All papers

Identifies how non-separable priors in prior-data fitted networks affect predictive accuracy under model misspecification.

Steers frozen diffusion models toward black-box materials design goals using evolutionary search along sampling trajectories.

Introduces an algebraic framework proving information loss asymmetry when decomposing and reconstructing joint probability distributions.

Develops an episodic sequential decision agent providing hard budget guarantees and sublinear regret bounds using conformal prediction.

Presents a taxonomy and research roadmap for measuring algorithmic fairness over time in systems with feedback loops.

Shows the PATTERN graph benchmark can be solved by memorization because pattern subgraphs are reused across splits.

Provides statistical error bounds for dense and sparse mixture-of-experts models, showing sparsity reduces compute rather than sample complexity.

Formulates an online sampling algorithm that adjusts temperature dynamically under cross-entropy budgets to prevent repetitive language model output.

Derives a verifier-tilted temperature sampling formula that optimizes pass-at-k metrics under entropy diversity and plausibility constraints.

Reconstructs magnetic reconnection plasma states from sparse multimodal observations using a residual 3D U-Net architecture.

Demonstrates theoretical sampling failures and kinetic traps when training unconstrained machine-learned interatomic potentials from energy and force data.

Uses reinforcement learning to place token anchors that guide text generation in frozen diffusion language models.

Proposes a sequential selection algorithm to prune redundant random features for multiclass classification with theoretical guarantees.

Introduces a recurrent latent graph transformer that solves capacitated vehicle routing problems by updating edge representations iteratively.

Presents an energy-regularized regression method to learn heat equation solutions from noisy physical data and proves estimation error bounds.

Analyzes how data and network structure alignment enables personalized model groups in decentralized learning without explicit personalization algorithms.

This paper proposes a graph differential equation and transformer architecture to perform joint data fusion and state estimation in power distribution grids.

This paper presents an invariant lexicographic canonicalization method to resolve rotation and permutation ambiguities in 3D point cloud representations.

This paper attaches reinforcement learning control heads to a pretrained physics foundation model for closed-loop fluid-flow control.

This paper proposes a game-theoretic reinforcement learning framework to defend virtual power plant resources without requiring grid network topology knowledge.

This paper studies stability thresholds and convergence boundaries of non-Euclidean gradient descent on quadratic objectives.

This paper derives theoretical bounds on Hoffman constants to analyze the conditioning and convergence of sparse regularized optimization problems.

This paper introduces a dual-latent variational autoencoder that separates temporal dynamics from cross-sensor correlations for time-series anomaly detection.

This paper introduces an inference-time scaling method using paired denoising and renoising to sample from target distributions despite imperfect verifiers.

This paper derives theoretical conditions on what decision-time history data releases must retain to ensure valid off-policy evaluation.

This paper presents a discrete diffusion model that operates at the fragment level to generate variable-sized molecular graphs.

This paper presents a deep mixed model combining neural networks with trainable subject-specific effects for longitudinal data prediction.

This paper uses a neural operator trained on reverse-time-migration images as a preconditioner to accelerate full-waveform inversion.

This paper introduces learned communication adapters and action heads to coordinate multiple vision-language-action robotic policies during collaborative tasks.

This paper introduces an annotated audio dataset of jazz recordings for beat tracking, chord estimation, and music structure analysis.

This paper introduces a reinforcement learning framework that trains coding agents to anticipate execution outcomes before taking tool actions.

This paper proposes a voice-conversion system using codec language models to preserve speech emotion while anonymizing speaker identity in real-time streams.

This paper analyzes contrastive representation learning theoretically by framing inner-product scores as spectral approximations of pointwise mutual information.

This paper introduces a flow matching framework that decouples backbone generation from sequence and side-chain design for target-conditioned cyclic peptide synthesis.

The paper proposes using vision-language models as success detectors rather than reward regressors for embodied reinforcement learning.

The paper proposes a Riemannian graph tokenizer that quantizes structural patterns to support cross-domain graph knowledge transfer.

The paper develops a neural network constrained by a personalized four-parameter physiological model to forecast blood glucose levels in diabetes.

The paper establishes theoretical bounds on interval lengths for split conformal prediction under Huber data contamination.

The paper demonstrates that convolutional neural networks can classify transmitter digital signal processing implementations from over-the-air radio signals.

The paper presents a continuous joint optimization framework for support vector machines that updates multipliers and kernel hyperparameters together.

The paper proposes sensitivity fidelity as an evaluation criterion to verify physical consistency in deep-learning Earth-system forecasting models.

The paper presents an accelerated federated learning method that assigns client-specific memory windows based on individual noise and drift rates.

The paper presents a unified three-stage framework for multiple instance learning and recommendation from scored bag collections.

The paper proposes a latent-space importance weighting algorithm to improve prediction accuracy under covariate shift.

The paper introduces an attention-guided graph neural network surrogate to reconstruct missing boundary information in physical field simulations.

The paper establishes theoretical risk bounds for a two-stage unsupervised domain adaptation method combining contrastive learning and adversarial alignment.

The paper introduces a progressive multi-label learning framework that decouples direct label fitting from predictor optimization via an auxiliary variable.

The paper analyzes screening collapse, showing how recursive training on synthetic data degrades feature selection prior to model training.

The paper introduces an adaptive role-assignment and feature reconstruction framework to improve hybrid federated image communication under wireless constraints.

The paper presents a transformer-based relation encoder for knowledge graph foundation models to reduce scaling and inference costs.

The paper proposes a hypothesis testing framework using spectral radius to determine if a complex network contains true central structure.

The paper derives geometric and statistical criteria showing when explicit penalties improve sample accuracy in flow matching generative models.

The paper establishes uniform policy score bounds and Lipschitz continuity conditions across four common reinforcement learning policy parameterizations.

This paper analyzes the statistical costs and computational lower bounds of constructing adaptive confidence sets under unknown Huber contamination.

This paper develops an asymptotically normal estimator to measure the value of policy improvement in adaptive experimental settings.

This paper introduces a data-flow graph representation and mutation testing technique to uncover bugs in deep learning frameworks.

This paper benchmarks deep learning architectures for anomaly detection on real-world cloud telemetry using timing-aware evaluation metrics.

This paper presents guarded block transfers for polytope distance computation to accelerate support vector machine training.

This paper analyzes how shared representations specialize among multiple targets and how optimizer state influences this specialization.

This paper introduces an online flow matching method to fine-tune guided diffusion models toward specific rewards without losing prompt alignment.