arXiv:2602.06204v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is a standard tool for parameter-efficient finetuning of large models. While it induces a small memory footprint, its training dynamics can be surprisingly complex as they depend on several hyperparameters such as initialization, adapter rank, and learning rate. In particular, it is unclear how the optimal learning rate scales with adapter rank, which forces practitioners to re-tune the learning rate whenever the rank is changed. In this paper, we introduce […]

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Inverse reinforcement learning (IRL) recovers the loss function of a forward learner from its observed responses adaptive IRL aims to reconstruct the loss function of a forward learner by passively observing its gradients as it performs reinforcement learning (RL). This paper proposes a novel passive Langevin-based algorithm that achieves adaptive IRL. The key difficulty in adaptive IRL is that the required gradients in the passive algorithm are counterfactual, that is, they are conditioned on events of probability zero […]

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arXiv:2603.20264v1 Announce Type: new Abstract: How do LLMs compare with symbolic tools on program synthesis tasks? We investigate this question on several synthesis domains: LTL reactive synthesis, syntax-guided synthesis, distributed protocol synthesis, and recursive function synthesis. For each domain, we choose a state-of-the-art symbolic tool and compare it to an open-source, 32 billion parameter version of the Qwen LLM and the proprietary, frontier LLM GPT-5. We couple Qwen with a symbolic verifier and run it repeatedly until it […]

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We study supervisory switching control for partially-observed linear dynamical systems. The objective is to identify and deploy the best controller for the unknown system by periodically selecting among a collection of $N$ candidate controllers, some of which may destabilize the underlying system. While classical estimator-based supervisory control guarantees asymptotic stability, it lacks quantitative finite-time performance bounds. Conversely, current non-asymptotic methods in both online learning and system identification require restrictive assumptions that are incompatible in a control setting, such […]

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Image Source: https://www.technologynetworks.com/diagnostics/news/wealth-and-education-play-significant-role-in-heart-disease-risk-396976 Why ensemble methods generalize better than deep learning on clinical tabular data Heart disease continues to be the leading cause of death worldwide, responsible for millions of deaths every year. Despite advances in clinical diagnostics, early and accurate detection remains a persistent challenge. Traditional diagnostic procedures are often invasive, expensive, and heavily dependent on physician interpretation. This is where Machine Learning (ML) offers a compelling alternative. In this article, I present a comprehensive machine learning study […]

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