Model predicts eligibility of cancer patients for clinical trials.
problem Clinical trials exclude many patients based on comorbidities and age.
method Deep neural networks trained on clinical trial protocols and free-texts.
result Model accurately predicts eligibility of clinical trial statements.
We study capital requirements for bounded financial positions defined as the minimum amount of capital to invest in a chosen eligible asset targeting a pre-specified acceptability test. We allow for general acceptance sets and general eligible assets, including defaultable bonds. Since the payoff of these assets is not…
Bayesian method corrects timing misalignment in recurrent event studies.
problem Estimating differences in event rates under two treatments with timing misalignment.
method g-computation procedure with joint semiparametric Bayesian model.
result Correctly estimates average causal effects under right-censoring.
New methods for fairness in regression using probabilistic classification.
problem Estimating fairness in continuous regression problems.
method Tractable approximations of fairness criteria using conditional probabilities from distinct classifiers.
result Model agnostic, tractable approximations of fairness criteria.
99% of papers use real-world data, but only 3% provide formal comparisons.
problem Lack of complete argumentative chains in demonstrating algorithmic effectiveness in machine learning papers.
method Systematic review of NeurIPS papers from 2017, assessing completeness of argumentative steps.
result Only 3% of papers provide formal comparisons, indicating incomplete argumentative chains.
Within the context of capital adequacy, we study comonotonicity of risk measures in terms of the primitives of the theory: acceptance sets and eligible, or reference, assets. We show that comonotonicity cannot be characterized by the properties of the acceptance set alone and heavily depends on the choice of the eligib…
Estimates treatment effects in bipartite systems with partial eligibility and interference.
problem Randomized experiments in bipartite systems with partial treatment eligibility and interference.
method Formalizes eligibility-constrained bipartite experiments, defines PTTE and STTE, identifies conditions, develops ensemble estimators, introduces projection.
result Proposed estimators recover PTTE and STTE with low bias and variance, corrects interference bias in field experiments.
New algorithm LSTD(λ)-RP uses random projections and eligibility traces for efficient reinforcement learning.
problem Policy evaluation in high-dimensional feature spaces with linear function approximation.
method Proposes LSTD(λ)-RP algorithm combining random projections and eligibility traces. result Demonstrates improved performance and better error bounds compared to prior methods.
Paper proposes fairgroup construction to improve fairness in Medicaid eligibility decisions.
problem Improper decisions in Medicaid eligibility allocation due to ML/DL model limitations.
method Fairgroup construction based on legal doctrine of disparate impact.
result Demonstrates improved fairness in regressive classifiers for Medicaid eligibility decisions.
Introduces expected eligibility traces for more efficient credit assignment in reinforcement learning.
problem Efficiently assigning credit to states and actions in reinforcement learning.
method Introduces expected eligibility traces, allowing updates to counterfactual sequences.
result Substantial improvements in temporal-difference learning can be achieved with expected traces.
Donors who defer their donations volunteer less in the future.
problem Volunteer labor can be less beneficial to charities than its costs.
method Regression discontinuity design with a procedure to handle manipulation.
result Donor manipulation invalidates standard regression discontinuity design, but a new method provides partial identification bounds.
We discuss risk measures representing the minimum amount of capital a financial institution needs to raise and invest in a pre-specified eligible asset to ensure it is adequately capitalized. Most of the literature has focused on cash-additive risk measures, for which the eligible asset is a risk-free bond, on the grou…
The risk of financial positions is measured by the minimum amount of capital to raise and invest in eligible portfolios of traded assets in order to meet a prescribed acceptability constraint. We investigate nondegeneracy, finiteness and continuity properties of these risk measures with respect to multiple eligible ass…
Unified algorithm Q(σ,λ) combines eligibility traces to improve memory and computation efficiency.
problem Memory and computation inefficiency in multi-step temporal-difference learning.
method Introduced Q(σ) algorithm, combined with eligibility traces to propose Q(σ,λ). result Proposed algorithm Q(σ,λ) converges to optimal value function exponentially. Meta-learning adjusts TD learning's eligibility trace parameter for more efficient reinforcement learning.
problem Efficiently tuning the eligibility trace parameter for temporal difference learning.
method Meta-learning method to adjust eligibility trace parameter state-dependently.
result Improves overall quality of update targets, minimizing target error.
