arXiv research

New method robustly discovers causal relationships from imperfect data.

In this paper, we study Reinforcement Learning from Demonstrations (RLfD) that improves the exploration efficiency of Reinforcement Learning (RL) by providing expert demonstrations. Most of existing RLfD methods require demonstrations to be perfect and sufficient, which yet is unrealistic to meet in practice. To work o…

Cash collateral is perfect in that it provides simultaneous counterparty credit risk protection and derivatives funding. Securities are imperfect collateral, because of collateral segregation or differences in CSA haircuts and repo haircuts. Moreover, the collateral rate term structure is not observable in the repo mar…

Method maps imperfect simulations to observed stellar spectra using unsupervised domain adaptation.

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.

Investigates a Kyle model with imperfect information and risk aversion.

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…

Bayesian regression underestimates parameter uncertainties in noisy models.

Paper relaxes the Lipschitz constraint in WGANs to improve performance.

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.

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…

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

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

The paper offers error bounds for quantized dynamical models.

Algorithm learns NE in imperfect information games with imperfect feedback.

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.

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…

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.

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

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

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

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

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

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

A new framework improves VaR recalibration by balancing reliance on imperfect volatility proxies.

Combining LETKF and RC improves chaotic system prediction from noisy, sparse data.

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…

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

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

Estimates treatment effects in randomized experiments with non-compliance.

We consider effort allocation in crowdsourcing, where we wish to assign labeling tasks to imperfect homogeneous crowd workers to maximize overall accuracy in a continuous-time Bayesian setting, subject to budget and time constraints. The Bayes-optimal policy for this problem is the solution to a partially observable Ma…

RLCFR improves CFR's generalization in imperfect information games.

In a model with no given probability measure, we consider asset pricing in the presence of frictions and other imperfections and characterize the property of coherent pricing, a notion related to (but much weaker than) the no arbitrage property. We show that prices are coherent if and only if the set of pricing measure…

The paper analyzes knowledge distillation in wide neural networks, providing theoretical insights and practical implications.

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

The paper proposes a new framework to generate synthetic data with human-like imperfections to prevent model collapse.

This thesis tackles data imperfections in ML, proposing methods to prevent discrimination and spurious feature learning.