This work aims to create a large-scale model for critical care time series data.
problem Lack of large-scale datasets and distribution shifts in critical care time series data.
method Harmonized dataset creation and transfer learning research.
result Established a foundation for large-scale multi-variate time series models in critical care.
Hierarchical Softmax approximates class probabilities for large datasets efficiently.
problem Computational inefficiency of Softmax for large-scale classification tasks.
method Used Hierarchical Softmax to approximate class probabilities efficiently.
result Hierarchical Softmax performance degrades as the number of classes increases.
This paper compresses large datasets for efficient machine learning.
problem Efficiently processing large datasets for machine learning.
method Constructing a sketch of the dataset using random features and averaging, then learning from the sketch.
result The approach can perform machine learning tasks without full dataset access, preserving both information and privacy.
Paper introduces ToyADMOS dataset for detecting anomalous machine sounds.
problem Lack of large-scale datasets for ADMOS anomaly detection.
method Collected anomalous sounds of miniature machines by deliberate damage.
result Released dataset includes over 180 hours of normal and 4,000 anomalous sounds.
Improved KG completion on large datasets using entity-relation pair occurrences.
problem Performance bottleneck in training large-scale KGs due to memory constraints.
method Construct a joint learning model using pairwise occurrence information and increase negative sampling quality.
result Significant improvement in performance, especially with low batch size and negative examples.
Efficiently clusters large datasets with a subset of landmarks.
problem High computational complexity in subspace clustering for large-scale datasets.
method Selects a subset of landmarks to reduce the clustering problem to linear time.
result Subspace clustering method runs in linear time with respect to the size of the original data.
Paper proposes data quality measures for large-scale high-dimensional data.
problem Lack of practical data quality measures for large-scale high-dimensional data.
method Proposes two data quality measures: class separability and in-class variability. Efficient algorithms based on random projections and bootstrapping are provided.
result Efficient algorithms for computing data quality measures on large-scale high-dimensional data.
Large-scale Hierarchical Classification (HC) involves datasets consisting of thousands of classes and millions of training instances with high-dimensional features posing several big data challenges. Feature selection that aims to select the subset of discriminant features is an effective strategy to deal with large-sc…
A new large-scale tabular benchmark for Learning from Label Proportions.
problem Lack of a large-scale open benchmark for tabular Learning from Label Proportions.
method Proposed LLP-Bench, a suite of 70 datasets (62 feature bag and 8 random bag) from real-world tabular data.
result Demonstrated the effectiveness of 9 SOTA and popular tabular LLP techniques on 62 feature bag datasets.
ParK efficiently solves kernel ridge regression for large datasets.
problem Large-scale kernel ridge regression efficiency and accuracy.
method Partitioning feature space with random projections and iterative optimization.
result Provably maintains statistical accuracy with reduced space and time complexity.
FibeRed reduces complex data dimensions while preserving topology.
problem Hard embedding of topologically complex datasets in low-dimensional Euclidean space.
method Modeling datasets with vector bundles, reducing fibers while preserving topology.
result FibeRed learns topologically faithful embeddings in lower dimensions than existing methods.
Two large medical dialogue datasets for improving healthcare.
problem Improving healthcare through better dialogue systems.
method Building two large-scale medical dialogue datasets: MedDialog-EN and MedDialog-CN.
result The datasets are the largest medical dialogue datasets to date.
Efficiently applies NTK to large-scale datasets using random features.
problem Computational limitations of kernel methods for large-scale datasets.
method Proposes a sketching-based algorithm combining random features of arc-cosine kernels to construct an efficient feature map of the NTK.
result Achieves comparable error bounds to exact kernel methods but with significantly reduced feature dimensionality.
Deep neural networks (DNNs) trained on large-scale datasets have exhibited significant performance in image classification. Many large-scale datasets are collected from websites, however they tend to contain inaccurate labels that are termed as noisy labels. Training on such noisy labeled datasets causes performance de…
Kernel clustering algorithm improved for large datasets using incomplete Cholesky factorization.
problem Large memory usage in kernel-based clustering for large-scale datasets.
method Approximate the kernel matrix using incomplete Cholesky factorization and apply linear k-means clustering. result The proposed method achieves similar performance to kernel k-means clustering but handles large-scale datasets efficiently. Paper introduces scalable clustering for large datasets with outliers.
problem Lack of scalable algorithms for large datasets with outliers.
method Provable robust clustering algorithm based on loss minimization for Gaussian mixture models.
result Algorithm provides high accuracy with theoretical guarantees and outperforms existing methods.
