A new perfect specialization model explains trade data better than imperfect models.
problem Improving the theoretical foundation of gravity equation in bilateral trade.
method Developed a perfect specialization model based on tradability.
result Tradability is the sole reason for deviations from basic models.
Paper studies fairness postprocessing with imperfect attribute information.
problem Ensuring fairness with imperfect protected attribute information.
method Equalized odds postprocessing method with imperfect attribute information.
result Conditions on perturbation ensure reduced bias in classifier.
New method improves IL from imperfect demos using confidence scores.
problem Learning optimal policies from imperfect demonstrations is challenging.
method Proposes two confidence-based IL methods: 2IWIL and IC-GAIL.
result Confidence scores from sub-optimal demos significantly improve IL performance.
The paper explores how regularization can lead to convergence in imperfect information games.
problem Finding equilibrium in imperfect information games with imperfect information.
method Investigates Follow the Regularized Leader dynamics and how adding a regularization term can lead to strong convergence guarantees.
result The approach leads to algorithms that converge exactly to the Nash equilibrium in imperfect information games.
The paper tackles learning from imperfect human feedback, especially in dueling bandit problems.
problem Learning from human feedback that can be irrational or imperfect.
method Developed a Robustified Stochastic Mirror Descent for Imperfect Dueling (RoSMID) algorithm.
result Achieved nearly optimal regret for dueling bandit problems under imperfect human feedback.
Model learns tensor representations from imperfect multimodal data.
problem Learning from imperfect multimodal data with noise or missing entries.
method Tensor rank minimization to regularize rank of tensor representations.
result Model effectively learns tensor representations from imperfect data.
Investigates a Kyle model with imperfect information and risk aversion.
problem Tackles a Kyle model with imperfect information and risk-averse informed traders.
method Solves an optimal transport problem and a filtering problem under specific measures.
result Constructs an equilibrium for the Gaussian Kyle model with imperfect information and risk aversion.
New method tackles simulator imperfection in data assimilation.
problem Handling simulator imperfection in data assimilation.
method Ensemble-based kernel learning approach.
result Functional approximation through machine learning can handle simulator imperfection.
Study on how imperfect labels affect classification methods.
problem Effect of imperfect training labels on classification performance.
method Bounding excess risk for various classifiers under noisy labels.
result Imperfect labels can improve performance of k-NN and SVM classifiers.
New framework identifies and reduces errors in machine learning under distribution shift.
problem Errors in machine learning models when distributions change.
method Developed a principled framework to characterize and eliminate epistemic errors in imperfect multitask learning.
result Provided a decompositional epistemic error bound for general settings of distribution shift.
Improved model-based estimation through tempered Bayes filter.
problem Improving predictive accuracy in partially-observable stochastic systems.
method Developed tempered Bayes filter combining likelihood and full posterior tempering.
result Tempered Bayes filter achieves improved predictive performance over the Bayes filter baseline.
Policy gradient method proves convergence in imperfect-information games.
problem Policy gradient methods in imperfect-information games (EFGs).
method Policy gradient approach with best-iterate convergence.
result Policy gradient leads to provable best-iterate convergence in self-play EFGs.
New method robustly discovers causal relationships from imperfect data.
problem Challenges in causal discovery from imperfect structural constraints.
method Prior alignment and conflict resolution through surrogate model and multi-task learning.
result Proposes a robust method for causal discovery under imperfect constraints.
Volatility smiles emerge from imperfect hedging in financial markets.
problem Imperfect hedging in financial markets leads to volatility smiles.
method Examined option prices as fair game agreements based on expected payoffs and risk.
result Resulting prices lead to the volatility smile.
Study on teaching with imperfect knowledge, showing its impact on optimal teaching sets.
problem Effect of imperfect teacher knowledge on effective teaching.
method Connections to machine teaching problem, optimal teaching sets.
result Teacher's success or failure depends on imperfect knowledge.
JPS improves joint policies for multi-agent collaboration in imperfect information games.
problem Learning good joint policies for multi-agent collaboration with imperfect information.
method Decomposes global changes to localized policy changes, iteratively improving joint policies without re-evaluating the entire game.
result JPS improves solutions provided by unilateral approaches and outperforms algorithms designed for collaborative policy learning.
Triangle lasso improves clustering and optimization in noisy graphs.
problem Noise and missing data in graph datasets lead to sub-optimal clustering results.
method Triangle lasso uses neighbors' similarity to find similar instances, avoiding direct feature comparison.
result Triangle lasso yields better results than state-of-the-art methods in practical scenarios.