In a capital adequacy framework, risk measures are used to determine the minimal amount of capital that a financial institution has to raise and invest in a portfolio of pre-specified eligible assets in order to pass a given capital adequacy test. From a capital efficiency perspective, it is important to identify the s…
This paper extends Q(σ) to Q(σ, λ) and Double Q(σ), improving reinforcement learning control.
problem Improving reinforcement learning control methods.
method Extends Q(σ) to Q(σ, λ) using eligibility traces and introduces Double Q(σ).
result The new Q(σ, λ) algorithm outperforms classical TD control methods.
Monetary risk measures are usually interpreted as the smallest amount of external capital that must be added to a financial position to make it acceptable. We propose a new concept: intrinsic risk measures and argue that this approach provides a direct path from unacceptable positions towards the acceptance set. Intrin…
Continuous selections for optimal portfolios under convex risk measures fail in finite-dimensional settings.
problem Finding optimal financial positions with continuous selections under convex risk measures.
method Analyzing set-valued maps in finite-dimensional settings with convex risk measures.
result Continuous selections do not always exist for optimal portfolios under convex risk measures.
Meta-learning technique speeds RL control learning and handles non-stationarity.
problem Hyperparameter tuning and non-stationarity in RL control.
method Meta-gradient descent for online step-size tuning with eligibility traces.
result Meta-step-size parameter easy to set, speeds learning, and handles non-stationarity.
New methods improve deep reinforcement learning by accelerating credit assignment.
problem Challenges in achieving fast and stable off-policy learning in deep reinforcement learning.
method Extends the generalized PBE objective to support multistep credit assignment and derives three gradient-based methods.
result Proposed methods outperform PPO and StreamQ in MuJoCo and MinAtar environments.
This paper investigates robust and efficient DR/RDR estimators for WATEs.
problem Lack of systematic investigation into robustness and efficiency conditions for WATE estimation.
method Proposes three RDR estimators using semiparametric efficient influence function and double/debiased machine learning.
result Demonstrates the practical relevance of the methods in medical and social sciences.
A fully decentralized multi-agent algorithm converges linearly with minimal memory.
problem Efficiently evaluating policies in multi-agent settings with limited exploration.
method Fully decentralized, combining off-policy learning, eligibility traces, and linear function approximation.
result Achieves linear convergence with minimal memory requirements.
Set-valued risk measures on Ldp with 0≤p≤∞ for conical market models are defined, primal and dual representation results are given. The collection of initial endowments which allow to super-hedge a multivariate claim are shown to form the values of a set-valued sublinear (coherent) risk measure. Sc…
New algorithms for collaborative reinforcement learning with limited communication.
problem Efficiently learning value functions in multi-agent systems with strict information constraints.
method Distributed gradient-based temporal difference algorithms with consensus schemes.
result Parameter estimates converge to ODEs with defined invariant sets under general assumptions.
Develops a risk score to assist ECMO planning for critically ill patients with viral or unspecified pneumonia.
problem Lack of a risk score to guide ECMO planning for critically ill patients.
method Leverages machine learning to develop the PEER score.
result PEER score predicts mortality and decompensation in patients eligible for ECMO.
New complete panel dataset for LMICs helps analyze innovation and development.
problem Lack of complete data for empirical analyses in LMICs.
method Predictive Mean Matching multiple imputation technique.
result Created a large dataset of 47 variables for 82 LMICs from 2005-2019.
We propose and analyze an alternate approach to off-policy multi-step temporal difference learning, in which off-policy returns are corrected with the current Q-function in terms of rewards, rather than with the target policy in terms of transition probabilities. We prove that such approximate corrections are sufficien…
Asymmetry PRISM outperforms CPU and GPU solvers for institutional rebalancing.
problem Institutional rebalancing with deadline constraints
method Asymmetry PRISM
result Asymmetry PRISM-CPU is 4.5x to 24.1x faster than the fastest completed reference row in the same lane.
TBQ(σ) improves trace utilization in off-policy reinforcement learning.
problem Efficiency of trace utilization under greedy target policies.
method Introduces TBQ(σ) that unifies tree-backup and Naive Q(λ) with a new parameter σ.
result TBQ(σ) improves efficiency in trace utilization, accelerating learning and performance.
Optimizes trading portfolios considering risk and profit.
problem Balancing risk and profit in trading portfolios.
method Risk-Aware Trading Swarm (RATS) algorithm.
result RATS improves portfolio performance and risk management.
Criteria ensure manifold triangulation without differentiable structure.
problem Ensuring a manifold can be triangulated without differentiable structure.
method Local coordinate chart criteria for homeomorphism verification.
result Criteria guarantee triangulation of manifolds without Delaunay property.
