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- LIBLINEAR: A Library for Large Linear Classification8,516
LIBLINEAR is an open source library for large-scale linear classification that supports logistic regression and linear support vector machines and provides easy-to-use command-line tools and library calls for users and developers.
- ZOO2,281
An effective black-box attack that also only has access to the input (images) and the output (confidence scores) of a targeted DNN is proposed, sparing the need for training substitute models and avoiding the loss in attack transferability.
- Cluster-GCN1,634
Cluster-GCN is proposed, a novel GCN algorithm that is suitable for SGD-based training by exploiting the graph clustering structure and allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy.
- Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent1,564
This paper studies a D-PSGD algorithm and provides the first theoretical analysis that indicates a regime in which decentralized algorithms might outperform centralized algorithms for distributed stochastic gradient descent.
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- A dual coordinate descent method for large-scale linear SVM1,043
A novel dual coordinate descent method for linear SVM with L1-and L2-loss functions that reaches an ε-accurate solution in O(log(1/ε)) iterations is presented.
- EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples701
The authors' elastic-net attacks to DNNs (EAD) feature L1-oriented adversarial examples and include the state-of-the-art L2 attack as a special case, suggesting novel insights on leveraging L1 distortion in adversarial machine learning and security implications ofDNNs.
- Training and Testing Low-degree Polynomial Data Mappings via Linear SVM598
The proposed fast linear-SVM methods are applied to the explicit form of polynomially mapped data and successfully applied to a natural language processing (NLP) application by improving the testing accuracy under some training/testing speed requirements.
- ImageNet Training in Minutes473
This paper empirically evaluates the effectiveness on two neural networks: AlexNet and ResNet-50 trained with the ImageNet-1k dataset while preserving the state-of-the-art test accuracy, and uses large batch size, powered by the Layer-wise Adaptive Rate Scaling (LARS) algorithm, for efficient usage of massive computing resources.
- Towards Robust Neural Networks via Random Self-ensemble471
This paper proposes a new defense algorithm called Random Self-Ensemble (RSE), which adds random noise layers to the neural network to prevent the strong gradient-based attacks, and ensembles the prediction over random noises to stabilize the performance.
- AutoZOOM: Autoencoder-Based Zeroth Order Optimization Method for Attacking Black-Box Neural Networks460
Experimental results suggest that, by applying AutoZOOM to a state-of-the-art black-box attack (ZOO), a significant reduction in model queries can be achieved without sacrificing the attack success rate and the visual quality of the resulting adversarial examples.
- SSE-PT: Sequential Recommendation Via Personalized Transformer323
A Personalized Transformer (SSE-PT) model, outperforming SASRec by almost 5% in terms of NDCG@10 on 5 real-world datasets, and can handle extremely long sequences and outperform SASRec in ranking results with comparable training speed, striking a balance between performance and speed requirements.
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- GenAttack305
GenAttack is introduced, a gradient-free optimization technique that uses genetic algorithms for synthesizing adversarial examples in the black-box setting and can successfully attack some state-of-the-art ImageNet defenses, including ensemble adversarial training and non-differentiable or randomized input transformations.
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- Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples269
This paper proposes a projected gradient method combined with group lasso and gradient regularization for sequence-to-sequence (seq2seq) models, whose inputs are discrete text strings and outputs have an almost infinite number of possibilities.
- OR-Bench: An Over-Refusal Benchmark for Large Language Models268
This study proposes a novel method for automatically generating large-scale over-refusal datasets and introduces OR-Bench, the first large-scale over-refusal benchmark, to help the community develop better safety aligned models.
- Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines267
A novel coordinate descent algorithm for training linear SVM with the L2-loss function that is more efficient and stable than state of the art methods such as Pegasos and TRON.
- A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification262
Extensive comparisons indicate that carefully implemented coordinate descent methods are very suitable for training large document data.
- Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory257
This work proposes a procedure to exactly compute the unrolled gradient with constant memory complexity, which allows to scale MTT to ImageNet-1K seamlessly with ~6x reduction in memory footprint, and discovers that it is challenging for M TT to handle datasets with a large number of classes, and proposes a novel soft label assignment that drastically improves its convergence.
- Fast coordinate descent methods with variable selection for non-negative matrix factorization256
This paper shows that FastHals has an inefficiency in that it uses a cyclic coordinate descent scheme and thus, performs unneeded descent steps on unimportant variables, and presents a variable selection scheme that uses the gradient of the objective function to arrive at a new coordinate descent method.
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- QUIC: quadratic approximation for sparse inverse covariance estimation224
This work proposes a novel algorithm for solving the resulting optimization problem which is a regularized log-determinant program based on Newton's method and employs a quadratic approximation, but with some modifications that leverage the structure of the sparse Gaussian MLE problem.
- BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables212
An algorithm BIGQUIC is developed, which can solve 1 million dimensional l1-regularized Gaussian MLE problems using a single machine, with bounded memory, and can achieve super-linear or even quadratic convergence rates.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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