Bayesian fusion forest combines clinical and real-world data to estimate treatment effects.
problem Estimating treatment effects on survival from mixed data sources with confounding.
method Nonparametric Bayesian fusion forest combining randomised and real-world data, relaxing unconfoundedness assumption.
result Fusion identifies treatment benefits not seen in clinical trials alone.
New method optimizes experimental design for specific applications.
problem Inability to adapt causal inference methods to specific downstream applications.
method Task-specific experimental design and sampling strategies.
result Significantly reduces data requirements for achieving RCT performance.
Simpson's paradox can invalidate the results of medical trials if an unobserved variable is considered.
problem The validity of medical trial results can be invalidated by an unobserved confounding variable.
method Simulated controlled trials to demonstrate the paradox.
result The results of a trial can be reversed by including an unobserved confounding variable.
Estimates long-term effects from short-term experiments and observational data with unobserved confounders.
problem Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding.
method Combining regression residuals with short-term experimental outcomes to create an instrumental variable for estimating long-term causal effects.
result The estimator is unbiased and its variance is analytically studied.
Extends moderate deviations for a randomised Heston model.
problem Analyzing deviations in the Heston model with randomisation.
method Used Gärtner-Ellis theorem and sharp large deviations tools.
result Extended moderate deviations results for the randomised Heston model.
New method uses randomised signatures for generating financial time series data.
problem Generating synthetic financial time series data accurately.
method Introduced a Wasserstein-type distance based on discrete-time randomised signatures.
result Demonstrated universal approximation for randomised signatures on continuous functions.
Private machine learning framework using randomised response.
problem Private machine learning in adversarial environments.
method Randomised response for noisy data release.
result Consistent estimation of true machine learning model.
Randomised classifiers outperform deterministic ones in strategic classification.
problem Strategic modification of features by agents in classification tasks.
method Theoretical analysis of randomised classifiers in strategic classification.
result Randomised classifiers can achieve better accuracy than deterministic ones under certain conditions.
The paper examines challenges in achieving fair predictions using causal counterfactuals.
problem Achieving fair predictions using causal counterfactuals in fairness settings.
method Analyzes the limitations of causal models in fairness settings and the challenges of selecting counterfactuals.
result Causal models that capture counterfactuals are outside the class commonly considered in fairness literature.
The paper addresses privacy-preserving BAI in clinical trials and user studies.
problem Privacy-preserving Best Arm Identification in adaptive clinical trials and user studies.
method The paper derives lower bounds on sample complexity for BAI algorithms with differential privacy constraints and proposes private variants of Top Two algorithms.
result Private variants of Top Two algorithms achieve asymptotic optimality in terms of sample complexity for BAI problems under differential privacy constraints.
New framework for policy gradient methods in continuous time reinforcement learning.
problem Addressing policy gradient methods for continuous time reinforcement learning.
method Control randomisation technique to derive policy gradient representation for various Markovian control problems.
result Demonstrated application to optimal switching problems in the energy sector.
New algorithm improves game learning with randomised optimism.
problem Learning in matrix games with unknown payoffs and bandit feedback.
method Integrates evolutionary algorithms into bandit framework for randomised optimism.
result Achieves sublinear regret, outperforming classical methods.
We propose a randomised version of the Heston model-a widely used stochastic volatility model in mathematical finance-assuming that the starting point of the variance process is a random variable. In such a system, we study the small-and large-time behaviours of the implied volatility, and show that the proposed random…
Randomized exploration in linear bandits achieves optimal regret bounds.
problem Optimizing exploration in high-dimensional linear bandit problems.
method Analysis of Thompson sampling without forced optimism.
result Randomized exploration algorithms achieve an O ( d n log ( n ) ) O(d\sqrt{n} \log(n)) O ( d n log ( n )) regret bound in smooth, strongly convex action spaces. Improved Bayesian optimisation method using randomised Gaussian process UCB.
problem Improving performance in Bayesian optimisation.
method Developed a modified Gaussian process upper confidence bound (GP-UCB) acquisition function.
result The method achieves better performance than GP-UCB in various problems.
Two modified tests improve the reliability of evaluating explanation methods.
problem Methodological concerns in evaluating explanation methods for saliency maps.
method Proposed modifications to the Model Parameter Randomisation Test (MPRT): Smooth MPRT and Efficient MPRT.
result Enhanced metric reliability, facilitating more trustworthy deployment of explanation methods.
