CcGAN tackles conditional image generation for continuous labels.
problem Mathematical challenges in conditioning on continuous, scalar labels.
method Proposes novel empirical losses and label input methods for continuous conditional GANs.
result CcGAN generates diverse, high-quality images from continuous labels.
AV-CPL uses continuous pseudo-labels for AVSR combining labeled and unlabeled data.
problem Improving AVSR performance with labeled and unlabeled data.
method Semi-supervised method using continuous pseudo-labels generated by the same AVSR model.
result Significant improvements in VSR performance on LRS3 dataset.
OSAMD adapts online to changing distributions with limited labels.
problem Models struggle with continual distribution shifts and expensive labeling in changing environments.
method Online Active Continual Adaptation with OSAMD, an online teacher-student structure and margin-based criterion.
result OSAMD achieves favorable dynamic regret bounds under changing environments with limited labels.
A new method for continual learning in GANs learns new modes with limited data.
problem Learning new target modes with limited samples while preserving previously learned ones.
method Mode-affinity score for generative modeling, generator replay, and weighted label generation.
result Gains over state-of-the-art methods, even with fewer training samples.
Earth observation embeddings can convert discrete biome maps into continuous representations that better capture ecological variation.
problem Biome maps impose categorical boundaries that compress continuous variation in biotic communities.
method Fit a linear classifier on Earth observation embeddings to predict biome labels.
result Continuous biome representation outperforms discrete biome labels for predicting species occurrence.
This research tackles imbalanced continual learning with a new sampling strategy.
problem Long-tailed distribution in multi-label datasets.
method Partitioning Reservoir Sampling (PRS) for balanced knowledge of head and tail classes.
result The proposed PRS strategy maintains a balanced knowledge of both head and tail classes.
We present a new replay-based method of continual classification learning that we term "conditional replay" which generates samples and labels together by sampling from a distribution conditioned on the class. We compare conditional replay to another replay-based continual learning paradigm (which we term "marginal rep…
Scarcity of labeled data is a bottleneck for supervised learning models. A paradigm that has evolved for dealing with this problem is data programming. An existing data programming paradigm allows human supervision to be provided as a set of discrete labeling functions (LF) that output possibly noisy labels to input in…
ORDisCo learns from unlabeled data to improve semi-supervised continual learning.
problem Lack of effective use of unlabeled data in semi-supervised continual learning.
method Deep Online Replay with Discriminator Consistency (ORDisCo) that continually passes the learned data distribution to a classifier and selectively stabilizes discriminator parameters.
result Significant performance improvement on various semi-supervised learning benchmark datasets.
Investigates Lipschitz continuity in neural networks across various settings.
problem Understanding the Lipschitz behavior of neural networks.
method Empirical investigation of Lipschitz bounds in different neural network architectures and datasets.
result Remarkable fidelity of the lower Lipschitz bound and a Double Descent trend in both upper and lower bounds.
While state-of-the-art kernels for graphs with discrete labels scale well to graphs with thousands of nodes, the few existing kernels for graphs with continuous attributes, unfortunately, do not scale well. To overcome this limitation, we present hash graph kernels, a general framework to derive kernels for graphs with…
This work tackles continual learning with semi-supervised data, showing that even with minimal labeled data, performance can match full-supervised methods.
problem Training deep networks on a stream of tasks without forgetting, especially when labeled data is scarce.
method Designing a novel CSSL method that leverages metric learning and consistency regularization to learn from both labeled and unlabeled data.
result Our method outperforms state-of-the-art methods trained with full supervision, achieving comparable performance with only 25% labeled data.
Continuous integration is an indispensable step of modern software engineering practices to systematically manage the life cycles of system development. Developing a machine learning model is no difference - it is an engineering process with a life cycle, including design, implementation, tuning, testing, and deploymen…
New PL method improves ASR accuracy without pseudo-labels.
problem Improving ASR accuracy with limited labeled data.
method End-to-end continuous pseudo-labeling with soft-labels.
result Soft-labels can lead to model collapse, but regularization can mitigate this.
