This work presents the first model extraction attack specifically designed for graph classification under strict black-box constraints, which uses model explanation outputs to guide Monte Carlo edge sensitivity estimation toward decision boundaries, with Hoeffding concentration guarantees on estimation accuracy.
Relay introduces a differentiable per-token channel that passes information between forward passes and is trained via truncated backpropagation through time (BPTT), demonstrating that state-of-the-art DLMs can be explicitly trained to relay latent information forward across decoding steps, advancing the performance-latency Pareto frontier.
Benjamin Rozonoyer, Jacopo Minniti, Dhruvesh Patel et al.· arXiv.org· 0 citations
Treatment allocation under budget constraints is a central challenge in digital advertising. The standard approach trains an offline uplift model on historical data, then solves a constrained optimization to allocate budget. This fails in cold-start settings where little historical data exists. We propose Budget-Constrained Causal Bandits (BCCB), an online framework that learns which users respond to ads while simultaneously spending the budget. BCCB unifies three components: learning individual-level treatment effects, exploring users whose response is uncertain, and pacing the budget over time. We derive the per-arrival decision rule as the KKT condition of a Lagrangian relaxation of the budgeted causal-allocation objective, providing a principled foundation for the algorithm. We evaluate on the Criteo Uplift dataset using 20 random seeds with paired statistical tests. Our central finding is a data-efficiency crossover at n = 7,500 historical observations (paired one-sided t-test, p = 0.043): below this threshold, offline pipelines either fail or produce unreliable allocations, while BCCB operates from the first user. BCCB exhibits 2-4x lower run-to-run variance than offline methods and outperforms all four online baselines (Thompson Sampling, budgeted Thompson Sampling, HTE Greedy, and Uplifting Bandits) at every budget level tested (p < 0.001). These results give practitioners a concrete decision rule for choosing between offline and online paradigms.
A self-supervised data enrichment method that leverages semantic clustering of report sentences that enrich the findings in the medical reports in the training set by adding positive/neutral observations from different clusters in a self-supervised manner.
Halil Ibrahim Gulluk, Olivier Gevaert· arXiv.org· 1 citation
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We propose a conjugate and calibrated Gaussian process (GP) model for multi-class classification by exploiting the geometry of the probability simplex. Our approach uses Aitchison geometry to map simplex-valued class probabilities to an unconstrained Euclidean representation, turning classification into a GP regression problem with fewer latent dimensions than standard multi-class GP classifiers. This yields conjugate inference and reliable predictive probabilities without relying on distributional approximations in the model construction. The method is compatible with standard sparse GP regression techniques, enabling scalable inference on larger datasets. Empirical results show well-calibrated and competitive performance across synthetic and real-world datasets.
Bernardo Williams, Harsha Vardhan Tetali, Arto Klami et al.· 0 citations
This work clarifies the trade-offs between KL divergence and Wasserstein metrics for the utility function and provides guidelines for selecting suitable criteria in practical BED applications.
A novel payoff-based learning scheme for distributed optimization in repeatedly-played strategic-form games and it is shown that payoff-dominant Nash equilibria are the only stochastically stable states.
Georgios C. Chasparis· European Control Conference· 1 citation
This work proposes 'One Model for All', a universal pre-training framework for EEG analysis across disparate datasets, and paves the way for more universal, scalable, and effective pre-trained models for diverse EEG analysis tasks.
Xiang Li, You Li, Yazhou Zhang· arXiv.org· 0 citations
RECAP (Robust Safety Alignment via Counter-Aligned Prefilling), a principled reinforcement learning (RL) method for post-training that explicitly teaches models to override flawed reasoning trajectories and reroute to safe and helpful responses, substantially improves safety and jailbreak robustness, reduces overrefusal, and preserves core reasoning capability.
Sheng-Hsuan Peng, E. Smith, Ivan Evtimov et al.· arXiv.org· 10 citations· ⚡2
This work demonstrates that a low-rank structure naturally emerges in the shifted successor measure, which captures the system dynamics after bypassing a few initial transitions, and establishes a connection between the necessary shift and the local mixing properties of the underlying dynamical system, which provides a natural way of selecting the shift.
Bastien Dubail, Stefan Stojanovic, Alexandre Proutière· Neural Information Processin...· 4 citations· ⚡1
The results demonstrate that SOM-based statistical replay offers a scalable, label-free, exemplar-free approach to class-incremental continual learning under standard task-boundary protocols.
Pujan Thapa, Alexander G. Ororbia, Travis J. Desell· 0 citations
Multi-sensorial media systems, including AR/VR, remote operation, and embodied AI, require visual grounding modules that remain reliable as sensing environments and application domains evolve. The Segment Anything Model (SAM) provides a strong foundation for dense visual segmentation, but its performance degrades on specialized and dynamically arriving domains such as medical imagery, camouflaged scenes, and shadow-dominant environments. Existing continual learning methods often rely on replay data or growing domain-specific modules, limiting compact deployment in evolving media pipelines. To address this issue, we propose RegCL, a non-replay continual adaptation framework that consolidates multi-domain segmentation knowledge into a single SAM adapter through incremental model merging. RegCL merges lightweight adaptation modules, e.g., LoRA-style AugModules, by optimizing prediction consistency between the merged model and domain-specific adapters while carrying forward compact historical feature statistics. Experiments across five heterogeneous segmentation datasets show that RegCL achieves strong retention and adaptation under domain-incremental learning, outperforming competitive non-replay continual learning and merging baselines. These results suggest that RegCL can serve as a compact visual adaptation component for evolving multi-sensorial media pipelines. The code is available at \href{https://github.com/Anderw-S/RegCL}{https://github.com/Anderw-S/RegCL}
Yuan-Chen Shu, Zhiwei Lin, Xiaoyu Zhou et al.· 0 citations
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
MIT News · Artificial Intelligence· news.mit.eduAug 24, 2026
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
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