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Research paper MIDiff proposes a multivariate-imaging diffusion model to generate synthetic mobile usage data, addressing sparsity and variable type heterogeneity.
A new research paper proposes a common mathematical framework and reporting checklist for local additive feature attribution methods in explainable AI.
LyaGuide proposes a Lyapunov-guided framework for stabilizing generative flow models, offering explicit stability guarantees for adapting pretrained flows.
Researchers propose a framework to evaluate XAI methods like LIME and SHAP across fidelity, simplicity, and stability, aiming for a unified explainability score.
COAT, a new framework combining counterfactual outcome estimation with mixed-integer optimization, learns interpretable prescriptive policies from observational data.
New research extends policy learning methods for treatment allocation under budget constraints to address missing treatment data.
Research identifies data-parameter interference as a source of instability in federated fine-tuning of LLMs using LoRA, proposing dynamic subspace boosting.
Research explores when to use Supervised Fine-Tuning (SFT) versus In-Context Learning (ICL) for LLM personalization given shared computational resources.
Research proposes a noise-robust framework integrating Inverse Reinforcement Learning (IRL) and Reinforcement Learning (RL) for eliciting risk preferences.
Research proposes 'rollout-based training' for constrained diffusion models to improve adherence to complex feasibility constraints in generated data.
Research on an Android app store shows increasing sponsored search ad load by up to 43% revenue gain can reduce total search conversions by 5%.
Research finds interleaved noise injection in stochastic optimization improves model performance across clean, corrupted, and out-of-distribution data.
New research establishes the first non-vacuous generalization bounds for parameter-efficient reinforcement learning with verifiable rewards (RLVR) fine-tuning.
Research finds adaptive Runge-Kutta optimizers, when compute-matched, yield training loss but not generalization improvement over plain Adam.
New research proposes a continuous-time reinforcement learning framework for fine-tuning discrete diffusion models, deriving continuous-time PPO variants.
Research introduces Probabilistic Physics-Informed Neural Networks (PPINNs) to estimate heterogeneous elastic properties from low-resolution, noisy data.
Research proposes a method for accelerating A/B tests through counterfactual estimation and policy overlap, reducing variance and cost.
Research explores how fairness-enhancing algorithms impact privacy leakage, a reverse analysis from prior work on privacy's effect on fairness.
New research proposes Angular Gaussian Supervised Contrastive Learning (AG-SCL) to improve deep learning reliability for long-tailed ECG arrhythmia diagnosis.
PolyQ is a new CPU-oriented compiler/quantization co-design enabling activation-aware channel-wise bit allocation for edge LLM inference.
TIDE is a proposed trustworthy and interpretable battery degradation estimator using contextual learning and symbolic distillation for reliable battery management.
Research introduces Trajectory-Aware Flow Matching for generative topology optimization, aiming to reduce computational costs in design exploration.
New research introduces a memory-efficient training framework for continuous-time Spiking Neural Networks (SNNs), addressing prior memory constraints.
A new research paper, Grad2Fair, proposes a gradient-driven method for achieving graph fairness in GNNs without relying on demographic data.
Researchers introduced MESHA, an algorithm for Best Arm Identification in strategic linear bandits, addressing misreporting of features.
Researchers introduced VoICE, a Voronoi-Induced Counterfactual Explainability framework for feature-weighted k-means clustering.
New research refines theoretical understanding of Local SGD (Federated Averaging), proposing tighter convergence rates under realistic data heterogeneity.
New research proposes Group Attention Neural Hawkes Process (GAttNHP) for improved event forecasting in Temporal Knowledge Graphs.
New research introduces ChronoQG, a benchmark for temporal knowledge graph question generation to evaluate preservation of temporal validity and constraints.
New research proposes evaluating epistemic uncertainty by its ability to identify regret, moving beyond current reliance on OOD detection and active learning.
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