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arXiv:2602.16216v1 Announce Type: new Abstract: Deep learning has improved automated electrocardiogram (ECG) classification, but limited insight into prediction reliability hinders its use in safety-critical settings....
arXiv:2602.16217v1 Announce Type: new Abstract: Surface extraction from implicit neural representations modelling a single class surface is a well-known task. However, there exist no surface...
arXiv:2602.16218v1 Announce Type: new Abstract: Bayesian quadrature is a probabilistic, model-based approach to numerical integration, the estimation of intractable integrals, or expectations. Although Bayesian quadrature...
arXiv:2602.16220v1 Announce Type: new Abstract: Modeling multiscale patterns is crucial for long-term time series forecasting (TSF). However, redundancy and noise in time series, together with...
arXiv:2602.16222v1 Announce Type: new Abstract: We investigate space-time trade-offs for population protocols in sparse interaction graphs. In complete interaction graphs, optimal space-time trade-offs are known...
arXiv:2602.16224v1 Announce Type: new Abstract: Time series data are prone to noise in various domains, and training samples may contain low-predictability patterns that deviate from...
arXiv:2602.16229v1 Announce Type: new Abstract: Learning latent actions from action-free video has emerged as a powerful paradigm for scaling up controllable world model learning. Latent...
arXiv:2602.16231v1 Announce Type: new Abstract: Large-scale video repositories are increasingly available for modern video understanding and generation tasks. However, transforming raw videos into high-quality, task-specific...
arXiv:2602.16233v1 Announce Type: new Abstract: Circuit cutting decomposes a large quantum circuit into a collection of smaller subcircuits. The outputs of these subcircuits are then...
arXiv:2602.16234v1 Announce Type: new Abstract: The study of learning in games typically assumes that each player always has access to all of their actions. However,...
arXiv:2602.16235v1 Announce Type: new Abstract: Mobile networks require safe optimization to adapt to changing conditions in traffic demand and signal transmission quality, in addition to...
arXiv:2602.16236v1 Announce Type: new Abstract: We revisit the classical problem of universal prediction of stochastic sequences with a finite time horizon $T$ known to the...
arXiv:2602.16238v1 Announce Type: new Abstract: We propose EasyControlEdge, adapting an image-generation foundation model to edge detection. In real-world edge detection (e.g., floor-plan walls, satellite roads/buildings,...
arXiv:2602.16240v1 Announce Type: new Abstract: Motivated by a wide range of applications in data mining and machine learning, we consider the problem of maximizing a...
arXiv:2602.16241v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used as automated annotators to scale dataset creation, yet their reliability as unbiased annotators--especially...
arXiv:2602.16245v1 Announce Type: new Abstract: Multimodal fusion frameworks, which integrate diverse medical imaging modalities (e.g., MRI, CT), have shown great potential in applications such as...
arXiv:2602.16246v1 Announce Type: new Abstract: Interactive large language model (LLM) agents operating via multi-turn dialogue and multi-step tool calling are increasingly used in production. Benchmarks...
arXiv:2602.16249v1 Announce Type: new Abstract: Self-supervised pretraining has transformed computer vision by enabling data-efficient fine-tuning, yet high-resolution training typically requires server-scale infrastructure, limiting in-domain foundation...
arXiv:2602.16251v1 Announce Type: new Abstract: Generative AI chatbots enable personalized problem-solving, but effective learning requires students to self-regulate both how they seek help and how...
arXiv:2602.16260v1 Announce Type: new Abstract: This paper addresses the problem of consensus tracking with fixed-time convergence, for leader-follower multi-agent systems with double-integrator dynamics, where only...
arXiv:2602.16264v1 Announce Type: new Abstract: In this work, we develop, for the first time, a supervised classification framework with class-dependent rewards (CDR) to predict $\geq$MM...
arXiv:2602.16268v1 Announce Type: new Abstract: Ring signatures are a powerful primitive that allows a member to sign on behalf of a group, without revealing their...
arXiv:2602.16274v1 Announce Type: new Abstract: We present the first high-probability regret bound for classical online Q-learning in infinite-horizon discounted Markov decision processes, without relying on...
arXiv:2602.16275v1 Announce Type: new Abstract: Symplectic integrators are the established standard for long-term simulations of nearly-integrable Hamiltonian systems due to their preservation of geometric structures....