Gradient-free deep learning for large datasets.
problem Training deep neural networks on large-scale datasets is resource-intensive and requires specialized techniques.
method Recursive Local Representation Alignment (RLRA) for gradient-free training.
result RLRA achieves comparable performance to backprop while converging faster and being parallelizable.
This paper presents a fast algorithm for clustering large datasets using Gaussian mixture models.
problem Efficiently clustering large-scale datasets with Gaussian mixture models.
method Variational EM algorithm with coreset objectives for sublinear complexity.
result Substantial speedups in clustering large-scale datasets (up to 32,000 clusters on 80 Million Tiny Images).
Feature selection is an important challenge in machine learning. It plays a crucial role in the explainability of machine-driven decisions that are rapidly permeating throughout modern society. Unfortunately, the explosion in the size and dimensionality of real-world datasets poses a severe challenge to standard featur…
We study large-scale classification problems in changing environments where a small part of the dataset is modified, and the effect of the data modification must be quickly incorporated into the classifier. When the entire dataset is large, even if the amount of the data modification is fairly small, the computational …
New method speeds up Gaussian process training and inference for large datasets.
problem Training and inference in Gaussian processes are computationally expensive for large datasets.
method Iterative alternating projection method that accesses subblocks of the kernel matrix, reducing time and space complexity.
result Empirically, the method accelerates GP training and inference by up to 72x compared to conjugate gradients.
This work improves Gaussian process model selection for large datasets.
problem Prohibitively high computational cost in Gaussian process model selection.
method Linear-time scaling and computational uncertainty tradeoff.
result Computation-aware Gaussian processes can be trained on large datasets efficiently.
Researchers organize and analyze a large public safety imagery dataset.
problem Efficiently organizing and analyzing a vast public safety imagery dataset.
method Hierarchical organization approach using Lincoln Laboratory Supercomputing Cluster for compute and storage.
result Successfully organized and evaluated the dataset with large-scale imagery inference across terabytes of data.
DPEs use ensembles to approximate BNNs for efficient large-scale visual active learning.
problem Efficiently annotating data for deep neural networks training.
method Deep Probabilistic Ensembles (DPEs) using regularized ensemble approximations of deep BNNs.
result DPEs achieve competitive performance with significantly less training data.
MineRL dataset tackles reinforcement learning with over 60M annotated state-action pairs.
problem Sample inefficiency in reinforcement learning methods.
method Large-scale, simulator-paired dataset of human demonstrations in Minecraft.
result Demonstrates the scale, diversity, and difficulty of Minecraft tasks.
Sparse Convex Biclustering improves accuracy and robustness in high-dimensional datasets.
problem Challenges in clustering rows and columns of large-scale datasets due to noise and computational complexity.
method Sparse Convex Biclustering (SpaCoBi) using convex optimization and stability-based tuning.
result Significantly outperforms state-of-the-art methods in accuracy for high-dimensional datasets.
AcceleratedLiNGAM speeds up causal discovery methods for large datasets.
problem Slow causal discovery methods for large-scale datasets.
method Parallelized LiNGAM method with GPU acceleration.
result Up to 32-fold speed-up on benchmark datasets.
A framework for large-scale federated learning with non-IID data.
problem Stability of models trained on non-IID data in federated learning.
method Generation of non-IID datasets and modular evaluation framework.
result Open-source benchmark for large-scale federated learning research.
Researchers parallelize neural kernels for large-scale data, achieving state-of-the-art accuracy.
problem Limited scalability of neural kernels on large datasets.
method Massively parallel computation across many GPUs, combined with a distributed, preconditioned conjugate gradients algorithm.
result Achieved state-of-the-art accuracy of 91.2% on CIFAR-5m dataset using neural kernels.
Paper presents an efficient algorithm for learning minimax risk classifiers with large-scale data.
problem Efficient learning of minimax risk classifiers for large-scale data with multiple classes.
method Combination of constraint and column generation for efficient learning.
result 10x speedup for general large-scale data and 100x speedup with many classes.
This thesis tackles large-scale learning with kernel methods and proposes scalable algorithms for lifelong robot learning.
problem The challenge of scaling kernel methods to large datasets.
method We analyze and develop approximate learning algorithms, including Nyström and random features, to improve scalability.
result Our methods enable robots to learn continuously and adapt to changing environments efficiently.
Scalable kernel methods for large datasets using Fourier representations and NUFFT.
problem Cubic complexity in kernel methods limits their use on large-scale datasets.
method Fourier representation of kernels combined with NUFFT for O(n log n) complexity.
result Achieves minimax convergence rates and processes up to tens of billions of samples.
