Algorithm optimizes lockdown policies balancing health and economy.
problem Balancing health and economic impacts of lockdowns during pandemics.
method Reinforcement learning to automatically compute lockdown policies.
result Algorithm learns optimal lockdown policies from disease and population data.
Study uses artificial counterfactuals to show lockdowns reduced US case and death counts.
problem Impact of lockdowns on US case and death counts during the pandemic.
method Artificial counterfactual approach comparing states with and without lockdowns.
result Average two times more cases would have occurred without lockdowns.
Optimizes lockdown strategies to balance economic activities and virus spread.
problem Balancing economic activities and virus spread during lockdowns.
method Modeling the pandemic as SEIR, applying Granovetter threshold model for social distancing, and using NSGA-II optimization.
result Optimal lockdown policies for ten weeks to minimize infections and economic impact.
Adaptive models improve electricity demand forecasting during lockdown.
problem Poor load forecasting due to sudden consumption changes during lockdown.
method Adaptive generalized additive models with Kalman filters and expert aggregation.
result Significant reduction in prediction errors compared to traditional models.
Agent-based model compares different COVID-19 testing policies and their effectiveness.
problem Understanding how different testing policies reveal the true number of infected cases.
method Developed an agent-based simulation framework in Python to model various testing policies and interventions.
result Contact Tracing consistently captures more positive cases than Random Symptomatic Testing, and LBT performs similarly.
Study examines how COVID-19 affected India's exchange rates and stock market.
problem Impact of COVID-19 on India's financial markets during and after lockdown.
method Secondary data analysis using VAR models across different phases of lockdown and unlock.
result Increase in confirmed cases does not significantly affect exchange rate and stock market.
Covid lockdown increased interest in Italian stock market, leading to new investors.
problem Impact of Covid lockdown on Italian stock market investors.
method Analysis of trading activity and investor demographics before and during lockdown.
result New investors during lockdown were more skilled traders than pre-lockdown investors.
MELO predicts electricity loads by adapting to shifts without external indicators.
problem Adapting to non-stationary prediction challenges in online settings.
method MELO combines multiple forgetting factors and aggregation rules to adaptively predict.
result MELO reduces RMSE by 34.7% compared to base predictors and external covariates.
Study shows house buyers in Christchurch value earthquake risk differently based on time since 2011 quake.
problem Understanding how house buyers' perception of earthquake risk changes over time.
method Used a hedonic price model to analyze house prices in Christchurch over three periods.
result Buyers value earthquake risk differently based on the time since the 2011 Christchurch earthquake.
AI uses KGs to assess economic impact of selective lockdowns on Italian companies.
problem Impact of selective lockdowns on Italian companies' economic stability.
method Automated Reasoning and Knowledge Graphs to analyze company networks.
result Identifies strategic companies at risk of takeover during lockdowns.
Study shows reducing anthropogenic emissions significantly lowers PM2.5 levels but has little effect on O3 in Delhi.
problem Understanding and mitigating the effects of anthropogenic emissions on air pollution in Delhi.
method Predictive modeling, causal inference, Gaussian Process modeling, Granger causality analysis.
result Reductions in anthropogenic emissions lead to significant decreases in PM2.5 levels but have little effect on O3. New method combines neural nets with epidemic models for better prediction.
problem Improving epidemic prediction and forecasting using deep neural networks.
method Integrates machine learning with compartmental disease models for data-driven analysis.
result Data augmentation strategy improves neural network reliability for epidemic forecasting.
This study analyzes the effects of lifting lockdowns on Brazil's COVID-19 spread.
problem Understanding the impact of lifting lockdowns on Brazil's COVID-19 spread.
method Extended SEIR model with on/off strategy and developed a neural regressor.
result Lifting lockdowns could lead to a more severe second wave of COVID-19.
Paper examines global Covid-19 data complexity and finds low intrinsic dimensions.
problem Understanding the complexity of Covid-19 data across countries.
method Used a Bayesian mixture model (Hidalgo) to estimate intrinsic dimensionality.
result Covid-19 data projects onto two low-dimensional manifolds without significant loss of information.
GIFsentiment predicts stock market returns and investor sentiment from social media GIFs.
problem Understanding investor sentiment in the stock market.
method Constructing a sentiment index from social media GIFs and analyzing its correlation with market returns and volume.
result GIFsentiment positively predicts stock market returns and negatively predicts returns for up to four weeks.
