arXiv research

New algorithms reduce regret in online learning with imperfect hints.

SAGE enhances reinforcement learning by injecting hints to prevent model stagnation.

Algorithm achieves logarithmic regret with sublinear hints.

New algorithms reduce forecasting errors by leveraging optimistic learning and hinting.

Task hinting improves transformer performance on longer tasks.

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…

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.

We propose a novel method for imputing missing data by adapting the well-known Generative Adversarial Nets (GAN) framework. Accordingly, we call our method Generative Adversarial Imputation Nets (GAIN). The generator (G) observes some components of a real data vector, imputes the missing components conditioned on what …

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…

Oracle-efficient algorithms for online learning with smoothed and hint-adversaries.

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

Due to the unparallelizable nature of the autoregressive factorization, AutoRegressive Translation (ART) models have to generate tokens sequentially during decoding and thus suffer from high inference latency. Non-AutoRegressive Translation (NART) models were proposed to reduce the inference time, but could only achiev…

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.

New method predicts y distributions from imperfect data.

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…

Bayesian framework mixes imperfect models for improved predictions.

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

Problems for the graduate students who want to improve problem-solving skills in geometry. Every problem has a short elegant solution -- this gives a hint which was not available when the problem was discovered.

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.

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…

Many recent invertible neural architectures are based on coupling block designs where variables are divided in two subsets which serve as inputs of an easily invertible (usually affine) triangular transformation. While such a transformation is invertible, its Jacobian is very sparse and thus may lack expressiveness. Th…

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

Knowledge distillation (KD) is a popular method for reducing the computational overhead of deep network inference, in which the output of a teacher model is used to train a smaller, faster student model. Hint training (i.e., FitNets) extends KD by regressing a student model's intermediate representation to a teacher mo…

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

Estimates treatment effects in randomized experiments with non-compliance.

RLCFR improves CFR's generalization in imperfect information games.

We review a resent {\em time-dependent} performance measure for economical time series -- the (optimal) investment horizon approach. For stock indices, the approach shows a pronounced gain-loss asymmetry that is {\em not} observed for the individual stocks that comprise the index. This difference may hint towards an sy…