New insights on active sequential prediction for mean estimation.
problem Active sequential prediction-powered mean estimation problem.
method Combining uncertainty-based suggestion with a constant probability, analyzing non-asymptotic bounds, and using no-regret learning.
result The optimal query probability is close to the constraint when using no-regret learning.
Active learning selects samples for labeling to build accurate models with minimal labeled data.
problem Costly acquisition of labeled data in supervised learning.
method Adaptive selection of unlabeled data samples for labeling.
result Efficient model building with minimal labeled data.
PDTS improves robustness in sequential decision-making.
problem Robust active task sampling for efficient and reliable decision-making.
method Characterizes robust active task sampling as a Markov decision process, proposes PDTS method.
result Significantly improves zero-shot and few-shot adaptation robustness.
New method uses neural tangent kernel for efficient active learning.
problem Efficiently approximating deep learning's look-ahead selection criteria.
method Approximates retraining with neural tangent kernel for active learning.
result Approximation works asymptotically and enables sequential active learning.
Paper proves conformal prediction works for any data distribution.
problem Quantifying risk in AI systems with non-exchangeable data.
method Developed a method to extend conformal prediction to any data distribution.
result Valid conformal prediction guarantees for any data distribution.
Parallelizes active learning for Bayesian inference using Nested Sampler.
problem Expensive likelihood evaluations in complex experiments.
method Uses Nested Sampler to generate nearly-optimal batches of candidates in parallel.
result Comparable accuracy to sequential conditioning with efficient parallelization.
Decoupled PFNs improve sequential decision-making by separating epistemic and aleatoric uncertainties.
problem Sequential decision-making requires distinguishing between epistemic uncertainty about latent signals and irreducible aleatoric observation noise.
method Developed a decoupled PFN architecture that uses query-level labels to train separate heads for latent signal and aleatoric noise.
result Empirically, decoupled PFNs mitigate the failure mode of total-variance exploration in noisy and heteroscedastic settings.
NM-PPG optimizes adaptive feature acquisition in POMDPs for better predictions.
problem Optimizing adaptive feature acquisition in prediction problems with costly features.
method Non-myopic pathwise policy gradients (NM-PPG) with continuous relaxation and straight-through rollout.
result NM-PPG outperforms state-of-the-art AFA methods on synthetic and real-world datasets.
New methods improve Bayesian inference and decision-making in online learning.
problem Current Bayesian deep learning does not fully utilize joint predictives for sequential decision-making.
method Proposes new evaluation settings for active learning and active sampling, focusing on marginal and joint cross-entropies.
result Initial experiments suggest challenges in applying current BDL inference techniques in high-dimensional spaces.
RAN model recognizes multiple activities from unlabeled sensor data.
problem Handling weakly labeled multi-activity data from wearable sensors.
method Recurrent Attention Networks (RAN) for sequential multi-activity recognition and localization.
result RAN model can infer multiple activities and determine activity locations from unlabeled data.
Active learning improves GP regression on complex, high-dimensional data.
problem Improving Gaussian Process regression in high-dimensional spaces with discontinuous functions.
method Combines manifold learning with active learning to optimize data selection and reduce dimensionality.
result Superior performance over random learning in synthetic data experiments.
A method to improve sequential learning by keeping past data errors in check.
problem Challenges in sequential learning with Gaussian processes due to accumulating errors.
method Memory-based dual sparse variational Gaussian processes.
result Improves accuracy in inference and learning for various applications.
We consider the problem of active feature acquisition, where we sequentially select the subset of features in order to achieve the maximum prediction performance in the most cost-effective way. In this work, we formulate this active feature acquisition problem as a reinforcement learning problem, and provide a novel fr…
Study efficient sequential evaluation of large language models using historical data.
problem Sequentially evaluate a new large language model (LLM) on a fixed question set.
method Construct a confidence sequence (CS) and design active querying rules to shrink CS width.
result Simple uniform sampling can sometimes outperform adaptive querying rules.
Warm-start strategies speed up GP inference by 19x.
problem Efficient sequential inference in Gaussian processes.
method Three warm-start strategies exploiting smaller linear systems.
result Warm-starting achieves up to 19x speed-up in convergence.
