arXiv research

New algorithm uses imperfect advice to improve online bipartite matching performance.

High-frequency traders manage inventories to exploit price information, leading to mean-reverting inventories and excess trading.

Study cooperative bandit learning with imperfect communication, achieving near-optimal performance.

Heuristic pricing strategy compensates for imperfect market anticipation in dynamic pricing competition.

DREAM learns optimal strategies in imperfect games without needing a simulator.

Study completes financial markets in complex models without external probabilities.

JPS improves joint policies for multi-agent collaboration in imperfect information games.

Model shows disclosure reduces trading costs in oligopolistic markets.

The paper examines how builders in Ethereum auctions can defect and replicate winning MEV opportunities, affecting searchers' bidding strategies.

I study the limit of a large random economy, where a set of consumers invests in financial instruments engineered by banks, in order to optimize their future consumption. This exercise shows that, even in the ideal case of perfect competition, where full information is available to all market participants, the equilibr…

New method improves IL from imperfect demos using confidence scores.

When society maintains a competitive system to promote an abstract goal, competition by necessity relies on imperfect proxy measures. For instance profit is used to measure value to consumers, patient volumes to measure hospital performance, or the Journal Impact Factor to measure scientific value. Here we note that \t…

Mobile network that millions of people use every day is one of the most complex systems in the world. Optimization of mobile network to meet exploding customer demand and reduce capital/operation expenditures poses great challenges. Despite recent progress, application of deep reinforcement learning (DRL) to complex re…

The paper explores how regularization can lead to convergence in imperfect information games.

The paper tackles learning from imperfect human feedback, especially in dueling bandit problems.

Model learns tensor representations from imperfect multimodal data.

Investigates a Kyle model with imperfect information and risk aversion.

The paper proposes multicalibration to improve matching in graphs with imperfect predictors.

New method tackles simulator imperfection in data assimilation.

In this paper, we present a multi-period trading model in the style of Kyle (1985)'s inside trading model, by assuming that there are at least two insiders in the market with long-lived private information, under the requirement that each insider publicly discloses his stock trades after the fact. Based on this model, …

Investors' strategic trading affects asset prices, modeled as a game.

We study the effect of imperfect training data labels on the performance of classification methods. In a general setting, where the probability that an observation in the training dataset is mislabelled may depend on both the feature vector and the true label, we bound the excess risk of an arbitrary classifier trained…

Policy gradient method proves convergence in imperfect-information games.

New method robustly discovers causal relationships from imperfect data.

Volatility smiles emerge from imperfect hedging in financial markets.

Study on teaching with imperfect knowledge, showing its impact on optimal teaching sets.

New approach for algorithms that learn predictors to improve performance.

Triangle lasso improves clustering and optimization in noisy graphs.

Deep neural network learns to play Big 2, a 4-player imperfect information game, outperforming amateurs.

New numerical method for quantile hedging in imperfect markets.

Modeling the purposeful behavior of imperfect agents from a small number of observations is a challenging task. When restricted to the single-agent decision-theoretic setting, inverse optimal control techniques assume that observed behavior is an approximately optimal solution to an unknown decision problem. These tech…

Algorithm learns NE in imperfect information games with imperfect feedback.

Model financial markets using open quantum systems to understand market imperfections.

Paper studies fairness postprocessing with imperfect attribute information.

Improves off-policy evaluation with imperfect annotations.

Bayesian machine learning algorithm for causal effects with imperfect compliance.

The study examines how verifier imperfections impact test-time scaling techniques.

New method predicts y distributions from imperfect data.

Paper improves RL from imperfect demonstrations with soft expert guidance.

Study learns optimal strategies in imperfect information games with self-play.

Researchers develop methods for causal inference with imperfect instrumental variables.

Paper solves learning imperfect-information games with fewer episodes.

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…

New algorithms reduce regret in online learning with imperfect hints.

Bayesian framework mixes imperfect models for improved predictions.

Selective planning with imperfect models reduces harmful effects of model inadequacy.

New method uses imperfect LLM annotations for valid statistical inference in social science.

This paper explores how imperfect reward models can improve online RLHF.