Deep neural network learns to play Big 2, a 4-player imperfect information game, outperforming amateurs.
problem Training a neural network to play a complex, imperfect information game with multiple players.
method Self-play reinforcement learning using Proximal Policy Optimization.
result Deep neural network trained via self-play reaches performance level surpassing amateur players.
New numerical method for quantile hedging in imperfect markets.
problem Quantile hedging in non-linear markets with imperfections.
method Piecewise Constant Policy Timestepping (PCPT) coupled with monotone finite difference approximation.
result Convergence of the proposed numerical scheme proved using BSDE arguments.
Algorithm learns NE in imperfect information games with imperfect feedback.
problem Learning Nash equilibrium in imperfect information games with bandit feedback.
method IXOMD algorithm for model-free learning with 1 / T 1/\sqrt{T} 1/ T convergence rate. result IXOMD achieves 1 / T 1/\sqrt{T} 1/ T convergence rate to NE. The paper develops a new discount rate for derivatives using imperfect securities as collateral.
problem Inconsistent and non-observable collateral rates in derivatives markets.
method Synthesizes effects of imperfect collateral into a new discount rate, employs break-even repo formulae, and uses linear programming for optimization.
result Liquidity value adjustment (LVA) can be significant for long-term derivatives portfolios.
Model financial markets using open quantum systems to understand market imperfections.
problem Understanding market imperfections through imperfect trading mechanisms.
method Using open quantum systems to represent financial markets, characterizing orbits, and analyzing reduced density matrices.
result Non-classical modes of time evolution can incorporate factors like illiquid trades and imperfect trading mechanisms.
Improves off-policy evaluation with imperfect annotations.
problem Limited dataset coverage for evaluating new policies.
method Doubly robust estimators combining IS and DM, incorporating counterfactual annotations.
result Using annotations within the DM component yields the most desirable theoretical results.
Bayesian machine learning algorithm for causal effects with imperfect compliance.
problem Heterogeneous causal effects in imperfect compliance scenarios.
method Bayesian Causal Forest with Instrumental Variable (BCF-IV) methodology.
result BCF-IV outperforms other techniques in discovering and estimating heterogeneous causal effects.
The study examines how verifier imperfections impact test-time scaling techniques.
problem Understanding how verifier imperfections affect test-time scaling methods.
method Proves the instance-level accuracy of Best-of-N and Rejection Sampling methods using the geometry of the verifier's ROC curve.
result RS outperforms BoN for fixed compute, but both converge to the same accuracy in the infinite-compute limit.
New method predicts y distributions from imperfect data.
problem Predicting y from imperfect data (discrete, truncated, censored).
method Optimal transformations to estimate p(y|x).
result Estimates location, scale, and shape of y distribution.
Paper improves RL from imperfect demonstrations with soft expert guidance.
problem Improper and insufficient expert demonstrations in RLfD.
method Formalizes imperfect expert setting, tackles optimality and convergence issues with soft constraints, and uses local linear search on dual form.
result Method achieves consistent improvement over other RLfD methods.
Study learns optimal strategies in imperfect information games with self-play.
problem Learning optimal strategies in imperfect information games.
method Proposes Follow the Regularized Leader (FTRL) algorithms for imperfect information games.
result Proposes two FTRL algorithms: Balanced FTRL and Adaptive FTRL.
Researchers develop methods for causal inference with imperfect instrumental variables.
problem Quantifying cause and effect relationships with imperfect instrumental variables.
method Established a quantitative relationship between violations of instrumental inequalities and minimal measurement dependence, providing adapted inequalities valid in the presence of relaxed measurement dependence.
result Adapted inequalities for average causal effect in instrumental scenarios with binary outcomes, addressing violations of instrumental inequalities.
Paper solves learning imperfect-information games with fewer episodes.
problem Learning imperfect-information extensive-form games from bandit feedback.
method Balanced Online Mirror Descent and Balanced Counterfactual Regret Minimization algorithms.
result Achieves near-optimal sample complexity for finding approximate Nash equilibria.
We study pricing and superhedging strategies for game options in an imperfect market with default. We extend the results obtained by Kifer in \cite{Kifer} in the case of a perfect market model to the case of an imperfect market with default, when the imperfections are taken into account via the nonlinearity of the weal…
First sample-efficient algorithm for learning EFCE in bandit feedback settings.
problem Learning EFCE in bandit feedback settings for IIEFGs.
method Proposed K K K -EFCE and uncoupled no-regret algorithm with wide-range regret minimization. result First sample-efficient algorithm for learning EFCE from bandit feedback.
Stochastic block model shows universal applicability to network inference problems.
problem Finding partitions in complex networks that maximize objective functions.
method Showed equivalence of popular algorithms to maximum likelihood formulation of SBM.
result SBM is nearly universal for solving MPE problems.
