Publications

Plausible Deniability Guarantees for Whistleblowers
Leo Richter
Matt J. Kusner
Whistleblowers are a key safeguard against organizational wrongdoing, but the threat of retaliation deters reporting. Existing whistleblower… (see more)-protection proposals lack formal privacy guarantees, and existing differential privacy mechanisms do not directly target the natural threat model -- one in which the audited organization itself observes auditor selection decisions and uses them to identify reporters. We formalize protection against a strong-adversary threat model as per-report
2026-07-14
arXiv (preprint)
doi.org
arxiv.org
The topology of adolescent mental health
Maria B. Jelen
Alexa Mousley
Kayson Fakhar
Estherina Trachtenberg
Yuankai He
Robert Kohler
Shambhavi Aggarwal
Varun Warrier
Danilo Bzdok
Sarah W. Yip
Duncan E. Astle
Abstract The increased vulnerability to mental health problems in adolescence is frequently reported but poorly understood, hampered by a ri… (see more)gid diagnostic system which fails to capture intertwining symptoms and only loosely aligns with biological axes of variability. Here, we reconceptualised the mental health symptoms of young adolescents in the ABCD cohort (N=11862) as a latent topology of overlapping symptom dimensions, using an unsupervised machine learning algorithm to establish how transdiagnostic dimensions co-occur and overlap within individuals. Combining this with a novel classification approach, we delineated zones within this landscape, within which specific profiles of symptoms were robustly represented. These data-driven profiles were leveraged to establish associated resting-state functional connectivity and genetic characteristics. In doing so we recaptured the commonly reported p- factor axis as well as further symptom-subtype dimensions. Gene ontology analysis revealed that shared neurobiological and cellular mechanisms embedded in both the genome and transcriptome may confer risk for psychopathology.
2026-07-14
medRxiv (preprint)
doi.org
Optimal energy trading in residential prosumer clusters via graphon mean field games
Mohamad Aziz
Hussein Suprême
Hanane Dagdougui
Antoine Lesage-Landry
2026-07-13
Electric Power Systems Research (published)
doi.org
Continual Test-Time Adaptation in Computer Vision: Methods, Benchmarks, and Future Directions
Sarthak Kumar Maharana
Shambhavi Mishra
Yunbei Zhang
shuaicheng niu
Taki Hasan Rafi
Jihun Hamm
Marco Pedersoli
Jose Dolz
Yunhui Guo
Deep neural nets achieve remarkable performance when training and test data share the same distribution, but this assumption frequently brea… (see more)ks in real-world deployment, where data undergoes continual distributional shifts. Continual Test-Time Adaptation (CTTA) addresses this challenge by adapting pretrained models to non-stationary target distributions on-the-fly, without access to source data or labeled targets, while mitigating two critical failure modes: catastrophic forgetting of source knowledge and error accumulation from noisy pseudo-labels over extended time horizons. In this comprehensive survey, we formally define the CTTA problem, analyze the diverse continual domain shift patterns that characterize different evaluation protocols, and propose a hierarchical taxonomy that categorizes existing methods into three families: optimization-based strategies (entropy minimization, pseudo-labeling, parameter restoration), parameter-efficient methods (normalization layer adaptation, adaptive parameter selection), and architecture-based approaches (teacher-student frameworks, adapters, visual prompting, masked modeling). We systematically review representative methods within each category and present comparative benchmarks and experimental results across standard evaluation settings. Finally, we discuss limitations of current approaches and highlight emerging research directions, including adaptation of foundation models and black-box systems, providing a roadmap for future research in robust continual test-time adaptation. We encourage visiting our repository at https://github.com/sarthaxxxxx/Awesome-Continual-Test-Time-Adaptation.
2026-07-12
Transactions on Machine Learning Research (accepted)
doi.org
openreview.net
Copositive Characterizations of Convex Hull Pricing
Madhusudan Ghosh
Antoine Lesage-Landry
Joshua Adam Taylor
Due to the nonconvex binary constraints of unit commitment (UC), no uniform linear pricing scheme supports the optimal dispatch. Convex hull… (see more) pricing (CHP) and copositive duality pricing (CDP) both address this problem. CHP derives the price from the subgradient of the value function of the convex hull relaxation of UC. CDP refers to several different pricing mechanisms that can be constructed from the dual multipliers of the completely positive programming reformulation. In this work, we define a centralized convex hull price over the joint feasible set of UC and prove that, under non-degeneracy, it coincides with the marginal copositive duality price. Numerical experiments on the Scarf example validate this equivalence and quantify the pricing gap introduced by the semidefinite restriction.
