RATE metrics evaluate treatment prioritization rules, subsuming existing methods.
problem Comparing and testing the quality of treatment prioritization rules.
method Rank-weighted average treatment effect (RATE) metrics.
result RATE metrics enable asymptotically exact inference in various study settings.
A framework uses proxies to prioritize treatment without estimating causal effects.
problem Prioritizing treatment when causal effects are hard to estimate.
method Decision-focused framework identifying conditions for proxy usefulness.
result Proxies can recover correct effect ordering under specific conditions.
Heteroskedasticity biases uplift model rankings, leading to inefficient treatment allocation.
problem Bias in uplift model rankings due to heteroskedasticity.
method Theoretical analysis and simulation on real-world data.
result Heteroskedasticity can cause individuals with high treatment effects to be ranked at the bottom, leading to inefficient treatment allocation.
Precision medicine is becoming a focus in medical research recently, as its implementation brings values to all stakeholders in the healthcare system. Various statistical methodologies have been developed tackling problems in different aspects of this field, e.g., assessing treatment heterogeneity, identifying patient …
Designs for allocating resources to prioritize needy applicants while estimating treatment effects.
problem Resource allocation under uncertainty with prioritized queues.
method Priority-queue randomization for treatment assignment and estimation of treatment effects.
result Identification of causal effects under different arrival and treatment assignment scenarios.
New methods prioritize acquiring confounding features for efficient treatment effect estimation.
problem Efficient treatment effect estimation from observational data with missing confounding information.
method Proposes two acquisition strategies: covariate balancing and reducing factual outcome error.
result Our proposed methods, especially reducing factual outcome error, improve sample efficiency for treatment effect estimation.
Traditional medicine typically applies one-size-fits-all treatment for the entire patient population whereas precision medicine develops tailored treatment schemes for different patient subgroups. The fact that some factors may be more significant for a specific patient subgroup motivates clinicians and medical researc…
Uplift modeling is an emerging machine learning approach for estimating the treatment effect at an individual or subgroup level. It can be used for optimizing the performance of interventions such as marketing campaigns and product designs. Uplift modeling can be used to estimate which users are likely to benefit from …
AACE learns treatment policies from EHRs using annotations to improve accuracy.
problem Learning treatment policies from multimodal EHRs with bias and inefficiency.
method Annotation-assisted coarsened effects (AACE) method.
result AACE outperforms existing methods in predicting treatment benefit from multimodal EHRs.
Proposes a new method for dynamic treatment regimes that improves sample efficiency and stability.
problem Challenges in estimating optimal treatments for individuals with dynamic decision-making stages.
method Focuses on prioritizing alignment between observed and optimal treatment trajectories across decision stages.
result Improves sample efficiency and stability of IPWE-based methods by relaxing the alignment requirement.
Enhances credit card limit adjustments by considering treatment uncertainty and prediction criteria.
problem Optimal treatment selection under multitreatment scenarios.
method Proposes a comprehensive methodology incorporating conditional value-at-risk and prediction criterion for continuous outcomes.
result Significantly improved policy performance in credit card limit adjustments.
Bayesian Causal Forest models estimate treatment effects with noncompliance.
problem Estimating treatment effects with noncompliance and varying compliance rates.
method Bayesian Causal Forest model for binary response variables.
result Flexibly estimate heterogeneous treatment effects among compliers.
To date, pavement management software products and studies on optimizing the prioritization of pavement maintenance and rehabilitation (M&R) have been mainly focused on three parameters; the pre-treatment pavement condition, the rehabilitation cost, and the available budget. Yet, the role of the candidate projects' spa…
A textbook on statistical machine learning for astronomy.
problem Uncertainty quantification in astronomical data analysis.
method Bayesian inference and classical statistical methods.
result Unified framework connecting modern and traditional methods.
Proposes a new method to optimize treatment allocation with budget constraints.
problem Optimizing treatment allocation with budget constraints in marketing.
method Learning to rank for profit maximization.
result Learning to rank can maximize the area under a policy's incremental profit curve.
AI-driven sales prioritization boosts renewal bookings by 8.08%.
problem Manual sales account prioritization is inefficient and under-invested.
method Developed an AI-based Account Prioritizer using machine learning and explanation algorithms.
result Generated a +8.08% increase in renewal bookings.