One of the challenges in display advertising is that the distribution of features and click through rate (CTR) can exhibit large shifts over time due to seasonality, changes to ad campaigns and other factors. The predominant strategy to keep up with these shifts is to train predictive models continuously, on fresh data…
We develop methodology for visualization of labeled mixed-featured datasets. We first investigate datasets with continuous features where our Max-Ratio Projection (MRP) method utilizes the group information in high dimensions to provide distinctive lower-dimensional projections that are then displayed using Radviz3D. O…
We study the problem of attacking a machine learning model in the hard-label black-box setting, where no model information is revealed except that the attacker can make queries to probe the corresponding hard-label decisions. This is a very challenging problem since the direct extension of state-of-the-art white-box at…
Crowdsourcing has become a popular method for collecting labeled training data. However, in many practical scenarios traditional labeling can be difficult for crowdworkers (for example, if the data is high-dimensional or unintuitive, or the labels are continuous). In this work, we develop a novel model for crowdsourcin…
CT-OT Flow estimates continuous-time dynamics from discrete snapshots.
problem Estimating continuous-time dynamics from temporally aggregated snapshots with noisy or uncertain timestamps.
method Two-stage framework: aligning neighboring intervals via partial optimal transport (POT) and reconstructing a continuous-time distribution through temporal kernel smoothing.
result Reduces distributional and trajectory errors compared with existing methods across synthetic and real datasets.
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.
The goal of few-shot learning is to learn a model that can recognize novel classes based on one or few training data. It is challenging mainly due to two aspects: (1) it lacks good feature representation of novel classes; (2) a few of labeled data could not accurately represent the true data distribution and thus it's …
After learning a concept, humans are also able to continually generalize their learned concepts to new domains by observing only a few labeled instances without any interference with the past learned knowledge. In contrast, learning concepts efficiently in a continual learning setting remains an open challenge for curr…
Paper tackles online adaptation to changing label distributions.
problem Adapting machine learning models to changing label distributions in real-world settings.
method Leverages novel analysis to show estimation of expected test loss is possible without true labels. Proposes adaptation algorithms inspired by classical online learning techniques.
result Empirically verified that OGD is particularly effective and robust to various label shift scenarios.
Weak labels can significantly speed up learning for strong tasks.
problem Learning with limited strong labels.
method Using weak labels to accelerate learning of strong tasks.
result Weak labels can accelerate learning to O(icefrac1n) rate. The utility of learning a dynamics/world model of the environment in reinforcement learning has been shown in a many ways. When using neural networks, however, these models suffer catastrophic forgetting when learned in a lifelong or continual fashion. Current solutions to the continual learning problem require experie…
We created financial benchmarks for distribution shifts in crude oil prices and volatility.
problem Scarcity of task-labeled time-series benchmarks in finance.
method Transformed asset price data into volatility proxies, generated task labels based on distribution shifts, and made datasets publicly available.
result Inclusion of task labels improves continual learning algorithms' performance on real-world data.
Detects harmful shifts without labels for model performance.
problem Detecting distribution shifts without access to labels.
method Uses a proxy derived from predictions of an error estimator.
result High power and false alarm control under various shifts.
We develop methods to efficiently approximate data in metric spaces without additional assumptions.
problem Efficiently approximating data in metric spaces without imposing structural assumptions.
method Identify discrete modulus of continuity, investigate consistency, propose algorithm, and develop approximation theory.
result Consistent approximation of data in metric spaces without structural assumptions.
A framework learns dynamic soft labels to improve model generalization and accuracy.
problem Models trained on one-hot labels overfit and are sensitive to noisy annotations.
method Proposes a framework where labels are treated as learnable parameters, adapting dynamically during optimization.
result Consistent gains across different datasets and architectures, improving ResNet18 by 2.1% on CIFAR100.
New research shows unlabeled data is equally valuable as labeled data in certain semi-supervised learning scenarios.
problem Improving learning performance with limited labeled data.
method Statistical models with continuous parameters, showing equal utility of unlabeled data under specific conditions.
result The learning rate of semi-supervised learning scales similarly to supervised learning when unlabeled data is abundant.
Paper introduces RPWithPrior for efficient label differential privacy in regression.
problem Protecting user privacy in regression tasks with minimal accuracy loss.
method Modeling responses as continuous random variables, avoiding discretization; estimating optimal intervals for randomized responses.
result RPWithPrior algorithm guarantees ε-label differential privacy and outperforms existing methods.
