arXiv research

New oracle uses uncertainty for active classification with noisy feedback.

Pairwise "same-cluster" queries are one of the most widely used forms of supervision in semi-supervised clustering. However, it is impractical to ask human oracles to answer every query correctly. In this paper, we study the influence of allowing "not-sure" answers from a weak oracle and propose an effective algorithm …

Paper addresses online alignment of large language models under uncertain preference feedback.

Study examines how uncertainty visualization affects analyst trust in automated classification systems.

Semi-supervised active clustering (SSAC) utilizes the knowledge of a domain expert to cluster data points by interactively making pairwise "same-cluster" queries. However, it is impractical to ask human oracles to answer every pairwise query. In this paper, we study the influence of allowing "not-sure" answers from a w…

Paper tackles NNS under uncertainty with improved algorithms.

A desirable property of interpretable models is small size, so that they are easily understandable by humans. This leads to the following challenges: (a) small sizes typically imply diminished accuracy, and (b) bespoke levers provided by model families to restrict size, e.g., L1 regularization, might be insufficient to…

A new Thompson Sampling framework handles uncertainty by imputing missing data.

New algorithm learns POMDPs without computational oracles.

CJE calibrates cheap LLM judges against an oracle, achieving high accuracy at a fraction of the cost.

We study contextual linear bandit problems under feature uncertainty, where the features are noisy and have missing entries. To address the challenges posed by this noise, we analyze Bayesian oracles given the observed noisy features. Our Bayesian analysis reveals that the optimal hypothesis can significantly deviate f…

ACFS optimizes spectral risk under decision-dependent uncertainty using adaptive forest sampling.

Bayesian-guided method selects optimal design from large candidate pool.

New algorithm learns FMDP structure while minimizing regret.

PCS-UQ framework improves uncertainty quantification for machine learning models.

Sufficient supervised information is crucial for any machine learning models to boost performance. However, labeling data is expensive and sometimes difficult to obtain. Active learning is an approach to acquire annotations for data from a human oracle by selecting informative samples with a high probability to enhance…

A Bayesian approach to multilabel classification using tree-based models.

QActor optimizes learning from noisy labeled data streams by querying experts for clean labels.

We develop a robust RL algorithm for off-dynamics environments with improved suboptimality bounds and computational efficiency.

Neural-$σ^2$-LinearUCB improves regret in neural contextual bandits.

Improved stock selection through predictive fundamentals and uncertainty estimates.

New approach optimizes decisions based on uncertainty in predictions.

Novel method to quantify aleatoric uncertainty of treatment effects from observational data.

Unified Bayesian-AI framework improves epidemiological risk prediction and uncertainty quantification.

Active learning selects most informative unlabeled samples for labeling.

New principle optimizes bandit decisions with context.

Adaptive Bayesian learning aggregates experts to improve performance.

This thesis enhances ML reliability by selectively abstaining from predictions when uncertain.

The paper introduces algorithms for uncertainty quantification in metric spaces.

Improved MALA method for neural networks uncertainty quantification.

Proposes a weighted conformal approach for cluster label uncertainty.

New model-free RL algorithm tackles robust average-reward problems with finite sample complexity analysis.

Oracle-efficient algorithms reduce combinatorial semi-bandit regret to logarithmic time.

New analysis shows Thompson Sampling can work with greedy approximations in combinatorial bandits.

Graph convolution networks (GCN) have emerged as the leading method to classify node classes in networks, and have reached the highest accuracy in multiple node classification tasks. In the absence of available tagged samples, active learning methods have been developed to obtain the highest accuracy using the minimal …

Develops asymptotic theory for deep Cox models to enable valid inference.

New algorithms sample convex bodies using Markov chains and restricted Gaussian oracles.

MAMBA learns policies competitive with multiple conflicting oracles.

We study the problem of interactively learning a binary classifier using noisy labeling and pairwise comparison oracles, where the comparison oracle answers which one in the given two instances is more likely to be positive. Learning from such oracles has multiple applications where obtaining direct labels is harder bu…

We study the problem of training machine learning models incrementally with batches of samples annotated with noisy oracles. We select each batch of samples that are important and also diverse via clustering and importance sampling. More importantly, we incorporate model uncertainty into the sampling probability to com…

Quantum oracles help identify counterfactuals better than classical ones.

SoQal reduces oracle label requests in active learning by up to 35%.

We consider the problem of minimizing the sum of submodular set functions assuming minimization oracles of each summand function. Most existing approaches reformulate the problem as the convex minimization of the sum of the corresponding Lovász extensions and the squared Euclidean norm, leading to algorithms requiring …

New oracles improve stochastic optimization with noisy or biased measurements.

Algorithm solves online binary classification and infinite games using ERM oracle.

Proposes a Bayesian framework for causal inference without explicit likelihood modeling.

New algorithm learns efficiently with a simple 'yes/no' oracle.

Differentially private conformal prediction improves statistical efficiency.