Study optimizes CT and microinsurance for efficient social protection in low-income countries.
problem Efficient targeting of cash transfers to reduce social protection costs in low-income countries.
method Modelled household capital dynamics using piecewise-deterministic Markov process, derived HJB equation for optimal injection, used dynamic programming.
result Optimal level of capital injection above poverty threshold for cost-effective social protection.
We give some general criteria of being a homeomorphism for continuous mappings of topological manifolds, as well as criteria of being a diffeomorphism for smooth mappings of smooth manifolds. As an illustration, we apply these criteria to the problems arising in two- and three-dimensional grid generation.
The study reveals flaws in pruning criteria and proposes a new assumption for better filter selection.
problem Flaws in existing pruning criteria for CNNs.
method Empirical experiments and Convolutional Weight Distribution Assumption.
result The Convolutional Weight Distribution Assumption improves filter selection in pruning.
New method finds optimal hyperparameters for multiple tasks and criteria.
problem Finding optimal hyperparameters for multiple tasks and criteria.
method Multi-Task Multi Criteria (MTMC) method that provides Pareto-optimal solutions.
result The method selects optimal hyperparameters based on given criteria significance coefficients.
Stochastic optimization problems often involve the expectation in its objective. When risk is incorporated in the problem description as well, then risk measures have to be involved in addition to quantify the acceptable risk, often in the objective. For this purpose it is important to have an adjusted, adapted and eff…
Improved recommendations using latent embeddings from user reviews.
problem Lack of consideration for latent embeddings in multi-criteria recommender systems.
method Utilized variational autoencoders to map user reviews into latent embeddings, which are then compressed into discrete vectors for multi-criteria recommendation.
result The proposed method significantly outperforms baselines across various datasets and evaluation measures.
New criteria for Heegaard splittings ensure strong irreducibility and finite Goeritz groups.
problem Determining strong irreducibility and finite Goeritz groups of Heegaard splittings.
method Two diagrammatic criteria for Heegaard splittings, accepting arbitrary disk systems.
result Criteria ensure strong irreducibility and finite Goeritz groups for Heegaard splittings.
Develops scenario theory for multi-criteria decision making.
problem Need for robustness assessment with multiple criteria and datasets.
method Collectively treats risks associated with individual criteria for multi-criteria decision problems.
result More accurate robustness certificates and sharper quantification of simultaneous criterion satisfaction.
We consider the problem of identifying patterns in a data set that exhibit anomalous behavior, often referred to as anomaly detection. In most anomaly detection algorithms, the dissimilarity between data samples is calculated by a single criterion, such as Euclidean distance. However, in many cases there may not exist …
The paper evaluates criteria for selecting cryptocurrencies based on historical data.
problem High risk of cryptocurrencies due to volatility.
method Characterized returns and risks using historical data in short time windows (7 and 15 days). Analyzed the importance of criteria using various methods.
result Importance of criteria for selecting cryptocurrencies is analyzed and evaluated.
Fairness criteria may harm over time, contrary to conventional wisdom.
problem The impact of fairness criteria on long-term population well-being.
method Study of fairness criteria in a one-step feedback model, analyzing long-term outcomes.
result Static fairness criteria do not necessarily promote improvement over time and may cause harm.
Study compares different complexity criteria for free boundary minimal surfaces.
problem Comparing different complexity criteria for free boundary minimal surfaces.
method Global theory of free boundary minimal surfaces.
result Provides a complete picture of how area, topology, and Morse index compare.
When sufficient labeled data are available, classical criteria based on Receiver Operating Characteristic (ROC) or Precision-Recall (PR) curves can be used to compare the performance of un-supervised anomaly detection algorithms. However , in many situations, few or no data are labeled. This calls for alternative crite…
The paper analyzes performance criteria for competing fund managers in Ito-diffusion markets.
problem Analyzing performance of competing fund managers in Ito-diffusion markets.
method Developed forward relative performance criteria and forward Nash equilibrium for passive and competitive cases.
result Extended performance criteria for investment problems in Ito-diffusion markets.
Paper defines numerical criteria to test handlebody link irreducibility.
problem Determining the irreducibility of handlebody links.
method A set of numerical criteria to test handlebody links for irreducibility.
result Effective method to recognize irreducibility of handlebody knots and most handlebody links.
We shall give useful criteria of lips, beaks and swallowtail singularities of smooth map from the plane into the plane. As an application of criteria, we will discuss the singularities of Cauchy problem of single conservation law.