AI learns to learn sequentially without forgetting.
problem Preventing catastrophic forgetting in machine learning models.
method Meta-learning a neuromodulatory activation-gating function to control selective activation in deep neural networks.
result State-of-the-art continual learning performance with 600 classes (9,000 updates).
A new method uses active learning to improve bile duct stone evaluation.
problem Efficiently collecting necessary patient data in sequential healthcare decisions.
method Developed an active learning-based multistage sequential decision-making model.
result Improves estimation efficiency by 62%-1838% compared to baseline methods.
Bayesian Perceptron offers fully Bayesian neural networks without complex computations.
problem Lack of uncertainty quantification in neural networks.
method Bayesian inference framework for perceptron training and predictions in closed-form.
result Analytical expressions for perceptron's output and weight learning provided.
Enhances DGP surrogates for efficient active learning.
problem Efficiently learning from expensive simulations with abrupt changes.
method Novel elliptical slice sampling for uncertainty quantification and active learning.
result Smaller training sets lead to effective and computationally tractable models.
Various strategies for active learning have been proposed in the machine learning literature. In uncertainty sampling, which is among the most popular approaches, the active learner sequentially queries the label of those instances for which its current prediction is maximally uncertain. The predictions as well as the …
Learning predictive models from small high-dimensional data sets is a key problem in high-dimensional statistics. Expert knowledge elicitation can help, and a strong line of work focuses on directly eliciting informative prior distributions for parameters. This either requires considerable statistical expertise or is l…
Due to the popularity of the Internet and smart mobile devices, more and more financial transactions and activities have been digitalized. Compared to traditional financial fraud detection strategies using credit-related features, customers are generating a large amount of unstructured behavioral data every second. In …
Active learning method reduces labeling cost for regression models with aggregated data.
problem Reducing labeling cost for training regression models with aggregated data.
method Sequentially selects sets to be labeled using mutual information quantifying model parameter uncertainty.
result Achieves better predictive performance with fewer labeled sets.
New active learning strategy improves decision-making accuracy.
problem Maximizing decision-making accuracy in sequential data acquisition.
method Introduces a novel active learning criterion that maximizes expected information gain on the posterior decision distribution.
result Improved performance in decision-making accuracy compared to existing alternatives.
New activation function BrownianReLU improves LSTM network performance on financial time series.
problem Gradient instability in noisy financial time series data.
method Introduces BrownianReLU, a stochastic activation function based on Brownian motion.
result Significantly improved predictive accuracy and generalization on financial datasets.
Extends risk control to adaptive data collection, anytime-valid guarantees.
problem Ensuring safety of machine learning models with critical risk measures.
method Sequential risk controlling prediction sets (RCPS) for adaptive data collection and active labeling.
result Anytime-valid guarantees for risk control in sequential data collection.
Deciding what and when to observe is critical when making observations is costly. In a medical setting where observations can be made sequentially, making these observations (or not) should be an active choice. We refer to this as the active sensing problem. In this paper, we propose a novel deep learning framework, wh…
Efficient algorithms identify true hypothesis from many options with minimal actions.
problem Identifying true hypothesis from a large set of options with minimal actions.
method Greedy approximation algorithms for active sequential hypothesis testing.
result First approximation guarantees for ASHT, independent of the number of hypotheses.
Obesity is a serious public health concern world-wide, which increases the risk of many diseases, including hypertension, stroke, and type 2 diabetes. To tackle this problem, researchers across the health ecosystem are collecting diverse types of data, which includes biomedical, behavioral and activity, and utilizing m…
Develops a new framework for analyzing sequential decision-making problems using information theory.
problem Lack of information-theoretic generalization bounds for sequential decision-making problems.
method Introduces a sequential supersample framework that separates learner filtration from proof-side enlargement, controlling the generalization gap by sequential CMI.
result Establishes a sequential CMI that controls the generalization gap in sequential decision-making problems.
We develop a novel "decouple-recouple" dynamic predictive strategy and contribute to the literature on forecasting and economic decision making in a data-rich environment. Under this framework, clusters of predictors generate different latent states in the form of predictive densities that are later synthesized within …
Enhanced network threat detection using KG, LLM, and imbalanced learning.
problem Challenges in network threat detection due to complex attack patterns and limited historical data.
method Integrated framework combining Knowledge Graph, Imbalanced Learning, and Large Language Model.
result Improved threat capture rate by 3%-4% and increased interpretability of risk predictions.