Time-continuous dimensional descriptions of emotions (e.g., arousal, valence) allow researchers to characterize short-time changes and to capture long-term trends in emotion expression. However, continuous emotion labels are generally not synchronized with the input speech signal due to delays caused by reaction-time, …
A number of modern learning tasks involve estimation from heterogeneous information sources. This includes classification with labeled and unlabeled data as well as other problems with analogous structure such as competitive (game theoretic) problems. The associated estimation problems can be typically reduced to solvi…
New method tackles dynamic data labeling issues with limited labels.
problem Dynamic data labeling with scarce labeled instances.
method Instance exploitation technique for aggressive model adaptation.
result Aggressive model adaptation leads to better performance than standard methods.
Structured prediction energy networks (SPENs; Belanger & McCallum 2016) use neural network architectures to define energy functions that can capture arbitrary dependencies among parts of structured outputs. Prior work used gradient descent for inference, relaxing the structured output to a set of continuous variables a…
The paper proposes a method to estimate treatment effects using surrogates when primary outcomes are missing.
problem Missing primary outcomes in causal inference applications can lead to biased estimates.
method Doubly robust method that uses both labeled and unlabeled data, incorporating surrogates.
result The proposed estimator is asymptotically normal and has improved variance compared to methods using only labeled data.
We introduce a novel method to combat label noise when training deep neural networks for classification. We propose a loss function that permits abstention during training thereby allowing the DNN to abstain on confusing samples while continuing to learn and improve classification performance on the non-abstained sampl…
Study how noisy labels affect semi-supervised learning.
problem Effect of noisy labels on semi-supervised learning performance.
method Proposed an algorithm derived from a continuous relaxation of the Maximum A Posteriori (MAP) estimator for a Degree Corrected Stochastic Block Model (DC-SBM).
result Our approach achieves promising performance even with very noisy labeled data.
A new method for online multi-label stream classification.
problem Challenges in classifying continuous data streams with concept drift and delayed labels.
method Online unsupervised incremental method based on self-organizing maps.
result The method is highly competitive in both stationary and concept drift scenarios.
Binary PheNorm extends phenotype labeling for EHRs using binary silver labels.
problem Lack of gold-standard phenotype labels in EHR studies.
method Proposes Binary PheNorm, an extension that uses binary silver labels directly in phenotype scoring.
result Binary PheNorm achieved strong discrimination using binary labels alone and improved performance when combined with count labels.
Paper improves deep learning for instance-level classification from label proportions.
problem Dealing with noisy pseudo-labeling and high-entropy class distributions in LLP.
method Introducing a two-stage training approach with constrained optimization and mixup strategy.
result Significant performance improvement in instance-level classification.
Paper tackles continuous transfer learning with evolving target domains.
problem Challenges of negative transfer in evolving target domains.
method Proposes label-informed C-divergence for measuring distribution shift and negative transfer.
result Demonstrates effectiveness of TransLATE framework in minimizing classification error and C-divergence.
Paper tackles robust imitation learning from noisy demonstrations.
problem Learning from noisy demonstrations is challenging.
method Optimizes a classification risk with a symmetric loss, combining pseudo-labeling and co-training.
result Our method is more robust than state-of-the-art methods.
Conditional Random Fields (CRF) are frequently applied for labeling and segmenting sequence data. Morency et al. (2007) introduced hidden state variables in a labeled CRF structure in order to model the latent dynamics within class labels, thus improving the labeling performance. Such a model is known as Latent-Dynamic…
A novel framework for regression with multiple experts, addressing challenges in infinite and continuous label spaces.
problem Challenges in regression with multiple experts due to the infinite and continuous nature of the label space.
method Introduces a novel framework for regression with deferral, analyzing both single-stage and two-stage scenarios with new surrogate loss functions.
result Proves H-consistency bounds for both single-stage and two-stage methods, providing stronger guarantees than Bayes consistency. We formulate a supervised learning problem, referred to as continuous ranking, where a continuous real-valued label Y is assigned to an observable r.v. X taking its values in a feature space X and the goal is to order all possible observations x in X by means of a scoring function $s:\mathcal{X}…
This work tackles robust multi-source domain adaptation under label shift.
problem Label shift and data contamination in multi-source domain adaptation.
method Domain-weighted empirical risk minimization framework with refinement procedure.
result The proposed method achieves superior performance in multi-category classification problems.
Modern neural networks are typically trained in an over-parameterized regime where the parameters of the model far exceed the size of the training data. Such neural networks in principle have the capacity to (over)fit any set of labels including pure noise. Despite this, somewhat paradoxically, neural network models tr…