All papers

This paper extends backward conformal prediction to covariate shift settings using importance weighting derived from sorted score vectors.

This paper develops an interpretable graph partitioning approach to create simplified surrogate models for spatial point predictions.

This paper studies the behavior, scaling laws, and predictive variance of image classification networks trained to memorize random labels.

This paper introduces a neural inverse optimal transport framework that uses cell lineage tracking to learn perturbation response costs.

This paper presents a method using internal representation similarity to identify which test problems benefit from test-time reinforcement learning.

This paper introduces an embedding perturbation technique to improve exploration diversity during reinforcement learning with verifiable rewards for language models.

This paper identifies modality imbalance failure modes in multimodal knowledge graph alignment and proposes methods to address them.

This paper proposes a distributional regression framework using ReLU neural networks to model conditional event probabilities on arbitrary response spaces.

This paper presents a dynamic sparse training method that reduces compute overhead by maintaining compact tensors with standard dense kernels.

This paper studies equilibrium learning in unbounded stochastic games using continuous trajectories to fit Gaussian process dynamics.

This paper introduces exploration queries for Gaussian process Bayesian optimization to provide theoretical guarantees on the recommended final solution.

This paper proposes a hierarchical control framework combining model predictive control and reinforcement learning to stabilize autonomous mobile robots with swiveling casters.

This paper examines how site-dependent data structures can produce misleadingly high performance estimates in Parkinson's gait classification models.

This paper introduces a two-stage reinforcement learning framework using language models to optimize complex network control tasks.

This paper adapts a simulated fluid foundation model to forecast living embryo tissue dynamics under genetic and temperature perturbations.

This paper analyzes how unsupervised deep networks under-represent high-frequency signals when learning representations from raw time-domain EEG data.

This paper evaluates neural network models to filter background noise in multi-telescope gamma-ray event detection.

This paper presents a moment-based anomaly detection framework for high-dimensional mixed data using dependence-aware aggregation across features.

The paper introduces structured random transform constructions to accelerate 1-bit quantized Johnson-Lindenstrauss compression for key-value caches.

The paper presents a generative flow matching surrogate to simulate bubbly flow interface deformation and trajectories rapidly.

The paper introduces an R package implementing nature-inspired optimization algorithms for spatial fuzzy clustering.

The paper evaluates an eight-qubit variational circuit bottleneck to improve confidence calibration in synthetic speech detection.

The paper combines neural operator priors with sequential Monte Carlo to improve sampling efficiency when solving partial differential equations.

The paper shows that resampling the decoded answer state improves recursive reasoning recovery more efficiently than perturbing latent states.

The paper presents an epigraph lifting and difference-of-convex scheme to solve inverse problems regularized by neural networks.

The paper proposes a black-box framework correcting time series foundation model forecasts using retrieved historical analogues and error residuals.

The paper introduces an interpretable causal forest extension using functional decomposition to explain covariate contributions to treatment effect heterogeneity.

The paper develops a dissimilarity metric for temporal networks based on how epidemic spreading processes unfold across them.

The paper proposes an adaptive treatment selection algorithm controlling false discovery rates under partial feedback with minimax optimality.

The paper introduces a trust-calibrated reinforcement learning framework coupling vision-language-action policy optimization with an evolving simulator.

The paper derives asymptotic risk bounds for constant and warmup-stable-decay learning rate schedules in streaming least-squares regression.

The dissertation presents offline-trained neural implicit representations of reachability corrections for real-time safe robot navigation in dynamic environments.

The paper proposes a neural architecture and reinforcement learning method to optimize market making under non-Markovian Hawkes order-flow dynamics.

The paper surveys theoretical relationships linking machine learning algorithms, data compression methods, and computational meta-complexity.

The paper proves a representation theorem and certified generalization bounds for learning functions governed by graded sparse interactions.

The paper uses a self-supervised convolutional neural network as a preconditioner to accelerate iterative solvers for parametric Helmholtz equations.

The paper establishes two provably convergent differentiable algorithms for pairwise conditional random field regularization in image and signal classification.

The paper introduces a computationally efficient method combining prediction error methods and moving horizon estimation to identify nonlinear dynamic models.

The paper derives PAC-Bayesian generalization bounds that substitute standard relative entropy with general optimal transport metrics.

The paper analyzes human gaze sequences across datasets using motion tokens to demonstrate domain-dependent local transition patterns.

The paper proposes using normalizing flows to evaluate high-dimensional Markov Chain Monte Carlo convergence from multivariate joint posteriors.

The paper presents a language-aware framework using language models and mixture-of-experts adaptors to improve speech recognition during code-switching.

The paper introduces a data-anchored penalty to prevent distributional bias and leakage in physics-informed diffusion models.

The paper introduces a multiscale octree flow framework for generating three-dimensional molecular structures and atom placements.

The paper proposes a hyperbolic aggregation framework for self-supervised brain wave representation learning across heterogeneous electrode montages.

The paper explores prefill-decode disaggregation architectures to speed up rollout generation during agentic reinforcement learning training.

The paper proposes an implicit integration approach for flow-based reinforcement learning policies to enable stable off-policy optimization.

The paper introduces an advantage-guided policy optimization framework that decouples policy representation from reinforcement learning policy updates.

The paper combines natural language reasoning with executable program evidence using reference anchoring to improve mathematical problem solving.

The paper presents a causal action geometry framework to estimate stable decision-response shapes from non-stationary time series.

The paper studies sequence-length parameter bounds and representation geometry required for attention layers to perform counting tasks.

The paper applies optimal transport with Gaussian mixtures to improve flow matching sample efficiency without requiring training labels.

The paper introduces a model that predicts wireless propagation across varying physical scenes, transceiver positions, and antenna configurations.

The paper investigates information-theoretic and geometric compression mechanisms over time within recurrent neural networks.

The paper introduces an orthogonal parameter alignment approach for low-rank adaptation to mitigate catastrophic forgetting in continual learning.

The paper develops an amortized variational inference method to estimate latent point process intensities from replicated event patterns.

The paper analyzes the spectral properties and limits of multinomial and residual resampling in high-dimensional particle filtering.

The paper introduces a nested H-score method for distributed feature extraction that avoids redundancy with receiver-side auxiliary information.

The paper proposes an amortized adaptive importance sampling method using a lightweight neural network to iteratively update proposal distributions.

The paper transfers quadrupedal locomotion skills from single side-view videos to robots by pinning unobserved lateral motion to stance width.