Most prior work on active learning of classifiers has focused on sequentially selecting one unlabeled example at a time to be labeled in order to reduce the overall labeling effort. In many scenarios, however, it is desirable to label an entire batch of examples at once, for example, when labels can be acquired in para…
Gaussian processes help in modeling complex, nonlinear relationships in signal processing.
problem Modeling complex, nonlinear relationships in signal processing.
method Sequential inference for Gaussian processes.
result Gaussian processes enable efficient and accurate modeling of complex relationships.
Describes state variables in sequential decision problems, linking them to Markovian and non-Markovian models.
problem Sequential decision problems, especially in active learning and POMDPs, where decisions affect what is observed and learned.
method Canonical framework and novel two-agent perspective of POMDPs, defining state variables to claim Markovian or non-Markovian models.
result Properly modeled sequential decision problems are Markovian, while real decision problems are often non-Markovian.
VSD efficiently learns conditional distributions for combinatorial designs.
problem Learning conditional distributions for rare combinatorial designs.
method Variational Search Distributions (VSD) using variational inference.
result VSD outperforms existing methods on real sequence-design problems.
Functional Magnetic Resonance Imaging (fMRI) provides dynamical access into the complex functioning of the human brain, detailing the hemodynamic activity of thousands of voxels during hundreds of sequential time points. One approach towards illuminating the connection between fMRI and cognitive function is through dec…
Continual Learning in artificial neural networks suffers from interference and forgetting when different tasks are learned sequentially. This paper introduces the Active Long Term Memory Networks (A-LTM), a model of sequential multi-task deep learning that is able to maintain previously learned association between sens…
Solving sequential decision prediction problems, including those in imitation learning settings, requires mitigating the problem of covariate shift. The standard approach, DAgger, relies on capturing expert behaviour in all states that the agent reaches. In real-world settings, querying an expert is costly. We propose …
A new method uses active learning to improve chemical simulation efficiency.
problem Efficiently estimating equilibrium-based chemical simulations.
method Sequential data-driven approach using Gaussian process uncertainty.
result Significantly reduced number of function evaluations.
Bayesian search optimizes exploration of feasible solutions under expensive constraints.
problem Identifying feasible solutions in computationally expensive constraint spaces.
method Bayesian models with an acquisition function for efficient exploration and exploitation.
result The proposed acquisition function improves the prediction of feasibility.
New method constructs confidence sets for GLMs via game theory.
problem Developing reliable confidence intervals for GLM parameters.
method Reduction to sequential prediction games with low regret.
result Online-to-confidence-set conversions provide new types of intervals.
A new active learning strategy for real-time data in production.
problem High annotation costs and time required for quality inspections of unlabeled data.
method A stream-based active learning approach using optimal experimental design theory.
result The proposed algorithm allows for faster reduction in prediction error.
Improved inference efficiency for complex simulations.
problem Challenges in performing inference under resource-intensive stochastic simulators.
method Active sequential neural posterior estimation (ASNPE) integrating active learning into posterior estimation.
result Improved sample efficiency with low computational overhead.
This paper proposes new methods for ALR that consider informativeness, representativeness, and diversity.
problem Efficiently label samples for regression models with limited labeled data.
method Integrates informativeness, representativeness, and diversity in pool-based sequential active learning.
result Demonstrates effectiveness of new ALR approaches on 12 datasets.
Paper tackles active learning for GNNs, reducing annotation costs.
problem Efficiently label nodes on graphs to reduce GNN training costs.
method Formulates as a sequential decision process, trains GNN-based policy network with reinforcement learning.
result Trains a transferable active learning policy that generalizes across different domains.
The problem of active diagnosis arises in several applications such as disease diagnosis, and fault diagnosis in computer networks, where the goal is to rapidly identify the binary states of a set of objects (e.g., faulty or working) by sequentially selecting, and observing, (noisy) responses to binary valued queries. …
New method selects features for sequential decision making.
problem Dynamic feature selection for instance-wise decisions.
method Latent variable model trained in a supervised manner; reasoning across stochastic latent space.
result Outperforms existing methods on various datasets.