arXiv research

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

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

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…

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.

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

Algorithm learns NE in imperfect information games with imperfect feedback.

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

New algorithm learns optimal decisions from imperfectly observed contexts.

Greedy policies perform poorly in imperfectly observed contextual bandits.

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…

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

The paper develops methods to estimate POMDPs from partial information.

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…

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

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

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

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…

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

In this paper we investigate the Follow the Regularized Leader dynamics in sequential imperfect information games (IIG). We generalize existing results of Poincaré recurrence from normal-form games to zero-sum two-player imperfect information games and other sequential game settings. We then investigate how adapting th…

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.

The paper analyzes insurance pricing and capital allocation in imperfect markets.

Paper studies distributed kernel regression with imperfect kernels, achieving optimal rates.

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

MEDIDA discovers model errors in chaotic systems using sparse regression and data assimilation.

Safe autonomous decisions made with machine learning predictions using Conformal Decision Theory.

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

Improves off-policy evaluation with imperfect annotations.

New taxonomy reveals different detection limits for various types of fraud.

Researchers develop methods for causal inference with imperfect instrumental variables.

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…

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…

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.

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…

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…

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…