Unified framework improves gene prioritization in disease studies.
problem Identifying genes involved in diseases using heterogeneous biological data.
method Network propagation-based gene prioritization with integrated biological information.
result Significant improvements in prioritizing genes not identified by traditional methods.
A new method prioritizes and recycles experiences for better reinforcement learning.
problem Improving reinforcement learning efficiency by prioritizing and recycling experiences.
method Double-prioritized state-recycled (DPSR) experience replay.
result DPSR achieved state-of-the-art results in Atari games, outperforming original and prioritized methods.
ReaPER improves learning efficiency by prioritizing reliable experiences.
problem Inefficient sampling of past experiences in reinforcement learning.
method Introducing a novel measure of reliability to prioritize experiences in PER.
result ReaPER outperforms PER in various environments, including Atari-10.
Personalized interventions in social services, education, and healthcare leverage individual-level causal effect predictions in order to give the best treatment to each individual or to prioritize program interventions for the individuals most likely to benefit. While the sensitivity of these domains compels us to eval…
Experience replay is widely used in deep reinforcement learning algorithms and allows agents to remember and learn from experiences from the past. In an effort to learn more efficiently, researchers proposed prioritized experience replay (PER) which samples important transitions more frequently. In this paper, we propo…
Study shows targeting students with intermediate predicted outcomes is most effective for financial aid renewal.
problem Determining which students to target for financial aid renewal to maximize effectiveness.
method Used causal forest to estimate heterogeneous treatment effects and targeted students accordingly; compared targeting low vs high predicted probability outcomes.
result Targeting students with intermediate predicted outcomes yields the highest effectiveness in financial aid renewal.
Two novel methods identify influential features in CMABs for better reward distribution.
problem Suboptimal features degrade rewards, interpretability, and efficiency in CMABs.
method Heterogeneous Incremental Effect (HIE) and Heterogeneous Distribution Divergence (HDD) methods.
result Consistent ability to identify influential HTE features, enhancing CMAB performance.
Paper introduces DRM for selecting robust CATE estimators.
problem Selecting CATE estimators without counterfactual outcomes.
method Distributionally Robust Metric (DRM) for CATE estimator selection.
result DRM selects robust CATE estimators robust to distribution shift.
Most exact methods for k-nearest neighbour search suffer from the curse of dimensionality; that is, their query times exhibit exponential dependence on either the ambient or the intrinsic dimensionality. Dynamic Continuous Indexing (DCI) offers a promising way of circumventing the curse and successfully reduces the dep…
Elucidating the genetic basis of human diseases is a central goal of genetics and molecular biology. While traditional linkage analysis and modern high-throughput techniques often provide long lists of tens or hundreds of disease gene candidates, the identification of disease genes among the candidates remains time-con…
In Hindsight Experience Replay (HER), a reinforcement learning agent is trained by treating whatever it has achieved as virtual goals. However, in previous work, the experience was replayed at random, without considering which episode might be the most valuable for learning. In this paper, we develop an energy-based fr…
New algorithm learns efficiently in multi-agent settings.
problem Efficient learning in multi-agent Markov decision processes.
method Cooperative Prioritized Sweeping: model-based reinforcement learning with sample efficiency.
result Outperforms state-of-the-art on SysAdmin and randomized environments.
A new approach to cost-sensitive multiclass classification prioritizes certain classes over others.
problem Cost-sensitive multiclass classification where some classes are more important than others.
method Apportioned margin framework that shifts the decision boundary to prioritize certain classes.
result The method improves the error rate for important classes while reducing overall error.
In recent years, automated data-driven decision-making systems have enjoyed a tremendous success in a variety of fields (e.g., to make product recommendations, or to guide the production of entertainment). More recently, these algorithms are increasingly being used to assist socially sensitive decision-making (e.g., to…
Smart bin monitors predict medication adherence with high accuracy.
problem Predicting chronic medication adherence to improve healthcare efficiency.
method Machine learning models trained on IoT device data.
result High predictive performance (ROC AUC 0.86).
Introduces PIT-plot for prioritizing projects based on their impact.
problem Optimizing R&D investments in project portfolios.
method Develops a new tool (PIT-plot) focusing on project impact rather than project properties.
result Identifies projects with the largest impact for risk mitigation or value-adding.
SAEs struggle with feature consistency across runs, hindering MI reliability.
problem Inconsistency of learned SAE features across different training runs.
method Propose using the Pairwise Dictionary Mean Correlation Coefficient (PW-MCC) to measure feature consistency.
result High levels of feature consistency (0.80 for TopK SAEs on LLM activations) are achievable with appropriate architectural choices.
