2043 papers
This book reinterprets generative AI, specifically through flow matching, as a statistical framework for nonparametric distribution learning that enables principled inference for tasks like missing-data imputation and causal analysis by integrating generative models with double/debiased machine learning techniques to ensure inferential validity.
This paper proposes a data-driven robust Markov decision process framework for Borel spaces with unknown disturbance distributions, utilizing ambiguity sets defined by distance functions to establish finite-sample performance guarantees, probabilistic convergence rates, and out-of-distribution bounds that empirical MDPs fail to provide.
This survey introduces reinforcement learning to economists as a flexible, sample-based extension of dynamic programming capable of solving high-dimensional economic models, while critically examining its practical limitations such as sample inefficiency, sensitivity to hyperparameters, and reliance on accurate simulators.
This thesis addresses the problem of overconfident conclusions in simulation-based inference by introducing a "balancing" regularization technique and a novel Bayesian neural network prior to ensure more reliable and calibrated statistical approximations.
This paper introduces ULFS-KDPE, a novel kernel-based estimator that achieves semiparametric efficiency for pathwise differentiable parameters in nonparametric models by constructing a data-adaptive debiasing flow via a universal least favorable submodel, thereby eliminating the need for explicit efficient influence function derivation while ensuring rigorous theoretical guarantees and computational tractability.
This paper proposes a hierarchical reinforcement learning framework that jointly optimizes antenna tilt angles and the data collection ratio between a physical network and its digital twin to maximize user data rates while minimizing communication overhead and delay.
The paper introduces MedCBR, a novel framework that integrates clinical guidelines with vision-language models to enhance the interpretability and accuracy of medical image diagnosis by transforming visual features into guideline-conformant concepts and structured clinical narratives.
This paper demonstrates that channel-centric models, including ray-tracing simulators, fail to accurately predict end-to-end throughput in private 5G networks due to systematic over-estimation of MIMO spatial layers, whereas data-driven Gaussian process models trained on direct measurements provide significantly more reliable predictions for communication-aware robot planning.
This paper proposes \textsc{applv}, a novel framework that leverages Vision-Language-Action models to dynamically predict and adapt classical planner parameters, thereby achieving superior navigation performance and generalization in highly constrained environments compared to existing methods.
The paper presents Midicoth, a lossless compression system that enhances online probability estimation by applying a lightweight, multi-stage micro-diffusion denoising layer to correct systematic biases in adaptive statistical models through a data-efficient, binary-tree decomposition of byte predictions.
This paper establishes a fundamental trilemma in AI safety, proving that no verification procedure can simultaneously guarantee soundness, generality, and tractability, thereby demonstrating that formal alignment certification is impossible without relaxing at least one of these critical properties.
This paper presents a physics-informed neural network (PINN) framework that robustly reconstructs hidden state variables and estimates biophysical parameters in multiscale neuronal models using only partial, noisy voltage observations, effectively overcoming the convergence failures and sensitivity issues common in traditional numerical methods.
The AetherFloat Family introduces a novel block-scale-free, quad-radix floating-point architecture that eliminates the hardware overhead of dynamic scaling and IEEE 754 inefficiencies, achieving significant area, power, and latency reductions in AI accelerators through explicit mantissas, base-4 scaling, and stochastic rounding.
This paper introduces Hebbian-Oscillatory Co-Learning (HOC-L), a unified two-timescale framework that integrates hyperbolic sparse geometry with Kuramoto-based phase synchronization to enable connectivity consolidation only when phase coherence signals meaningful patterns, thereby achieving provable convergence and complexity in bio-inspired neural architectures.
This paper demonstrates that synergistically integrating Supervised Contrastive Learning, Hopfield networks, and Hierarchical Gated Recurrent Networks into Spiking Neural Networks achieves optimal neuromorphic vision performance on N-MNIST by balancing accuracy, energy efficiency, and structured neuronal clustering, rather than relying on isolated architectural optimizations.
This paper presents a data-rate-aware CNN accelerator architecture for FPGAs that utilizes multi-pixel processing and design-space exploration to optimize hardware utilization and resource efficiency across varying data rates, thereby enabling the efficient implementation of complex CNNs on a single device.
This paper reviews the landscape of ultra-low-power edge and in-sensor AI processors and empirically benchmarks a segmentation model on GAP9, STM32N6, and Sony IMX500 platforms to demonstrate that while in-sensor processing offers superior energy-delay performance, different architectures provide distinct trade-offs between latency, energy efficiency, and power budgets.
KernelCraft introduces the first benchmark evaluating agentic LLM systems that use feedback-driven workflows to automatically generate and optimize low-level kernels for emerging hardware with novel ISAs, demonstrating their ability to produce valid, high-performance code that rivals or exceeds traditional compiler baselines.
The paper introduces Task-Aware Modulation with Representation Learning (TAM-RL), a novel framework that combines spatio-temporal representation learning with physically grounded constraints to significantly improve the accuracy and generalizability of global terrestrial carbon flux estimates compared to existing state-of-the-art methods.
This paper introduces a family of mean-normalized matrix operator norms to derive width-independent smoothness bounds for deep neural networks, leading to the development of MOGA, a row/column-normalized optimizer that enables stable hyperparameter transfer across model widths and outperforms Muon in speed while maintaining competitive performance.