2026-07-12
arXiv (preprint)
doi.org
arxiv.org
From Expressivity to Sample Complexity: Narrow Teachers for Transformers via C-RASP
Michael Rizvi-Martel
Satwik Bhattamishra
Guillaume Rabusseau
Michael Hahn
A theoretical understanding of Transformers is crucial to better understand the capacities and limitations of large language models (LLMs). … (see more)There is much work analyzing the expressivity of attention-based models. By proposing handcrafted weights or using computational complexity arguments, a large amount of past theoretical works have sought to characterize which tasks are and which are not in the hypothesis class of Transformer models. However, little work investigates the learnability of such solutions. In this work, we make progress towards this goal. Inspired by recent loss landscape analysis work, we propose preliminary sample complexity bounds for learning C-RASP constructions with Transformers.
2026-07-12
arXiv (preprint)
doi.org
arxiv.org
Generative AI and Price Discrimination in the Housing Market
Jitsama Tanlamai
Warut Khern-am-nuai
Maxime C. Cohen
Housing discrimination has been recognized as an important societal issue for decades. While this issue can manifest in multiple ways, one o… (see more)f the most observed avenues is price discrimination, where houses in white-dominant neighborhoods are worth more than houses in minority-dominant neighborhoods that are otherwise similar. Prior studies have empirically documented such pricing discrimination and attributed it to human biases. In addition, recent studies have shown that issues of this kind are unlikely to be addressed by traditional AI models, even those specifically designed to address discrimination. In this paper, we first compare AI-generated versus human-generated housing prices using a sample of 284,749 U.S. properties. We then study the impact of generative AI in the context of price discrimination in the housing market and find that it can help alleviate this issue. Our mechanism exploration provides empirical evidence regarding underlying mechanisms that drive such a counter-intuitive result. Practical and policy implications are also discussed.
2026-07-12
Information Systems Research (published)
doi.org
Special issue dedicated to the International Symposium on Mathematical Programming (ISMP), Montréal, 2024
Youssef Diouane
Franklin Djeumou Fomeni
Alain Hertz
Dominique Orban
Courtney Paquette
2026-07-12
Mathematical Programming (published)
doi.org
World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning
Tong Nie
Yuewen Mei
Junlin He
Yihong Tang
Jian Sun
Wei Ma
Robust motion planning in dense traffic requires autonomous vehicles to interact in rare and safety-critical scenarios that are underreprese… (see more)nted in naturalistic driving data. Although adversarial training offers a feasible solution, existing methods often rely on external scenario generators, heuristic perturbations, or simulator-heavy rollouts, which makes them difficult to integrate with modern autoregressive planners. Here, we cast adversarially robust planner learning as a constrained min-max game and propose Adversarial World Modeling (AWM), a theoretically grounded multi-agent self-play fine-tuning framework. Since solving the exact game is intractable, AWM introduces a principled decoupled solver. In the inner minimization, the planner's predictive world model is converted into a role-conditioned adversary that learns sparse, scene-adaptive attack coalitions via counterfactual credit assignment. In the outer maximization, the ego planner optimizes a regret-aware robust best response against the frozen AWM, utilizing tail-risk weighting and reference-anchored trust regions to improve hard-case recovery while preserving nominal driving behavior. Experiments on the nuPlan and InterPlan benchmarks demonstrate that our method generates transferable adversarial interactions and yields a robust planner that achieves competitive closed-loop performance in both nominal and highly interactive long-tail scenarios. Theoretical analysis justifies the decoupled solver and the main optimization components.