The study analyzes sentiment of European tweets during the pandemic.
problem Understanding public sentiment during the COVID-19 pandemic.
method Cross-language sentiment analysis of multilingual tweets using neural networks and sentence embeddings.
result Sentiment analysis reveals that lockdown announcements correlate with a deterioration of mood, which recovers quickly.
Paper introduces a new method to model epidemic dynamics with varying parameters.
problem Capturing discontinuous variations in epidemic model parameters.
method Total variation regularization with Iterated Nelder--Mead optimization.
result The method accurately models epidemic dynamics with instant changes.
India's 2020-21 GDP growth forecast is projected at 1.9% due to COVID-19.
problem Impact of COVID-19 on India's 2020-21 GDP growth forecast.
method Quarterly GVA estimates using capacity utilisation assumptions and 2019-20 data.
result India's GDP growth in 2020-21 is expected to be 1.9%.
AI monitors social distancing and masks at manufacturing plants.
problem Ensuring safety of workers during post-COVID production.
method Computer vision and AI techniques for social distancing and mask detection.
result Real-time alerts prevent violations of social distancing and mask-wearing.
Study examines how pandemic anxiety affects financial market trust.
problem Anxiety during pandemic and trust in financial markets.
method Used Google search volume and stock market data to create mood indicators.
result Different clusters of countries and markets in terms of pessimism and optimism emerged.
This study uses complex networks to analyze influential spreaders and their effects on different market sectors.
problem Existing methods failed to distinguish between positive and negative influences of market sectors.
method LIEST (Local Influential Effects for Specific Target) method using complex network analysis.
result LIEST effectively distinguishes positive and negative influences of market sectors during different periods.
Method summarizes and predicts time series data for COVID-19 cases and deaths.
problem Summarizing and predicting time series data for multiple related time series.
method Hierarchical algorithm generating shapelets for centroids, nearest neighbor search for labeling, dynamic time warping for non-uniform lengths.
result Predictive model for individual time series based on aggregated statistics.
Adapts GRPO for off-policy RL, improving reward.
problem Improving training stability and efficiency in RL.
method Adapts GRPO to off-policy setting, uses clipped surrogate objectives.
result Off-policy GRPO outperforms on-policy GRPO in empirical tests.
Paper tackles efficient evaluation of natural stochastic policies in offline RL.
problem Efficiency issues in evaluating natural stochastic policies due to unknown evaluation policy.
method Derive efficiency bounds for tilting and modified treatment policies, propose nonparametric estimators.
result Proposed estimators attain efficiency bounds under lax conditions and enjoy partial double robustness.
New framework studies policy learning problems under data scarcity.
problem Learning improving policies when data is insufficient.
method Developed a mathematical framework for policy learning problems.
result Reduced policy learning problems to simpler ones in sample complexity.
New algorithms improve policy evaluation in reinforcement learning.
problem Off-policy stability and on-policy efficiency issues in policy evaluation.
method Introduced novel algorithms using oblique projection method.
result Demonstrated both off-policy stability and on-policy efficiency.
We study the problem of off-policy policy optimization in Markov decision processes, and develop a novel off-policy policy gradient method. Prior off-policy policy gradient approaches have generally ignored the mismatch between the distribution of states visited under the behavior policy used to collect data, and what …
Stabilizes policy optimization with off-policy data using divergence augmentation.
problem Premature convergence and instability in policy optimization with off-policy data.
method Incorporates Bregman divergence between behavior and current policies to ensure safe policy updates.
result Empirically shows better performance in data-scarce scenarios compared to other algorithms.
Study proposes managing COVID-19 without economy shutdowns.
problem Avoiding lockdowns while maintaining healthcare system capacity.
method Detailed heterogeneous epidemiological model, calibrated to data.
result Countries can avoid lockdowns if ICU beds per million > 100.
We consider the problem of off-policy evaluation in Markov decision processes. Off-policy evaluation is the task of evaluating the expected return of one policy with data generated by a different, behavior policy. Importance sampling is a technique for off-policy evaluation that re-weights off-policy returns to account…
New methods estimate policy value and gradients for deterministic policies from off-policy data.
problem Estimating policy value and gradients for deterministic policies from off-policy data.
method Proposed new doubly robust estimators based on kernelization approaches.
result Demonstrated a rate independent of horizon length for policy value and gradient estimation.
Study designs logging policies to minimize off-policy evaluation error.
problem Minimizing OPE error with logging policies for target policies.
method Characterizes reward-coverage tradeoff, proposes a unifying framework, derives optimal policies.
result Provides actionable guidance for firms choosing recommendation systems.