This paper proposes a new method for learning covers of geometric datasets to improve topological inference and visualization.
problem Improving topological inference and visualization of large-scale geometric datasets.
method Proposes a method for learning topologically-faithful covers of geometric datasets using optimization.
result Simplicial complexes obtained from learned covers outperform standard methods in terms of size and representation of large-scale topology.
Efficiently computes tree-Wasserstein barycenter for large-scale multilevel clustering and scalable Bayes.
problem Large-scale multilevel clustering and scalable Bayes problems.
method Proposes an efficient algorithm for tree-Wasserstein barycenter and variants.
result Significantly improves efficiency in computation and memory usage for large-scale applications.
Many real-world regression problems demand a measure of the uncertainty associated with each prediction. Standard decision forests deliver efficient state-of-the-art predictive performance, but high-quality uncertainty estimates are lacking. Gaussian processes (GPs) deliver uncertainty estimates, but scaling GPs to lar…
This paper proposes a new Nystrom-based clustering algorithm for large-scale data.
problem Spectral clustering's high computational complexity for large-scale data.
method Centroid Minimum Sum of Squared Similarities (CMS3) sampling procedure with eigen spectrum shape heuristic.
result Competitive low-rank approximations in test datasets compared to state-of-the-art methods.
We present a structural clustering algorithm for large-scale datasets of small labeled graphs, utilizing a frequent subgraph sampling strategy. A set of representatives provides an intuitive description of each cluster, supports the clustering process, and helps to interpret the clustering results. The projection-based…
Federated learning for Bayesian clustering of large datasets.
problem Bayesian model-based clustering of large-scale binary and categorical data.
method Federated variational inference with local merge and delete moves in parallel batches, followed by global merge moves.
result Empirical validation shows superior performance compared to existing algorithms.
A method for large scale Gaussian process classification has been recently proposed based on expectation propagation (EP). Such a method allows Gaussian process classifiers to be trained on very large datasets that were out of the reach of previous deployments of EP and has been shown to be competitive with related tec…
Study presents a dataset and evaluation framework for representation learning in complex multimodal systems.
problem Lack of large-scale standard datasets for representation learning in complex multimodal systems.
method Implemented and compared several approaches to representation learning on a large-scale dataset for landing an airplane.
result Representations can be used for various applications including anomaly detection and optimal control.
Optimized online learning with kernels for large-scale adversarial data.
problem Efficient online learning for large-scale, potentially adversarial datasets.
method Online variations of kernel Ridge regression using approximated basis functions.
result Optimal regret for a wide range of kernels with low per-round complexity.
Transformers learn to predict chess moves with surprising accuracy and strength.
problem Training transformers on chess to predict moves accurately.
method Large-scale chess dataset (10M games), supervised learning with up to 270M parameters.
result Transformers can predict action-values for novel boards with high accuracy.
Two novel algorithms improve scalability and robustness of spectral clustering for large datasets.
problem Scalability and robustness of spectral clustering for large-scale datasets.
method Ultra-scalable spectral clustering (U-SPEC) and ultra-scalable ensemble clustering (U-SENC) algorithms.
result Robust and efficient clustering of ten-million-level datasets on a PC.
OGB provides diverse graph datasets for robust ML research.
problem Challenges in scalable and robust graph machine learning.
method Unified evaluation protocol, diverse datasets, and automated pipeline.
result Significant scalability and generalization challenges identified.
A new kernel, Isolation Kernel, simplifies large scale online kernel learning without sacrificing accuracy.
problem Building efficient and scalable kernel-based models from large datasets with high accuracy.
method Introducing Isolation Kernel, which creates an exact, sparse, and finite-dimensional feature map of a kernel, allowing for efficient large scale online kernel learning without accuracy loss.
result Large scale online kernel learning can be achieved efficiently and accurately using Isolation Kernel.
PCA-Guided Quantile Sampling preserves data structure in large datasets.
problem Preserving data structure in large-scale subsampling.
method PCA QS uses leading principal components to guide stratified sampling.
result PCA QS maintains statistical and geometric structure without distorting semantics.
Large-scale eye-tracking dataset for Atari games.
problem Improving AI decision-making through human eye-tracking data.
method Recorded 117 hours of gameplay with eye movements from 20 Atari games.
result Human gameplay decisions and scores comparable to human records.
Motivation: Post-database searching is a key procedure in peptide dentification with tandem mass spectrometry (MS/MS) strategies for refining peptide-spectrum matches (PSMs) generated by database search engines. Although many statistical and machine learning-based methods have been developed to improve the accuracy of …