Papers Sparse Learning
“Sparse Learning” 태그가 달린 논문 209편 · 필터 해제
Maximum Tsallis Entropy Distributions for Robust and Efficient Sparse Learning from Correlated Data
This paper addresses the limitations of Gaussian distribution assumptions in statistical sparse learning, particularly in modeling correlated and heterogeneous data. Conventional Gaussian models often lack robustness tow…
Sparse LearningHERALD: Counterfactual Audits and Minimal Repairs for Proof-of-Retrieval Rewards
Search-agent rewards mix answer quality, citation grounding, tool cost, and anti-hacking terms; a high score therefore need not imply that cited evidence was retrieved, and added penalties can cancel. We introduce HERALD…
Sparse LearningAdversarial LassoNet: Robust Feature Selection via Stability-Driven Sparse Learning
Sparse feature selection is critical for high-dimensional machine learning, yet traditional $\ell_1$-regularized methods are often brittle under observational noise and spurious correlations, leading to unstable feature …
Sparse LearningPhysics-Aware Sparse Learning and Selective Online Adaptation for Euler-Lagrange Robot Dynamics
Accurate dynamics models are essential for model-based robotic control, yet nominal Euler--Lagrange models often become inaccurate in the presence of payload variation, unmodeled coupling, friction, aerodynamic effects, …
Sparse LearningTwo-Valued Symmetric Circulant Matrices: Applications in Deep Learning
Despite the success of deep neural networks in vision, medical diagnosis, and IoT scenarios, their deployment on resource-limited platforms poses serious challenges due to their high storage requirements, computational c…
Medical DiagnosisSparse LearningByzantine-Robust Distributed Sparse Learning Revisited
We revisit Byzantine robust distributed estimation for high-dimensional sparse linear models. By combining local $\ell_1$-regularized robust estimation with robust aggregation at the server, the framework applies to pseu…
Sparse LearningHolder Policy Optimisation
Group Relative Policy Optimisation (GRPO) enhances large language models by estimating advantages across a group of sampled trajectories. However, mapping these trajectory-level advantages to policy updates requires aggr…
Continuous ControlSparse LearningThe Benefits of Temporal Correlations: SGD Learns k-Juntas from Random Walks Efficiently
We study how temporal correlations in the data can make certain sparse learning problems efficiently learnable by gradient-based methods. Our focus is on Boolean k-juntas, a canonical sparse learning problem known to pos…
Sparse LearningMeta Additive Model: Interpretable Sparse Learning With Auto Weighting
Sparse additive models have attracted much attention in high-dimensional data analysis due to their flexible representation and strong interpretability. However, most existing models are limited to single-level learning …
Bilevel OptimizationSparse LearningSpeeding Up Mixed-Integer Programming Solvers with Sparse Learning for Branching
Machine learning is increasingly used to improve decisions within branch-and-bound algorithms for mixed-integer programming. Many existing approaches rely on deep learning, which often requires very large training datase…
Graph Neural NetworkSparse LearningStep-Size Decay and Structural Stagnation in Greedy Sparse Learning
Greedy algorithms are central to sparse approximation and stage-wise learning methods such as matching pursuit and boosting. It is known that the Power-Relaxed Greedy Algorithm with step sizes $m^{-α}$ may fail to conver…
Sparse LearningMARO: Learning Stronger Reasoning from Social Interaction
Humans face countless scenarios that require reasoning and judgment in daily life. However, existing large language model training methods primarily allow models to learn from existing textual content or solve predetermi…
Mathematical ReasoningInstruction FollowingSparse LearningSUG-Occ: Explicit Semantics and Uncertainty Guided Sparse Learning for Efficient 3D Occupancy Prediction
3D semantic occupancy prediction has emerged as a critical perception task for autonomous driving due to its ability to offer voxel-level semantic and geometric understanding of the environment. However, such a refined r…
Autonomous DrivingSparse LearningElfCore: A 28nm Neural Processor Enabling Dynamic Structured Sparse Training and Online Self-Supervised Learning with Activity-Dependent Weight Update
In this paper, we present ElfCore, a 28nm digital spiking neural network processor tailored for event-driven sensory signal processing. ElfCore is the first to efficiently integrate: (1) a local online self-supervised le…
Self-Supervised LearningGesture RecognitionSparse LearningEchoingPixels: Aliasing-Resistant Joint Token Reduction for Audio-Visual LLMs
Audio-Visual Large Language Models (AV-LLMs) face prohibitive computational costs of processing massive, redundant audio-visual tokens. Existing unimodal compression techniques fail to capture the heterogeneous and mutua…
Sparse LearningSparsity via Hyperpriors: A Theoretical and Algorithmic Study under Empirical Bayes Framework
This paper presents a comprehensive analysis of hyperparameter estimation within the empirical Bayes framework (EBF) for sparse learning. By studying the influence of hyperpriors on the solution of EBF, we establish a th…
Image DeblurringSparse LearningGaLLoP: Gradient-based Sparse Learning on Low-Magnitude Parameters
Sparse fine-tuning techniques adapt LLMs to downstream tasks by only tuning a sparse subset of model parameters. However, the effectiveness of sparse adaptation depends on optimally selecting the model parameters to be f…
parameter-efficient fine-tuningSparse LearningA Universal Banach--Bregman Framework for Stochastic Iterations: Unifying Stochastic Mirror Descent, Learning and LLM Training
Stochastic optimization powers the scalability of modern artificial intelligence, spanning machine learning, deep learning, reinforcement learning, and large language model training. Yet, existing theory remains largely …
Stochastic OptimizationReinforcement LearningSparse LearningUncertainty Quantification for Large-Scale Deep Networks via Post-StoNet Modeling
Deep learning has revolutionized modern data science. However, how to accurately quantify the uncertainty of predictions from large-scale deep neural networks (DNNs) remains an unresolved issue. To address this issue, we…
Sparse LearningOnline Nonsubmodular Optimization with Delayed Feedback in the Bandit Setting
We investigate the online nonsubmodular optimization with delayed feedback in the bandit setting, where the loss function is $α$-weakly DR-submodular and $β$-weakly DR-supermodular. Previous work has established an $(α,β…
Sparse Learning