304 papers
This paper introduces ContextBench, a benchmark for evaluating methods that generate fluent inputs to trigger specific latent features in language models, and demonstrates that enhanced Evolutionary Prompt Optimization variants achieve state-of-the-art performance in balancing elicitation strength with linguistic fluency.
The paper proposes Iterative Quantum Feature Maps (IQFMs), a hybrid quantum-classical framework that constructs deep architectures by iteratively connecting shallow, noise-resilient quantum feature maps with classically computed weights to mitigate hardware limitations and achieve performance comparable to classical neural networks without optimizing variational quantum parameters.
This paper proposes a machine learning approach that learns data-driven, structure-preserving (symplectic and time-reversible) maps equivalent to the mechanical action of a system, enabling accurate long-time-step molecular dynamics simulations that eliminate the energy conservation and equipartition artifacts typical of non-structure-preserving ML predictors.
This paper introduces Kernel VICReg, a novel self-supervised learning framework that extends the VICReg objective into a Reproducing Kernel Hilbert Space to capture nonlinear dependencies and improve representation learning performance on datasets with complex geometric structures.
This paper introduces the Tensor Atomic Cluster Expansion (TACE), a unified atomistic machine learning framework that employs irreducible Cartesian tensors to efficiently model both scalar and tensorial observables without complex angular-momentum coupling, demonstrating robust accuracy and scalability across diverse chemical systems and tasks.
This paper proposes a sequential "Clusters-of-Centres" algorithm that utilizes multivariate Cochran-type tests on summary statistics to identify and merge homogeneous groups in multi-centre studies, establishing asymptotic distributions and proving that a multi-round bootstrap variant can recover the true centre partition with high probability.
This paper introduces Self-Speculative Masked Diffusions, a novel generative model for discrete data that leverages causal attention and integrated speculative sampling to produce non-factorized predictions, thereby achieving a roughly 2x reduction in neural network forward passes compared to standard masked diffusion models.
This paper demonstrates that injecting external verification into synthetic data retraining can prevent model collapse and yield near-term improvements, though theoretical analysis and experiments across linear regression, VAEs, and LLMs show that long-term performance ultimately converges to the verifier's knowledge center and may plateau or decline if the verifier is imperfect.
The paper proposes CADM, a cluster-customized adaptive distance metric that dynamically adjusts distance measurements based on the unique attribute distributions within each cluster to improve categorical and mixed data clustering performance.
The paper introduces DAISI, a scalable data assimilation algorithm that leverages flow-based generative models with a novel inverse-sampling step to integrate forecast information and guide conditional sampling, enabling accurate filtering in complex, nonlinear systems where traditional Gaussian-based methods fail.
This paper proposes a novel global sensitivity analysis method based on Individual Conditional Expectation (ICE) curves that overcomes the limitations of traditional Partial Dependence Plots (PDPs) in capturing input interactions, offering a mathematically proven, more informative metric for explainable machine learning in engineering design.
This paper proposes a prior information-based distributionally robust individualized treatment rule (PDRO-ITR) that integrates multi-source data to address posterior shift by maximizing the worst-case policy value over a covariate-dependent uncertainty set, thereby ensuring robust performance and achieving superior results in simulations and real-world applications.
This paper proposes a prediction-powered conditional inference method that leverages abundant unlabeled data and machine learning predictors to construct valid, variance-reduced confidence intervals for conditional functionals at fixed test points without requiring parametric models, while establishing rigorous nonasymptotic error bounds and asymptotic normality.
This paper introduces behavior-decomposed linear dynamical systems (b-dLDS), a novel modeling approach that disentangles behavior-related neural dynamics from internal computations in large-scale brain recordings, demonstrating superior performance over existing supervised models and successfully scaling to tens of thousands of neurons in zebrafish hindbrain data.
This paper demonstrates that in random feature ridge regression, a strong student model trained on imperfect labels from a weak teacher can achieve substantially improved scaling laws and even reach minimax optimal rates, regardless of whether the teacher's own test error decays with sample size.
This paper proposes "Artificial Replay," a new experimental design that reuses recorded rewards from a single policy trajectory to enable unbiased, cost-effective, and low-variance comparisons between multi-armed bandit algorithms, thereby significantly reducing the number of required user interactions compared to traditional independent restarts.
This paper establishes the first large deviation principle for convolutional neural networks in the infinite-channel regime, characterizing the asymptotic behavior of conditional covariance matrices and posterior distributions under Gaussian priors while also providing streamlined proofs for their Gaussian equivalence and concentration.
This paper improves the known bounds for agnostic learning of concept classes with bounded Gaussian surface area by demonstrating that a polynomial degree of suffices for -approximation, thereby yielding near-optimal complexity for learning polynomial threshold functions in the statistical query model.
This paper demonstrates that predictive coding graphs constitute a mathematical superset of feedforward neural networks, thereby strengthening their theoretical foundation in machine learning and highlighting the importance of network topology.
This paper analyzes the gradient flow dynamics of the value-softmax model, a core component of self-attention, to demonstrate that training inherently drives the system toward low-entropy solutions, thereby providing a theoretical explanation for empirical phenomena like attention sinks and massive activations.