Proposes a framework for semi-supervised continual learning from sequentially arriving data.
problem Learning from data with changing task distribution over time, especially in domains with a mix of labeled and unlabeled data.
method Meta-Consolidation for Continual Semi-Supervised Learning (MCSSL) framework with a hypernetwork and semi-supervised auxiliary classifier.
result Significant improvements in continual semi-supervised learning setting.
Paper presents unsupervised calibration for split conformal classification.
problem Inconvenient requirement of labeled calibration samples.
method Uses unsupervised calibration samples alongside supervised training samples.
result Achieves comparable performance to supervised calibration methods.
IMPaCT improves node classification in chronological split temporal graphs.
problem Domain adaptation challenges in graph data due to chronological splits.
method IMPaCT proposes a method to impose invariant properties based on realistic assumptions derived from temporal graph structures.
result IMPaCT achieves a 3.8% performance improvement over current SOTA method on the ogbn-mag graph dataset.
BELA infers labels for unlabeled data at lower cost.
problem Efficiently labeling large unlabeled datasets.
method Supervised splitting with bias-reduction techniques.
result BELA outperforms existing adaptive labeling strategies.
Recent work has shown that using unlabeled data in semi-supervised learning is not always beneficial and can even hurt generalization, especially when there is a class mismatch between the unlabeled and labeled examples. We investigate this phenomenon for image classification on the CIFAR-10 and the ImageNet datasets, …
Random Forests (RFs) are strong machine learning tools for classification and regression. However, they remain supervised algorithms, and no extension of RFs to the one-class setting has been proposed, except for techniques based on second-class sampling. This work fills this gap by proposing a natural methodology to e…
Study reveals attributes help in few-shot classification generalization.
problem Understanding what makes some novel classes easier to learn.
method Defined attributes to quantify concept relatedness, used supervised and self-supervised learning.
result Combining self-supervised pretraining with supervised finetuning improves generalization.
OutlierTree detects outliers using decision trees and provides explanations.
problem Detecting outliers in data while providing understandable explanations.
method Supervised decision tree splits with 1-d confidence intervals.
result Human-readable explanations for outlier detection.
Unified framework for semi-supervised learning reduces annotation needs.
problem Sparse annotations and large amounts of unlabeled data in computational pathology.
method S5CL integrates fully-supervised, self-supervised, and semi-supervised learning through hierarchical contrastive losses.
result S5CL improves accuracy and F1-score in histopathological datasets with sparse labels.
A new framework converts EEG signals between subjects and tasks.
problem Noise and variability in EEG data hinder generalizable signal extraction.
method Contrastive Split-Latent Permutation Autoencoder (CSLP-AE) framework.
result The CSLP-AE framework enables zero-shot conversion between unseen subjects.
Project analyzes drug reviews to predict ratings using machine learning.
problem Predicting drug ratings from text reviews.
method Implemented supervised machine learning algorithms with TFIDF and Count Vectors.
result Good results in predicting test data sets for popular conditions.
TAPE benchmarks protein learning tasks, finds self-supervised pretraining boosts performance.
problem Fragmented datasets and lack of standardized evaluation in protein modeling.
method TAPE introduces five semi-supervised learning tasks, curates splits, benchmarks models.
result Self-supervised pretraining more than doubles performance in some cases.
Novel approach for creating interpretable classifiers using bilevel optimization of split-rules in NLDTs.
problem Creating highly accurate and easily interpretable classifiers for practical applications.
method Representing classifiers as assemblies of simple mathematical rules using NLDTs with evolutionary bilevel optimization.
result The approach ensures interpretability while achieving high accuracy on various classification problems.
This paper tackles few-shot classification by improving GAN-based data augmentation.
problem Improving few-shot classification performance using GANs with limited data.
method Fine-tuning GANs for few-shot classification, addressing training and evaluation challenges.
result Semi-supervised fine-tuning is a more effective approach for few-shot classification with limited data.
Combines neural networks and decision trees for supervised learning.
problem Challenges in optimizing hierarchical parameters and constructing tree structures.
method Probabilistic approach with modified gradient ascent and adaptive tree construction.
result Novel classification and regression technique that combines strengths of neural networks and decision trees.