New algorithms reduce regret in online learning with imperfect hints.
problem Designing algorithms to minimize regret in online learning with imperfect hints.
method Developed algorithms that are resilient to bad hints and interpolate between correlated and no-hints cases.
result Achieved nearly matching lower bounds for online learning with imperfect directional hints.
Selective planning with imperfect models reduces harmful effects of model inadequacy.
problem Harmful effects of using an imperfect model in reinforcement learning.
method Selective planning with heteroscedastic regression to estimate predictive uncertainty from model inadequacy.
result Effective selective planning requires considering both parameter uncertainty and model inadequacy.
Bayesian framework mixes imperfect models for improved predictions.
problem Improving predictions of complex computational models in unknown domains.
method Local Bayesian Dirichlet mixing of imperfect models using the Dirichlet distribution.
result Global and local mixtures of models achieve excellent performance in prediction accuracy and uncertainty quantification.
New method uses imperfect LLM annotations for valid statistical inference in social science.
problem Inaccurate large language model annotations in social science research.
method Design-based supervised learning (DSL) combining imperfect LLM surrogates with gold-standard labels.
result DSL provides valid statistical inference with comparable predictive accuracy to existing methods.
DREAM learns optimal strategies in imperfect games without needing a simulator.
problem Learning optimal strategies in imperfect-information games with multiple agents.
method DREAM is a deep reinforcement learning algorithm that converges to Nash Equilibria and coarse correlated equilibria.
result DREAM achieves state-of-the-art performance in benchmark games and is competitive with simulator-based algorithms.
This paper explores how imperfect reward models can improve online RLHF.
problem Sample efficiency in online RLHF from imperfect reward models.
method Identifies policy coverability and proposes TPO algorithm for transfer learning.
result TPO algorithm improves sample efficiency compared to standard online learning.
Study on liquidity and market efficiency in auction games with imperfect information.
problem Generating liquidity in illiquid auction markets with imperfect information.
method Characterized Nash equilibria in a two-player game with imperfect information, linking market spreads to signal strength.
result Without incentives, the market is inefficient and does not lead to trades. Quadratic fees indexed on half spread can generate liquidity.
In this review we establish various connections between complex networks and symmetry. While special types of symmetries (e.g., automorphisms) are studied in detail within discrete mathematics for particular classes of deterministic graphs, the analysis of more general symmetries in real complex networks is far less de…
New algorithm uses imperfect advice to improve online bipartite matching performance.
problem Online bipartite matching with imperfect advice.
method Designing an algorithm that uses external advice to improve performance between advice-free methods and optimal ratio.
result Algorithm achieves competitive ratio interpolating between advice-free methods and optimal ratio of 1.
A new framework improves VaR recalibration by balancing reliance on imperfect volatility proxies.
problem How to balance reliance on imperfect volatility proxies in one-sided VaR recalibration.
method Proxy-reliance control framework that interpolates between constant-shift and proxy-scaled corrections.
result Lower or intermediate proxy reliance can outperform fully proxy-scaled recalibration in stressed left-tail VaR control.
Study pricing and hedging for American options in a market with default risk.
problem Pricing and hedging American options in a market with default risk.
method Defines seller's and buyer's superhedging prices using optimal stopping problems and nonlinear expectations.
result Seller's and buyer's superhedging prices coincide and are characterized by nonlinear reflected BSDEs.
Combining LETKF and RC improves chaotic system prediction from noisy, sparse data.
problem Improving chaotic system prediction from imperfect observations and models.
method Combining LETKF and RC to predict spatio-temporal chaotic systems from noisy and sparsely distributed observations.
result The proposed method using LETKF and RC outperforms LETKF in predicting chaotic systems from noisy and sparse observations.
We present a novel methodology for predicting future outcomes that uses small numbers of individuals participating in an imperfect information market. By determining their risk attitudes and performing a nonlinear aggregation of their predictions, we are able to assess the probability of the future outcome of an uncert…
The paper proposes a fast method to predict tactical solutions to operational problems under imperfect information.
problem Predicting tactical solutions to operational planning problems under imperfect information.
method Formulated as a two-stage optimal prediction stochastic program, solved with a supervised machine learning algorithm using training data from deterministic problems.
result Deep learning algorithms produce highly accurate predictions in very short computing time (milliseconds or less).
A new feature selection method for semi-supervised learning with imperfect labels.
problem Feature selection for semi-supervised learning with imperfectly labeled data.
method Genetic algorithm for proposing feature subsets, probabilistic error model for mislabeling, multi-class C-bound selection criterion.
result Empirical results show the effectiveness of the proposed framework compared to state-of-the-art approaches.