New methods reduce bias in machine learning predictions for causal inference without extra data.
problem Machine learning predictions from satellite data shrink toward the mean, leading to biased causal estimates.
method Two post-hoc correction methods: Linear Calibration Correction (LCC) and Tweedie's approach, reduce shrinkage-induced bias.
result Tweedie's method yields nearly unbiased treatment-effect estimates, enabling multiple trials with a single map.
Develops adaptive framework for estimating survival effects with censoring.
problem Estimating causal effects in survival data with censoring.
method Derives semiparametric efficiency bound, proposes efficiency-optimal allocation policy, and develops Adaptive Survival Estimator (ASE).
result ASE achieves asymptotic normality via martingale central limit theorem and demonstrates efficiency gains over uniform randomization.
Study repurposes open data to find potential COVID-19 drugs.
problem Developing effective treatments for COVID-19.
method Deep learning network-based approach using large scientific corpus.
result Identified 41 repurposable drugs for COVID-19.
Deep neural networks (DNNs) are increasingly being adopted for sensing and control functions in a variety of safety and mission-critical systems such as self-driving cars, autonomous air vehicles, medical diagnostics, and industrial robotics. Failures of such systems can lead to loss of life or property, which necessit…
The central aim in this paper is to address variable selection questions in nonlinear and nonparametric regression. Motivated by statistical genetics, where nonlinear interactions are of particular interest, we introduce a novel and interpretable way to summarize the relative importance of predictor variables. Methodol…
New loss function equivalence reveals PER's uniform sampling can be improved.
problem Improving Prioritized Experience Replay (PER) for better learning efficiency.
method Transforming non-uniformly sampled data loss functions into uniformly sampled ones.
result Some environments can replace PER with a new loss function without performance loss.
The paper automates policy learning for nonlinear welfare criteria using machine learning and debiasing techniques.
problem Learning optimal policies from observational data with nonlinear welfare criteria.
method Modeling a nonlinear welfare criterion with a utility function, estimating propensity scores with machine learning, and using sieve approximations and cross-validation for model selection.
result The proposed policy learning method satisfies oracle inequalities, providing theoretical guarantees on performance.
The paper argues for prioritizing identifying structure over complex models for scientific discovery.
problem Underdetermination of mechanisms in high-dimensional data, leading to unreliable explanations.
method Proposes concrete standards for 'mechanistic ML' to avoid collapsing explanations.
result Large language models (LLMs) can collapse large equivalence classes of explanations, making it hard to distinguish between mechanisms.
Exploration efficiency is a challenging problem in multi-agent reinforcement learning (MARL), as the policy learned by confederate MARL depends on the collaborative approach among multiple agents. Another important problem is the less informative reward restricts the learning speed of MARL compared with the informative…
Adambs adapts Adam to prioritize important training examples.
problem Training deep learning models efficiently with varying example importance.
method Adapt Adam using a bandit sampling approach to prioritize examples based on their importance.
result Adambs improves convergence rate compared to Adam in some cases.
A variety of methods have been proposed for interpreting nodes in deep neural networks, which typically involve scoring nodes at lower layers with respect to their effects on the output of higher-layer nodes (where lower and higher layers are closer to the input and output layers, respectively). However, we may be inte…
GraphITE estimates individual effects of graph-structured treatments.
problem Estimating individual effects of complex treatment structures.
method Graph neural networks and Hilbert-Schmidt Independence Criterion regularization.
result GraphITE outperforms baselines in estimating treatment effects for large numbers of treatments.
M3E2 neural network estimates multiple treatment effects.
problem Estimating effects of multiple treatments simultaneously.
method Multi-task learning neural network model for multiple treatments, continuous and binary.
result M3E2 outperforms baselines in synthetic datasets.
Curious Replay improves model-based reinforcement learning agents' adaptability.
problem Existing model-based reinforcement learning agents struggle to adapt quickly to changing environments.
method Curious Replay uses a curiosity-based priority signal for prioritized experience replay tailored to model-based agents.
result Agents using Curious Replay achieve improved performance in exploration and on benchmarks.
In Reinforcement Learning (RL), an agent explores the environment and collects trajectories into the memory buffer for later learning. However, the collected trajectories can easily be imbalanced with respect to the achieved goal states. The problem of learning from imbalanced data is a well-known problem in supervised…