Transformer is reinterpreted as a numerical ODE solver, leading to improved architectures.
problem Understanding and improving the Transformer architecture.
method Interpreted Transformer as a numerical ODE solver for a multi-particle dynamic system, proposing new architectures.
result Macaron Net outperforms Transformer on supervised and unsupervised learning tasks.
Ensembled neural networks improve MRI image quality.
problem Accelerated parallel MR imaging with high SSIM scores.
method Ensembled Σ-net combining parallel coil and sensitivity networks, trained with supervised and semi-supervised methods. result Ensembling models achieves visually sharp and textured images with robust SSIM scores.
Data representations that contain all the information about target variables but are invariant to nuisance factors benefit supervised learning algorithms by preventing them from learning associations between these factors and the targets, thus reducing overfitting. We present a novel unsupervised invariance induction f…
In supervised learning, an inductive learning algorithm extracts general rules from observed training instances, then the rules are applied to test instances. We show that this splitting of training and application arises naturally, in the classical setting, from a simple independence requirement with a physical interp…
ISL improves causal structure learning with invariant structures across different environments.
problem Improving causal structure discovery for better generalization and explainability.
method ISL splits data into environments, learns invariant structures, and selects optimal classifiers based on graph structures.
result ISL accurately discovers causal structures and outperforms alternative methods on synthetic and real-world datasets.
Semi-supervised node classification in graphs is a fundamental problem in graph mining, and the recently proposed graph neural networks (GNNs) have achieved unparalleled results on this task. Due to their massive success, GNNs have attracted a lot of attention, and many novel architectures have been put forward. In thi…
Pipeline selects best RL policy from limited data.
problem Selecting best RL policy from small datasets.
method Task- and method-agnostic pipeline using multiple data splits.
result Pipeline produces higher-performing policies.
Proposes autoencoding with random forests using spectral graph theory.
problem Learning low-dimensional embeddings of random forest models.
method Combines nonparametric statistics and spectral graph theory for optimization.
result Establishes a universal consistent decoder for random forest models.
In this report, we present an unsupervised machine learning method for determining groups of molecular systems according to similarity in their dynamics or structures using Ward's minimum variance objective function. We first apply the minimum variance clustering to a set of simulated tripeptides using the information …
The paper studies momentum-based minimization for Ginzburg-Landau on Euclidean spaces and graphs.
problem Minimizing the Ginzburg-Landau functional on Euclidean spaces and graphs.
method Momentum-based minimization using a convex-concave splitting-based FISTA-type time discretization.
result Momentum can lead to faster convergence if the time step size is large but not too large.
A new method learns representations without end-to-end backpropagation.
problem Learning representations without labeled data and end-to-end backpropagation.
method Gradient-isolated modules trained using InfoNCE bound.
result Top module's representations yield competitive results on downstream tasks.
Conventional decision trees have a number of favorable properties, including interpretability, a small computational footprint and the ability to learn from little training data. However, they lack a key quality that has helped fuel the deep learning revolution: that of being end-to-end trainable, and to learn from scr…
We present an algorithm for learning decision trees using stochastic gradient information as the source of supervision. In contrast to previous approaches to gradient-based tree learning, our method operates in the incremental learning setting rather than the batch learning setting, and does not make use of soft splits…
This paper proposes an alternative to E2E training for deep networks, reducing memory footprint.
problem High GPUs memory footprint in end-to-end training of deep networks.
method Locally supervised learning with information propagation loss to avoid information collapse.
result The proposed method achieves competitive performance with less than 40% memory footprint compared to E2E training.
Generative Adversarial Networks (GANs) produce systematically better quality samples when class label information is provided., i.e. in the conditional GAN setup. This is still observed for the recently proposed Wasserstein GAN formulation which stabilized adversarial training and allows considering high capacity netwo…
Paper learns dictionaries for sparse signal recovery using automatic differentiation.
problem Learning dictionaries for sparse signal recovery from noisy data.
method Approximates reconstructions using FB algorithm and learns dictionaries with projected gradient descent.
result Successfully learns 1D TV dictionary from piecewise constant signals.
SPlit optimizes dataset splitting for better model performance.
problem Improving model performance through optimal dataset splitting.
method Adapting Support Points (SP) algorithm for subsampling and categorical variables in a sequential nearest neighbor approach.
result SPlit significantly improves worst-case testing performance compared to random splitting.