2026-07-11
arXiv (preprint)
doi.org
arxiv.org
Co-evolution of self-replication and function in a digital primordial soup
Francesco Cicala
Eyvind Niklasson
Ettore Randazzo
Sami Boukortt
Alessio Basti
Mayalen Etcheverry
Rif A. Saurous
Ben Laurie
James Manyika
Blaise Agüera y Arcas
Blake A. Richards
While traditional evolutionary algorithms hard-code reproduction, self-replication can emerge spontaneously within digital ``primordial soup… (see more)s''. This paper investigates the co-evolution of this emergent self-replication alongside problem-solving capabilities. We initialize a population of random 32-byte Z80 assembly programs, requiring self-replication to arise purely through random assembly-level mutations and pairwise program interactions. To link these behaviors, we introduce a task-based validation step: correctly evaluating a polynomial raises a program's interaction probability above a baseline rate. Our experiments yield four primary findings. First, self-replication and mathematical problem-solving successfully co-evolve from initial randomness. Second, the pressure to compute accelerates the emergence of compact, robust reproductive architectures that preserve memory for task execution. Third, applying metabolic constraints increases the likelihood that programs evolve conditional halting, terminating early during validation while bypassing the halt during interaction to execute block-copy replication. Finally, when programs are partitioned into spatial task niches, spontaneous self-replication generates an emergent learning curriculum, utilizing simple solutions as stepping stones toward complex polynomials. Altogether, these results demonstrate an interactive feedback loop: environmental task demands actively shape the physical architecture of self-replication, while spontaneous replication alters the evolutionary trajectory of functional problem-solving.
2026-07-09
arXiv (preprint)
doi.org
arxiv.org
Density Evolution of Soft-Decision Collapsed Projection-Aggregation Decoding for Reed-Muller Codes over the BIAWGN Channel
Jiajie Li
Marvin Rübenacke
Warren J. Gross
Reed-Muller (RM) codes have been shown to achieve capacity over a range of channels, and recently proposed projection-aggregation (PA) decod… (see more)ing has been experimentally shown to achieve near-maximum-likelihood decoding performance. These recent achievements motivate theoretical research on PA decoding. In this work, we analyze the density function of the soft output from collapsed projection-aggregation (CPA) decoding for RM codes over the binary-input additive white Gaussian noise (BIAWGN) channel. We prove that soft-decision CPA decoding returns an exact marginal probability and is symmetric. Based on the analysis, we build a density evolution model for CPA decoding. To simplify the density evolution, we approximate the projection and the fast Hadamard transform decoding using hard-decision decoding. Simulation results over the BIAWGN channel show that our proposed density evolution model captures the fast reduction in the mean and the variance of the soft information returned from the CPA decoding, which qualitatively explains the decoding mechanism and the fast convergence speed of the CPA decoding. We perform an asymptotic analysis based on the proposed density evolution, and we show that CPA decoding can achieve a vanishing error probability for RM codes with a vanishing code rate.
2026-07-09
arXiv (preprint)
doi.org
arxiv.org
Exploring Test-time Scaling via Prediction Merging on Large-Scale Recommendation
Fuyuan Lyu
Z Chen
Jingyan Jiang
Lingjie Li
Xing Tang
xiuqiang He
Xue Liu
Inspired by the success of language models (LM), scaling up deep learning recommendation systems (DLRS) has become a recent trend in the com… (see more)munity. All previous methods tend to scale up the model parameters during training time. However, how to efficiently utilize and scale up computational resources during test time remains underexplored, which can prove to be a scaling-efficient approach and bring orthogonal improvements in LM domains. The key point in applying test-time scaling to DLRS lies in effectively generating diverse yet meaningful outputs for the same instance. We propose two ways: One is to explore the heterogeneity of different model architectures. The other is to utilize the randomness of model initialization under a homogeneous architecture. The evaluation is conducted across eight models, including both classic and SOTA models, on three benchmarks. Sufficient evidence proves the effectiveness of both solutions. We further prove that under the same inference budget, test-time scaling can outperform parameter scaling. Our test-time scaling can also be seamlessly accelerated with the increase in parallel servers when deployed online, without affecting the inference time on the user side. Code is available here. https://github.com/aTitye/TTS4CTR.
2026-07-09
ACM SIGIR Conference on Research and Development in Information Retrieval (published)
doi.org
arxiv.org