TrainDeeploy: framework for hardware-accelerated parameter-efficient fine-tuning of transformer models on extreme-edge devices with memory constraints.
Study of subliminal learning where student LLMs acquire behavioral traits from teacher models via synthetic data training on unrelated domains.
BRACE framework for bandits with noncompliance addressing objective selection between recommendation welfare and treatment learning.
SCDP: sensor-conditioned diffusion policies for humanoid locomotion using only onboard sensors without privileged state estimation.
Multi-DNN inference system for edge devices supporting sparse model variants across heterogeneous processors with improved resource matching.
Study of photonic quantum machine learning under noise, exploring integration of quantum computing with ML for scalable quantum information processing.
EsoLang-Bench: benchmark using esoteric programming languages to evaluate genuine reasoning in LLMs beyond memorization on code tasks.
C2FMAE: coarse-to-fine masked autoencoder for self-supervised visual pre-training combining global semantics with fine-grained detail.
Study on how reasoning affects honesty in LLMs using moral trade-off dataset, finding reasoning increases rather than decreases honesty.
Survey on decentralized federated learning removing central coordinator and using peer-to-peer coordination for collaborative training.
Study of structured lottery tickets in over-parameterized CNNs, connecting pruning to strong lottery ticket hypothesis for computational efficiency.
Sparse Variational Student-t Processes framework extending Gaussian processes for scalable heavy-tailed data modeling.
HYGENE: diffusion-based generative model for creating realistic hypergraphs used in social networks, bioinformatics, and recommender systems.
Research on robust neural network training at arbitrary precision and sparsity, addressing gradient flow issues in quantization and sparse regimes.
ARLBench benchmark for hyperparameter optimization in reinforcement learning agents, addressing evaluation costs and generalizability across domains and algorithms.
Unsupervised representation learning from sequence data using sparse transformation factorization.
Scalable graph neural networks replacing attention with message passing in transformer blocks for large graphs.
Framework quantifying impact of model personalization on prediction accuracy and explanation quality.
Analysis of clustering validity indices in noisy Gaussian mixtures accounting for feature relevance.
Algorithm for learning interpretable model trees with linear combinations in leaf nodes vs constant values.
Consequentialist framework for binary classification evaluation using proper scoring rules and decision theory.
Improved RL training method for LLM reasoning handling all-negative sample groups in GRPO framework.
Generative energy-based model extending RBMs with categorical hidden units for structured discrete representations.
Jailbreak attack on safety-aligned LLMs via self-introspection without requiring model weight access.
Systematic evaluation of quantized LLMs on edge devices, testing models 0.5B-14B with seven PTQ methods.
RL framework using SAT problems to improve LLM reasoning with automatic verification and scalable training.
Large-scale benchmark evaluating ML solvers on combinatorial optimization with real-world industrial datasets.
Semi-supervised extension of conformal prediction using unlabeled data for uncertainty quantification.
Theoretical analysis of pure exploration problems with infinite answer sets, extending bandit algorithms.
Meta-learning approach to rate time series data quality using LLM judgments across diverse domains.
Framework addressing multivariate time series challenges: channel dependencies, asynchronous sampling, and missing values.
Comparison of DeepONet architectures for solving PDE consolidation problems in geotechnical engineering.
Neural latent dynamics model using Langevin equations with VAE framework for capturing spiking activity patterns.
MLES: Multimodal LLM-assisted evolutionary search discovers interpretable programmatic control policies for reinforcement learning.
CTRL: Clustered Transfer Residual Learning for multi-source ML datasets maintaining per-source reliability and differences.
Graph neural networks informed by RF physics principles for accurate, data-efficient prediction of circuit performance.
Iterative in-context learning strategy improves LLM generalization on algebraic reasoning tasks and out-of-distribution examples.
Neural network surrogate model predicts current distribution in superconducting magnets faster than FEM simulations.
Score-based generative model using Kuramoto dynamics on periodic domains for orientation-rich image generation.
Test-time entropy minimization method using asymmetric learning to adapt models to novel environments without logit inflation.
Combines bounded extremum seeking feedback control with deep RL to improve robustness for time-varying nonlinear systems.
Shows expert pruning outperforms merging for compressing sparse mixture-of-experts models on generative tasks.
Bradley-Terry policy optimization for extending RL-based training to non-verifiable tasks with only pairwise human preference supervision.
Reinforcement learning framework combining permutation relative policy optimization to improve LLM reasoning on tabular prediction tasks.
Domain-incremental learning framework for graph models that preserves knowledge across multiple graph domains.
Post-hoc calibration method using structured matrix scaling for improving probability estimates in multi-class classifiers.
Lightweight data valuation method for time-series foundation models using in-context learning instead of expensive influence functions.
Uses time-series foundation models with in-context learning for bearing-health classification without fine-tuning.
Proposes periodic asynchrony to separate inference and training deployment for faster LLM reinforcement learning post-training efficiency.
Graph foundation model framework with structure-aware augmentation for improved robustness against adversarial attacks and domain noise.