Objective: To determine the completeness of argumentative steps necessary to conclude effectiveness of an algorithm in a sample of current ML/AI supervised learning literature. Data Sources: Papers published in the Neural Information Processing Systems (NeurIPS, née NIPS) journal where the official record showed a 2017…
The paper extends keenness concept to bridge splittings and finds conditions for existence.
problem Extending keenness concept to bridge splittings and finding conditions for existence.
method Extending the concept of keenness to bridge splittings and proving existence conditions.
result Existence of strongly keen (g,b)-splitting of a link with distance n for certain integers g, b, and n. Non-split almost complex supermanifolds and non-split Riemannian supermanifolds are studied. The first obstacle for a splitting is parametrized by group orbits on an infinite dimensional vector space. Further it is shown that non-split structures appear in the first case as deformations of a split reduction and in the …
Study flippable Heegaard splittings in Seifert fibered spaces.
problem Identifying flippable Heegaard splittings in Seifert fibered spaces.
method Examining isotopies that interchange Heegaard splitting sides in Seifert fibered spaces.
result Characterized which Heegaard splittings are flippable in Seifert fibered spaces.
We study the self-dual Yang-Mills equations in split signature. We give a special solution, called the basic split instanton, and describe the ADHM construction in the split signature. Moreover a split version of t'Hooft ansatz is described.
Paper proposes a novel SVM method for creating survival trees.
problem Creating non-linear survival trees for right-censored data.
method L2-regularized dipole splitting criteria with kernel methods.
result Non-linear splits using polynomial and Gaussian kernels show similar predictive power but often smaller tree sizes.
This paper presents a new ensemble learning method for classification problems called projection pursuit random forest (PPF). PPF uses the PPtree algorithm introduced in Lee et al. (2013). In PPF, trees are constructed by splitting on linear combinations of randomly chosen variables. Projection pursuit is used to choos…
New methods improve prediction regions for high-dimensional data.
problem Creating effective prediction regions for high-dimensional data.
method CD-split and HPD-split methods that combine split method and data-driven partition.
result CD-split and HPD-split converge to oracle highest predictive density set and satisfy local and asymptotic conditional validity.
New spheres can split a 4D link in ways not possible in 3D.
problem Exploring how splitting spheres behave in 4D space.
method Constructing specific 2-component surface-links in S4. result Found non-isotopic splitting spheres in S4∖Lm,n. This paper studies properties of weak reducing pairs in critical Heegaard splittings.
problem Characterize weak reducing pairs in critical Heegaard splittings.
method Analyze the properties of weak reducing pairs in critical Heegaard splittings.
result Provide a necessary condition for a Heegaard surface to be critical.
We describe Information Forests, an approach to classification that generalizes Random Forests by replacing the splitting criterion of non-leaf nodes from a discriminative one -- based on the entropy of the label distribution -- to a generative one -- based on maximizing the information divergence between the class-con…
We show that if a split link is obtained from a split link L in S3 by 1/n-Dehn surgery along a trivial knot C, then the link L∪C is splittable. That is to say, it is impossible to obtain a split link from a split link via a non-trivial twisting. As its corollary, we completely determine when a trivial li…
Study evaluates when splitting classifiers can improve performance despite disparate treatment.
problem Impact of disparate treatment in classification models.
method Comparison of split classifiers and group-blind classifiers, quantifying performance improvement.
result Proves an equivalent expression for the benefit-of-splitting which can be efficiently computed.
New methods convert complex link presentations to simpler, recognizable forms.
problem Representing split and composite links in a clear, recognizable way.
method Pocket and flip moves to transform link presentations.
result Pocket moves are the only obstruction to representing split links by split plat presentations.
In (\cite{zhang2014nonlinear,zhang2014nonlinear2}), we have viewed machine learning as a coding and dimensionality reduction problem, and further proposed a simple unsupervised dimensionality reduction method, entitled deep distributed random samplings (DDRS). In this paper, we further extend it to supervised learning …
The splitting number of a link is the minimal number of crossing changes between different components required, on any diagram, to convert it to a split link. We introduce new techniques to compute the splitting number, involving covering links and Alexander invariants. As an application, we completely determine the sp…