an extensive theoretical analysis of CRM, where we show that our proposal extrapolates to special affine hulls of seen attribute combinations. Empirical evaluations on benchmark datasets confirms the improved robustness of CRM compared to other methods from the literature designed to tackle various forms of subpopulation shifts.
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detection
Karish Grover
Geoff Gordon
Christos Faloutsos
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (publié)
proceedings.mlr.press
proceedings.mlr.press
Discovering Symbolic Cognitive Models from Human and Animal Behavior
Pablo Samuel Castro
Nenad Tomasev
Ankit Anand
Navodita Sharma
Rishika Mohanta
Aparna Dev
Kuba Perlin
Siddhant Jain
Kyle Levin
Noemi Elteto
Will Dabney
Alexander Novikov
Glenn C Turner
Maria K Eckstein
Nathaniel D. Daw
Kevin J Miller
Kim Stachenfeld
Symbolic models play a key role in cognitive science, expressing computationally precise hypotheses about how the brain implements a cogniti… (voir plus)ve process. Identifying an appropriate model typically requires a great deal of effort and ingenuity on the part of a human scientist. Here, we adapt FunSearch (Romera-Paredes et al. 2024), a recently developed tool that uses Large Language Models (LLMs) in an evolutionary algorithm, to automatically discover symbolic cognitive models that accurately capture human and animal behavior. We consider datasets from three species performing a classic reward-learning task that has been the focus of substantial modeling effort, and find that the discovered programs outperform state-of-the-art cognitive models for each. The discovered programs can readily be interpreted as hypotheses about human and animal cognition, instantiating interpretable symbolic learning and decision-making algorithms. Broadly, these results demonstrate the viability of using LLM-powered program synthesis to propose novel scientific hypotheses regarding mechanisms of human and animal cognition.
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (publié)
proceedings.mlr.press
proceedings.mlr.press
Egalitarian Gradient Descent: A Simple Approach to Accelerated Grokking
Ali Saheb Pasand
Elvis Dopgima Dohmatob
Grokking is the phenomenon whereby, unlike the training performance, which peaks early in the training process, the test/generalization perf… (voir plus)ormance of a model stagnates over arbitrarily many epochs and then suddenly jumps to usually close to perfect levels. In practice, it is desirable to reduce the length of such plateaus, that is to make the learning process"grok"faster. In this work, we provide new insights into grokking. First, we show both empirically and theoretically that grokking can be induced by asymmetric speeds of (stochastic) gradient descent, along different principal (i.e singular directions) of the gradients. We then propose a simple modification that normalizes the gradients so that dynamics along all the principal directions evolves at exactly the same speed. Then, we establish that this modified method, which we call egalitarian gradient descent (EGD) and can be seen as a carefully modified form of natural gradient descent, groks much faster. In fact, in some cases the stagnation is completely removed. Finally, we empirically show that on classical arithmetic problems such as modular addition and sparse parity problem which this stagnation has been widely observed and intensively studied, that our proposed method eliminates the plateaus.
2025-10-05
ArXiv (prépublication)
doi.org
arxiv.org
From Language Models over Tokens to Language Models over Characters
Tim Vieira
Benjamin LeBrun
Mario Giulianelli
Juan Luis Gastaldi
Brian DuSell
John Terilla
Timothy J. O'Donnell
Ryan Cotterell
Modern language models are internally -- and mathematically -- distributions over …
2025-10-05
Proceedings of the 42nd International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press