DSPI connects natural policy gradient to policy iteration, proving global convergence.
problem Optimizing policies in reinforcement learning.
method DSPI framework, combining smoothed policy iteration and natural policy gradient.
result DSPI achieves geometric convergence and optimal complexity for policy optimization.
POTEC tackles off-policy learning in large action spaces, improving effectiveness.
problem Existing OPL methods fail in large discrete action spaces due to bias or variance issues.
method Two-stage algorithm: cluster selection via policy-based approach, action selection via regression-based approach.
result POTEC provides substantial improvements in off-policy learning effectiveness, especially in large and structured action spaces.
Monotonic policy improvement and off-policy learning are two main desirable properties for reinforcement learning algorithms. In this paper, by lower bounding the performance difference of two policies, we show that the monotonic policy improvement is guaranteed from on- and off-policy mixture samples. An optimization …
Memory-efficient algorithm reduces variance in off-policy RL.
problem High variance in off-policy policy optimization.
method Memory-efficient, stochastically variance-reduced algorithm using off-policy samples.
result Empirically validated effectiveness of the proposed algorithm.
In this work, we consider the problem of estimating a behaviour policy for use in Off-Policy Policy Evaluation (OPE) when the true behaviour policy is unknown. Via a series of empirical studies, we demonstrate how accurate OPE is strongly dependent on the calibration of estimated behaviour policy models: how precisely …
Extends OPE to evaluate policies using diverse logging data.
problem Evaluate policies using log data from different policies.
method Develops an OPE method for various logging policies.
result Method's predictions converge to true performance as sample size increases.
PS framework selects best policy from library for CSO problems.
problem Policy selection in CSO with heterogeneous performance across covariate space.
method PS framework constructs library of candidate policies and learns a meta-policy to select the best one.
result PS consistently outperforms best single policy in heterogeneous CSO problems.
Entropy regularization improves policy optimization in reinforcement learning.
problem Improving policy optimization in reinforcement learning.
method Entropy regularization is introduced to soften the greedy policy towards a more diverse softmax policy, leading to a continuously parameterized algorithm that interpolates between policy gradient and Q-learning.
result An intermediate algorithm can improve performance in reinforcement learning.
New method optimizes treatment policies to avoid winner's curse.
problem Winner's curse in treatment policy optimization.
method Inference-aware policy optimization.
result Optimizes for both estimated performance and downstream evaluation.
PBVFs generalize across policies using learned value functions.
problem RL algorithms forget information about old policies when updating value functions to track the learned policy.
method Introduce Parameter-Based Value Functions (PBVFs) that include policy parameters in their inputs, enabling them to generalize across different policies.
result PBVFs enable zero-shot learning of new policies that outperform any policy seen during training.
Optimizes Thompson sampling policies using policy gradient methods.
problem Improving Thompson sampling in bandit problems.
method Applies policy gradient algorithms to optimize Thompson sampling policies.
result Direct policy search on Thompson sampling improves performance.
Study optimizes portfolio allocation policies using off-policy data and constraints.
problem Optimizing portfolio allocation policies under constraints using off-policy data.
method Solves a minimax objective with off-policy estimators and online learning to control constraint violations.
result Constructs near-optimal allocation policies for various regimes of operation and constraints.
This paper extends off-policy reinforcement learning to the multi-agent case in which a set of networked agents communicating with their neighbors according to a time-varying graph collaboratively evaluates and improves a target policy while following a distinct behavior policy. To this end, the paper develops a multi-…
The paper interprets policy-gradient algorithms using continuation theory.
problem Optimizing nonconvex functions in reinforcement learning.
method Formulates policy optimization as optimization by continuation, interprets policy-gradient algorithms as implicitly optimizing deterministic policies.
result Exploration in policy-gradient algorithms is seen as computing a continuation of the return of the policy.
We study the problem of off-policy critic evaluation in several variants of value-based off-policy actor-critic algorithms. Off-policy actor-critic algorithms require an off-policy critic evaluation step, to estimate the value of the new policy after every policy gradient update. Despite enormous success of off-policy …
This work analyzes the gap between off-policy and on-policy policy gradient methods and provides conditions to reduce this gap.
problem The gap between off-policy and on-policy policy gradient methods and conditions to reduce it.
method Theoretical analysis and empirical evidence of conditions to reduce the on-off gap.
result Conditions to reduce the on-off gap between off-policy and on-policy policy gradient methods.