2233 papers
This paper introduces Fast Explanation (FEX), a novel framework that utilizes policy gradient optimization to represent attribution-based explanations as probability distributions, achieving over 97% reduction in inference time and 70% less memory usage compared to traditional model-agnostic methods while maintaining high-quality, scalable explanations for image and text classification tasks.
This paper identifies a "corruption stage" in few-shot fine-tuned diffusion models caused by a narrowed learning distribution and proposes a Bayesian Neural Network approach with variational inference to broaden this distribution, thereby mitigating corruption and improving image fidelity, quality, and diversity without additional inference costs.
This paper proposes a structured semiparametric framework combining an algorithm choice model and a viewer response model to correct the severe bias in standard estimators caused by algorithmic interference in two-sided marketplaces, thereby enabling accurate global treatment effect estimation for platform-wide algorithm updates.
The paper proposes OTAD, a novel two-step defense framework that combines optimal transport theory with convex integration to train deep neural networks that achieve both high accuracy and certified local Lipschitz robustness against adversarial attacks.
This paper establishes the statistical foundations of the mini-batch maximum partial-likelihood estimator (mb-MPLE) for deep Cox models optimized via stochastic gradient descent, proving its consistency and optimal convergence rates while providing practical guidance on hyperparameter tuning and demonstrating its effectiveness in large-scale applications where standard estimation is intractable.
This paper proposes a novel Variational Learning framework for Gaussian Process Latent Variable Models that utilizes Stochastic Gradient Annealed Importance Sampling to overcome proposal distribution challenges in high-dimensional spaces, achieving tighter variational bounds and superior performance compared to state-of-the-art methods.
This paper cautions against treating post hoc explainers like SHAP and LIME as definitive evidence for underlying data relationships in business research, demonstrating through a systematic review and simulation that their explanations often misalign with true data-generating processes due to feature correlation and the Rashomon effect, and thus should be used only as exploratory tools rather than for hypothesis validation.
This paper challenges the prevailing assumption of Spiking Neural Networks' inherent energy superiority by introducing a rigorous, fair-comparison framework that reveals SNNs only outperform Quantized ANNs under specific low-spike-rate conditions, while demonstrating that such optimized SNNs could nearly double the battery life of devices like smartwatches.
This paper introduces a novel Coupled Oscillator Network (CON) model that overcomes key limitations in latent-space control by ensuring Lagrangian structure, global input-to-state stability, and an invertible input-force mapping, thereby enabling efficient closed-form control strategies for complex mechanical systems using only raw visual feedback.
The paper proposes xTED, a flexible cross-domain adaptation framework that utilizes a diffusion model to edit and transform source domain trajectories into target domain distributions at the data level, thereby bridging domain gaps and enhancing policy learning performance without requiring complex domain-specific modeling.
This paper introduces BNEM, a robust diffusion-based sampler that learns from energy functions via bootstrapped noised energy matching to efficiently generate independent samples from Boltzmann distributions, outperforming existing methods on complex molecular dynamics benchmarks.
This paper establishes that policy gradient methods achieve global convergence with non-asymptotic sample complexity guarantees for finite-horizon MDPs with general state and action spaces by proving the Polyak-Łojasiewicz-Kurdyka condition holds, thereby providing the first theoretical foundations for optimizing multi-period inventory and stochastic cash balance systems.
This paper introduces a constant-lag Neural Delay Differential Equations (NDDEs) framework, inspired by the Mori-Zwanzig formalism, to effectively learn non-Markovian dynamics from partially observed data by identifying memory effects through time delays, demonstrating superior performance over existing methods like LSTMs and ANODEs across synthetic, chaotic, and experimental datasets.
This paper introduces LS-Imagine, a novel approach that enhances open-world reinforcement learning by constructing a long short-term world model with goal-conditioned jumpy transitions and affordance maps, thereby enabling agents to efficiently explore vast state spaces and optimize for long-horizon rewards, as demonstrated by significant improvements in MineDojo.
This paper introduces a PAC-Bayesian framework modeling adversarial training with momentum SGD as a discrete-time dynamical system to derive time-resolved generalization bounds that mechanistically explain robust overfitting and reveal the trade-offs in adversarial weight perturbation design.
This paper demonstrates that frozen transformers, when used in an in-context learning setting, can implicitly infer hidden states to accurately predict the outputs of both linear and nonlinear dynamical systems from noisy observations, achieving performance comparable to optimal and heuristic Bayesian filters without requiring test-time gradient updates or explicit knowledge of the system model.
This paper proposes Adaptive Transfer Clustering (ATC), a unified framework that automatically leverages commonalities between a main and an auxiliary dataset to improve clustering performance despite unknown discrepancies, while providing theoretical optimality guarantees under Gaussian mixture models and demonstrating effectiveness through extensive experiments.
This paper introduces a neurosymbolic system that reconstructs medical images using visual primitives to generate high-level structural explanations, achieving superior classification accuracy and transparency compared to conventional deep learning models in diagnosing histological abnormalities.
The paper introduces Puppet-CNN, a framework that models convolutional layer parameters as states evolving within a learned neural ODE, enabling input-adaptive computation and significant parameter reduction by dynamically determining the effective network depth based on input complexity.
This paper proposes an input-adaptive framework for diffusion models that dynamically adjusts the generative trajectory and sampling steps for each sample based on its complexity, thereby maintaining generation quality while reducing the average number of required steps.