Numerous kinds of uncertainties may affect an economy, e.g. economic, political, and environmental ones. We model the aggregate impact by the uncertainties on an economy and its associated financial market by randomised mixtures of Lévy processes. We assume that market participants observe the randomised mixtures only …
We design a randomised parallel version of Adaboost based on previous studies on parallel coordinate descent. The algorithm uses the fact that the logarithm of the exponential loss is a function with coordinate-wise Lipschitz continuous gradient, in order to define the step lengths. We provide the proof of convergence …
Unified high-probability regret bounds for online convex optimisation with randomised gradient estimators.
problem Online convex optimisation with randomised gradient estimators for ℓ q \ell_q ℓ q -Lipschitz losses. method FTRL with randomised two-point finite-difference gradient estimators based on cone-measure sampling from ℓ r \ell_r ℓ r -spheres. result Unified high-probability regret bounds for all p , q , r ∈ [ 1 , ∞ ] p,q,r \in [1,\infty] p , q , r ∈ [ 1 , ∞ ] . A system uses randomisation to explore data guided by user knowledge and interests.
problem Creating an efficient exploratory data analysis system aware of user background knowledge and interests.
method Model user background knowledge with tiles, use constrained randomisation for efficient implementation, and apply linear projection pursuit to find informative views.
result The method is robust under noise, fast for interactive use, and gives understandable results for real-world data.
New method improves calibration of BayesCG for better uncertainty quantification.
problem Bayesian conjugate gradient method's poor calibration limits its utility.
method Randomized postiteration strategy to enhance posterior calibration.
result The method improves the distribution of posterior errors and enhances uncertainty quantification.
Improved control approach for correlated bandits with better performance.
problem General multi-armed bandit problem with correlated elements.
method Introducing entropy regularisation to obtain a smooth asymptotic approximation of the value function, leading to a semi-index approximation of the optimal decision process.
result Performance of Asymptotic Randomised Control (ARC) algorithm compares favorably with other approaches.
Develops a monitoring procedure to detect changes in large approximate factor models.
problem Detecting structural changes in large approximate factor models.
method Randomises the test statistic to create a sequence of i.i.d. statistics for monitoring changes.
result Very small probability of false detections and tight detection times of change-points.
Machine learning experiments often mislead due to unmet assumptions.
problem Machine learning experiments with pooled data may not meet necessary assumptions for unbiased causal effect estimation.
method Analysis of assumptions required for unbiased causal effect estimation in machine learning experiments.
result Practical applications of A/B-tests with machine learning models may not yield unbiased estimates of causal effect.
Machine learning predicts patient recruitment for clinical trials.
problem Improving patient recruitment prediction for clinical trials.
method Machine learning methods applied to historical clinical trial data.
result Reduced prediction error compared to current industry standards.
New bounds for sampling algorithms without log-concavity assumptions.
problem Sampling high-dimensional probability measures without log-concavity assumptions.
method Euler discretisation of SDEs with novel convergence rates and coupling construction.
result Explicit L 2 L^2 L 2 convergence rates and non-asymptotic bounds for sampling algorithms. Theoretical limits show experimental data can falsify but not validate causal estimates from observational studies.
problem Fundamental limits on validating causal estimates using experimental data in observational studies.
method Impossible inference framework, Gaussian Process based approach.
result Experimental data can falsify but not validate causal estimates from observational studies.
A new algorithm improves policy discovery in uncertain environments.
problem Discovering optimal policies in uncertain environments.
method Bayesian least-squares policy iteration (BLSPI) with randomised variant (RBLSPI).
result RBLSPI effectively discovers optimal policies by quantifying uncertainty.
Doctor2Vec learns doctor representations from EHRs for better clinical trial recruitment.
problem Identifying the right doctors for clinical trials based on EHR data and trial descriptions.
method Dynamic Memory Network with attention mechanism to learn doctor and trial representations.
result Improved performance by up to 8.7% in PR-AUC on real-world trials and EHR data.
Combines trial and observational data to improve policy evaluation.
problem External validity of randomized trial results in target populations.
method Uses covariate data to model trial sampling and certifies policy evaluations.
result Valid trial-based policy evaluations under model miscalibration.
MILCCI integrates labels across categories for better understanding of multi-trial data.
problem Understanding how labels encode multi-trial observations and disentangling their effects.
method Sparse per-trial decomposition leveraging label similarities within each category.
result MILCCI identifies interpretable components and integrates label information.
The paper compares methods for estimating heterogeneous treatment effects using multiple randomized trials.
problem Estimating heterogeneous treatment effects reliably and precisely with a single dataset is challenging.
method Non-parametric approaches for estimating heterogeneous treatment effects using data from multiple trials.
result Methods that directly allow for heterogeneity of the treatment effect across trials perform better than those that do not.
