arXiv research

Two algorithms for linear contextual bandits with rare updates achieve optimal regret and efficiency.

DG separates successes and failures by gating updates with advantage and surprisal.

Two algorithms achieve optimal regret with limited adaptivity in multinomial logistic bandits.

Excessively changing policies in many real world scenarios is difficult, unethical, or expensive. After all, doctor guidelines, tax codes, and price lists can only be reprinted so often. We may thus want to only change a policy when it is probable that the change is beneficial. In cases that a policy is a threshold on …

Proposes a method to refine PDE-driven high-dimensional rare-event simulation.

A novel resampling technique addresses class imbalance in imbalanced datasets.

The paper analyzes dynamics of momentum in high dimensions with sparse updates.

Study on network-valued processes with asynchronous updates, proving consistency in community and changepoint estimation.

We combine Bayesian networks (BNs) and structural reliability methods (SRMs) to create a new computational framework, termed enhanced Bayesian network (eBN), for reliability and risk analysis of engineering structures and infrastructure. BNs are efficient in representing and evaluating complex probabilistic dependence …

Reservoir computing predicts chaotic systems for long horizons with sparse updates.

Second-order methods for neural network optimization have several advantages over methods based on first-order gradient descent, including better scaling to large mini-batch sizes and fewer updates needed for convergence. But they are rarely applied to deep learning in practice because of high computational cost and th…

New algorithms reduce matching regret by limiting frequent updates.

A continual learning agent should be able to build on top of existing knowledge to learn on new data quickly while minimizing forgetting. Current intelligent systems based on neural network function approximators arguably do the opposite---they are highly prone to forgetting and rarely trained to facilitate future lear…

Learning to remember long sequences remains a challenging task for recurrent neural networks. Register memory and attention mechanisms were both proposed to resolve the issue with either high computational cost to retain memory differentiability, or by discounting the RNN representation learning towards encoding shorte…

Methods proposed in the literature towards continual deep learning typically operate in a task-based sequential learning setup. A sequence of tasks is learned, one at a time, with all data of current task available but not of previous or future tasks. Task boundaries and identities are known at all times. This setup, h…

A TTA framework improves forecasting accuracy in non-stationary time series.

DaRE forests enable efficient data deletion from random forests.

COMMOD debiases models with minimal and interpretable changes.

New RL algorithm achieves nearly optimal performance for linear MDPs.

It is common in modern prediction problems for many predictor variables to be counts of rarely occurring events. This leads to design matrices in which many columns are highly sparse. The challenge posed by such "rare features" has received little attention despite its prevalence in diverse areas, ranging from natural …

Generative model simulates rare events for better decision making.

AI boosts study of rare weather extremes with lower costs.

Safe RL for autonomous vehicles using PCPO with trust regions and parallel learners.

Rare diseases affecting 350 million individuals are commonly associated with delay in diagnosis or misdiagnosis. To improve those patients' outcome, rare disease detection is an important task for identifying patients with rare conditions based on longitudinal medical claims. In this paper, we present a deep learning m…

Develops a method to estimate rare-event probabilities under distributional uncertainty.

Reduces false positives in classifying rare online platforms.

This paper evaluates data enrichment techniques for rare event detection in manufacturing.

Enhanced diffusion sampling improves rare event sampling in biomolecular simulations.

Enhanced diffusion sampling tackles rare event sampling in biomolecular simulations.

Proposes a method to ensure accurate estimation of rare events in AI systems.

In many situations, classes of data points of primary interest also happen to be those that are least numerous. A well-known example is detection of fraudulent transactions among the collection of all financial transactions, the vast majority of which are legitimate. These types of problems fall under the label of `rar…

New method for inference on covariates in NMF with random effects.

A new model learns preferences incrementally without personal data.

WEST uses EHRs and expert cases to improve rare disease phenotyping.

Rare diseases affect a relatively small number of people, which limits investment in research for treatments and cures. Developing an efficient method for rare disease detection is a crucial first step towards subsequent clinical research. In this paper, we present a semi-supervised learning framework for rare disease …

New algorithms protect user data while optimizing personalized decisions.

Given a labeled dataset that contains a rare (or minority) class of of-interest instances, as well as a large class of instances that are not of interest, how can we learn to recognize future of-interest instances over a continuous stream? We introduce RaRecognize, which (i) estimates a general decision boundary betwee…

This research examines rare spurious correlations in neural networks and their impact on accuracy and privacy.

In rare disease physician targeting, a major challenge is how to identify physicians who are treating diagnosed or underdiagnosed rare diseases patients. Rare diseases have extremely low incidence rate. For a specified rare disease, only a small number of patients are affected and a fractional of physicians are involve…

Simulates patient pathways to detect delayed rare disease diagnoses.

Markov chain Monte Carlo (MCMC) algorithms for hidden Markov models often rely on the forward-backward sampler. This makes them computationally slow as the length of the time series increases, motivating the development of sub-sampling-based approaches. These approximate the full posterior by using small random subsequ…

Model predicts travel time under rare conditions using a vector-space model.

Deep CNNs struggle with rare taxa; one-class classifiers help identify them.

Study compares resampling methods for rare event prediction in longitudinal studies.

The paper analyzes logistic regression for rare events data, deriving new insights on estimator efficiency and sampling strategies.

New test uncovers causal links in rare event dynamics.

Proposes Ada-Sit method for mortality prediction of rare diseases.

PRESTO improves rare event prediction by shrinking towards proportional odds model.