arXiv research

Investigates a Kyle model with imperfect information and risk aversion.

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

Policy gradient method proves convergence in imperfect-information games.

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

Paper solves learning imperfect-information games with fewer episodes.

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

Algorithm learns NE in imperfect information games with imperfect feedback.

Study on liquidity and market efficiency in auction games with imperfect information.

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

Bayesian framework calibrates imperfect models using physics-informed priors and Hamiltonian Monte Carlo.

We derive asset pricing formula for markets with incomplete information and subjective views.

RLCFR improves CFR's generalization in imperfect information games.

Paper introduces a method to learn physics between digital twins using imperfect models.

Adaptive OMD reduces variance in learning optimal strategies for imperfect information games.

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…

When the available statistical information is imperfect, it is dangerous to follow standard optimisation procedures to construct an optimal portfolio, which usually leads to a strong concentration of the weights on very few assets. We propose a new way, based on generalised entropies, to ensure a minimal degree of dive…

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

We introduce a new virtual environment for simulating a card game known as "Big 2". This is a four-player game of imperfect information with a relatively complicated action space (being allowed to play 1,2,3,4 or 5 card combinations from an initial starting hand of 13 cards). As such it poses a challenge for many curre…

In this paper the theory of semi-bounded rationality is proposed as an extension of the theory of bounded rationality. In particular, it is proposed that a decision making process involves two components and these are the correlation machine, which estimates missing values, and the causal machine, which relates the cau…

In this paper, we investigate Dimensionality reduction (DR) maps in an information retrieval setting from a quantitative topology point of view. In particular, we show that no DR maps can achieve perfect precision and perfect recall simultaneously. Thus a continuous DR map must have imperfect precision. We further prov…

Most approaches aiming to ensure a model's fairness with respect to a protected attribute (such as gender or race) assume to know the true value of the attribute for every data point. In this paper, we ask to what extent fairness interventions can be effective even when only imperfect information about the protected at…

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

Imitation learning (IL) aims to learn an optimal policy from demonstrations. However, such demonstrations are often imperfect since collecting optimal ones is costly. To effectively learn from imperfect demonstrations, we propose a novel approach that utilizes confidence scores, which describe the quality of demonstrat…

Counterfactual regret minimization (CFR) is the most popular algorithm on solving two-player zero-sum extensive games with imperfect information and achieves state-of-the-art performance in practice. However, the performance of CFR is not fully understood, since empirical results on the regret are much better than the …

New framework uses tempered optimism to handle imperfect experts in online learning.

The paper develops methods to estimate POMDPs from partial information.

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…

New method robustly discovers causal relationships from imperfect data.

This paper offers a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a methodology to quickly predict tactical solutions to a given operational problem. In this context, the tactical solution is less detailed than the operational one but it has to be comput…

Simulator imperfection, often known as model error, is ubiquitous in practical data assimilation problems. Despite the enormous efforts dedicated to addressing this problem, properly handling simulator imperfection in data assimilation remains to be a challenging task. In this work, we propose an approach to dealing wi…

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

There has been an increased interest in multimodal language processing including multimodal dialog, question answering, sentiment analysis, and speech recognition. However, naturally occurring multimodal data is often imperfect as a result of imperfect modalities, missing entries or noise corruption. To address these c…

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…

Paper proposes a novel policy distillation method for better order execution in noisy markets.

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

Improves off-policy evaluation with imperfect annotations.

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.

Researchers develop methods for causal inference with imperfect instrumental variables.

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…

Recently, network lasso has drawn many attentions due to its remarkable performance on simultaneous clustering and optimization. However, it usually suffers from the imperfect data (noise, missing values etc), and yields sub-optimal solutions. The reason is that it finds the similar instances according to their feature…

New algorithms reduce regret in online learning with imperfect hints.

Bayesian framework mixes imperfect models for improved predictions.

We study the optimal trading policies for a wind energy producer who aims to sell the future production in the open forward, spot, intraday and adjustment markets, and who has access to imperfect dynamically updated forecasts of the future production. We construct a stochastic model for the forecast evolution and deter…

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

This paper studies the equilibrium pricing of asset shares in the presence of dynamic private information. The market consists of a risk-neutral informed agent who observes the firm value, noise traders, and competitive market makers who set share prices using the total order flow as a noisy signal of the insider's inf…

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