We propose and evaluate alternative ensemble schemes for a new instance based learning classifier, the Randomised Sphere Cover (RSC) classifier. RSC fuses instances into spheres, then bases classification on distance to spheres rather than distance to instances. The randomised nature of RSC makes it ideal for use in en…
C3T-Budget optimizes drug efficacy in dose-finding trials with budget and safety constraints.
problem Heterogeneous patient populations and budget constraints make dose-finding clinical trials challenging.
method Contextual constrained clinical trial algorithm that maximizes drug efficacy while learning subgroup responses.
result Demonstrates efficient budget usage and balanced learning-treatment trade-off in simulated trials.
New study shows non-adaptive trials can be outperformed by adaptive designs in treatment selection.
problem Determining the best allocation of resources in clinical trials.
method Analysis of batched arm elimination designs and comparison with completely randomized trials.
result Simple adaptive designs universally and strictly dominate non-adaptive completely randomized trials for at least three treatment arms.
New method detects biomarker-treatment interactions in clinical trials.
problem Detecting interactions between high-dimensional biomarkers and treatments in randomized trials.
method Two-stage penalized regression screening using ridge regression for multivariate screening.
result Ridge regression screening provides greater power than traditional methods in correlated data.
The personalization of treatment via bio-markers and other risk categories has drawn increasing interest among clinical scientists. Personalized treatment strategies can be learned using data from clinical trials, but such trials are very costly to run. This paper explores the use of active learning techniques to desig…
QR-learner estimates individual treatment effects using external data.
problem Limited power to detect individual treatment effects in randomized trials.
method Model-agnostic learner that estimates conditional average treatment effects (CATE) using external data.
result QR-learner reduces mean squared error and can recover true CATE.
Bayesian BIC for multi-trial data improves VAR model order selection.
problem Optimal VAR model order selection for multi-trial event-based data.
method Derive and apply Bayesian Information Criterion (BIC) for multi-trial ensemble data.
result Multi-trial BIC successfully recovers real model order and estimates small model order.
Improved single-trial P300 classification accuracy using PCA and machine learning.
problem Low signal-to-noise ratio in P300 ERP trials.
method PCA preprocessing followed by LDA and neural networks.
result 13% accuracy gain with 3-4 principal components.
We develop a new Monte Carlo variance reduction method to estimate the expectation of two commonly encountered path-dependent functionals: first-passage times and occupation times of sets. The method is based on a recursive approximation of the first-passage time probability and expected occupation time of sets of a Le…
Optimum in Convex Hulls (OCH) generalizes clinical trial results to broader populations.
problem Clinical trials exclude confounding but limit recruitment; observational data are more inclusive but suffer from confounding.
method OCH uses convex hulls of conditional expectations or densities to approximate the true treatment effect from both observational and trial data.
result OCH estimates the treatment effect with state-of-the-art accuracy in terms of both expectations and densities.
New statistical tests detect interactions in clinical trials.
problem Detecting interactions between treatments and patient descriptors.
method Univariate and combined statistical tests based on random walk theory.
result Demonstrated utility and robustness of the proposed method.
EVILL uses randomised perturbations to improve exploration in bandit problems.
problem Improving exploration in structured stochastic bandit problems.
method Solves for the minimiser of a linearly perturbed regularised negative log-likelihood function.
result EVILL matches the performance of Thompson-sampling-style methods in theory and practice.
Paper develops a privacy-preserving nonparametric regression method.
problem Nonparametric regression with local differential privacy constraints.
method Privatised discretisation and Laplace noise applied to feature vectors and responses.
result Strongly universally consistent estimator for regression and classification.
Causal analysis reveals regional discrepancies in TOPCAT trial results.
problem Inconclusive results in TOPCAT trial for heart failure treatment.
method Causal discovery methods with domain knowledge integration.
result Significant causal effects shown for some subgroups globally.
The paper explores using historical data to improve clinical trial analysis by optimizing covariate weights.
problem Limited covariates in small clinical trials reduce the effectiveness of analysis.
method Leverage historical data to pre-specify covariate weights as a composite covariate.
result A composite covariate improves the cost/benefit ratio and reduces overfitting in small clinical trials.
SEEDA optimizes dose allocation in clinical trials to balance efficacy and safety.
problem Complex relationships between efficacy and toxicity in new drug trials.
method Adaptive clinical trial methodology that maximizes cumulative efficacy while ensuring safety constraints.
result SEEDA outperforms existing methods in finding optimal doses with higher success rates and fewer patients.