Tootfinder

Context-Aware Model-Based Reinforcement Learning for Autonomous Racing
Emran Yasser Moustafa, Ivana Dusparic
https://arxiv.org/abs/2510.11501 https://arxiv…

Context-Aware Model-Based Reinforcement Learning for Autonomous Racing
Autonomous vehicles have shown promising potential to be a groundbreaking technology for improving the safety of road users. For these vehicles, as well as many other safety-critical robotic technologies, to be deployed in real-world applications, we require algorithms that can generalize well to unseen scenarios and data. Model-based reinforcement learning algorithms (MBRL) have demonstrated state-of-the-art performance and data efficiency across a diverse set of domains. However, these algori…

Demystifying the Role of Rule-based Detection in AI Systems for Windows Malware Detection
Andrea Ponte, Luca Demetrio, Luca Oneto, Ivan Tesfai Ogbu, Battista Biggio, Fabio Roli
https://arxiv.org/abs/2508.09652

Demystifying the Role of Rule-based Detection in AI Systems for Windows Malware Detection
Malware detection increasingly relies on AI systems that integrate signature-based detection with machine learning. However, these components are typically developed and combined in isolation, missing opportunities to reduce data complexity and strengthen defenses against adversarial EXEmples, carefully crafted programs designed to evade detection. Hence, in this work we investigate the influence that signature-based detection exerts on model training, when they are included inside the training…

Automated Evolutionary Optimization for Resource-Efficient Neural Network Training
Ilia Revin, Leon Strelkov, Vadim A. Potemkin, Ivan Kireev, Andrey Savchenko
https://arxiv.org/abs/2510.09566

Automated Evolutionary Optimization for Resource-Efficient Neural Network Training
There are many critical challenges in optimizing neural network models, including distributed computing, compression techniques, and efficient training, regardless of their application to specific tasks. Solving such problems is crucial because the need for scalable and resource-efficient models is increasing. To address these challenges, we have developed a new automated machine learning (AutoML) framework, Parameter Efficient Training with Robust Automation (PETRA). It applies evolutionary op…

PAC Learnability in the Presence of Performativity
Ivan Kirev, Lyuben Baltadzhiev, Nikola Konstantinov
https://arxiv.org/abs/2510.08335 https://arxiv.org/p…

PAC Learnability in the Presence of Performativity
Following the wide-spread adoption of machine learning models in real-world applications, the phenomenon of performativity, i.e. model-dependent shifts in the test distribution, becomes increasingly prevalent. Unfortunately, since models are usually trained solely based on samples from the original (unshifted) distribution, this performative shift may lead to decreased test-time performance. In this paper, we study the question of whether and when performative binary classification problems are…

Universal super-resolution framework for imaging of quantum dots
Dominik Va\v{s}inka, Jaewon Lee, Charlie Stalker, Victor Mitryakhin, Ivan Solovev, Sven Stephan, Sven H\"ofling, Falk Eilenberger, Seth Ariel Tongay, Christian Schneider, Miroslav Je\v{z}ek, Ana Predojevi\'c
https://arxiv.org/abs/2510.06076

Universal super-resolution framework for imaging of quantum dots
We present a universal deep-learning method that reconstructs super-resolved images of quantum emitters from a single camera frame measurement. Trained on physics-based synthetic data spanning diverse point-spread functions, aberrations, and noise, the network generalizes across experimental conditions without system-specific retraining. We validate the approach on low- and high-density In(Ga)As quantum dots and strain-induced dots in 2D monolayer WSe$_2$, resolving overlapping emitters even un…

Surya - Foundation Model for Heliophysics / SuryaBench - Benchmark Dataset for Advancing Machine Learning in Heliophysics and #SpaceWeather Prediction: https://arxiv.org/abs/2508.14112 / https://arxiv.org/abs/2508.14107 -> Here comes a foundation model for the Sun: https://research.ibm.com/blog/surya-heliophysics-ai-model-sun?previewToken=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpZCI6MTEsImlhdCI6MTc1NTYyMTUwOSwiZXhwIjoxNzU1ODgwNzA5LCJzdWIiOiI0NDY1In0.pv2-edPifhET2uDPQB4hsp4HsBzhZycvMcFlZpjFQKA -> NASA and IBM Unveil AI That Helps Scientists Forecast Solar Storms: https://gizmodo.com/nasa-and-ibm-unveil-ai-that-helps-scientists-forecast-solar-storms-2000645123 - experts recently identified major gaps in their ability to forecast space weather; Surya aims to fill them.

IBN: An Interpretable Bidirectional-Modeling Network for Multivariate Time Series Forecasting with Variable Missing
Shusen Ma, Tianhao Zhang, Qijiu Xia, Yun-Bo Zhao
https://arxiv.org/abs/2509.07725

IBN: An Interpretable Bidirectional-Modeling Network for Multivariate Time Series Forecasting with Variable Missing
Multivariate time series forecasting (MTSF) often faces challenges from missing variables, which hinder conventional spatial-temporal graph neural networks in modeling inter-variable correlations. While GinAR addresses variable missing using attention-based imputation and adaptive graph learning for the first time, it lacks interpretability and fails to capture more latent temporal patterns due to its simple recursive units (RUs). To overcome these limitations, we propose the Interpretable Bidi…

RL Is a Hammer and LLMs Are Nails: A Simple Reinforcement Learning Recipe for Strong Prompt Injection
Yuxin Wen, Arman Zharmagambetov, Ivan Evtimov, Narine Kokhlikyan, Tom Goldstein, Kamalika Chaudhuri, Chuan Guo
https://arxiv.org/abs/2510.04885

RL Is a Hammer and LLMs Are Nails: A Simple Reinforcement Learning Recipe for Strong Prompt Injection
Prompt injection poses a serious threat to the reliability and safety of LLM agents. Recent defenses against prompt injection, such as Instruction Hierarchy and SecAlign, have shown notable robustness against static attacks. However, to more thoroughly evaluate the robustness of these defenses, it is arguably necessary to employ strong attacks such as automated red-teaming. To this end, we introduce RL-Hammer, a simple recipe for training attacker models that automatically learn to perform stro…

Unveiling the Role of Data Uncertainty in Tabular Deep Learning
Nikolay Kartashev, Ivan Rubachev, Artem Babenko
https://arxiv.org/abs/2509.04430 https://ar…

Unveiling the Role of Data Uncertainty in Tabular Deep Learning
Recent advancements in tabular deep learning have demonstrated exceptional practical performance, yet the field often lacks a clear understanding of why these techniques actually succeed. To address this gap, our paper highlights the importance of the concept of data uncertainty for explaining the effectiveness of the recent tabular DL methods. In particular, we reveal that the success of many beneficial design choices in tabular DL, such as numerical feature embeddings, retrieval-augmented mod…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- Imputation-free Learning of Tabular Data with Missing Values using Incremental Feature Partitions...
Manar D. Samad, Kazi Fuad B. Akhter, Shourav B. Rabbani, Ibna Kowsar