A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
RATE metrics evaluate treatment prioritization rules, subsuming existing methods.
A framework uses proxies to prioritize treatment without estimating causal effects.
Heteroskedasticity biases uplift model rankings, 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.
New methods prioritize acquiring confounding features for efficient 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.
Proposes a new method for dynamic treatment regimes that improves sample efficiency and stability.
Enhances credit card limit adjustments by considering treatment uncertainty and prediction criteria.
Bayesian Causal Forest models estimate treatment effects with noncompliance.
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.
Proposes a new method to optimize treatment allocation with budget constraints.
AI-driven sales prioritization boosts renewal bookings by 8.08%.
A new method prioritizes and recycles experiences for better reinforcement learning.
ReaPER improves learning efficiency by prioritizing reliable experiences.
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…
This work proposes a unified framework to leverage biological information in network propagation-based gene prioritization algorithms. Preliminary results on breast cancer data show significant improvements over state-of-the-art baselines, such as the prioritization of genes that are not identified as potential candida…
Study shows targeting students with intermediate predicted outcomes is most effective for financial aid renewal.
Two novel methods identify influential features in CMABs for better reward distribution.
Paper introduces DRM for selecting robust CATE estimators.
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…
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.
Introduces PIT-plot for prioritizing projects based on their impact.
SAEs struggle with feature consistency across runs, hindering MI reliability.
New methods reduce bias in machine learning predictions for causal inference without extra data.
We present a new model-based reinforcement learning algorithm, Cooperative Prioritized Sweeping, for efficient learning in multi-agent Markov decision processes. The algorithm allows for sample-efficient learning on large problems by exploiting a factorization to approximate the value function. Our approach only requir…
Develops adaptive framework for estimating survival effects with censoring.
Study repurposes open data to find potential COVID-19 drugs.
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.
The paper automates policy learning for nonlinear welfare criteria using machine learning and debiasing techniques.
The paper argues for prioritizing identifying structure over complex models for scientific discovery.
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.
GraphITE estimates individual effects of graph-structured treatments.
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…
M3E2 neural network estimates multiple treatment effects.
Curious Replay improves model-based reinforcement learning agents' adaptability.
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…
Optimizes COVID-19 testing policy using a Multi-Armed Bandit approach.