Author name cluster

Qiang Lou

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

ICLR Conference 2024 Conference Paper

Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing

Xinyu Hu
Pengfei Tang
Simiao Zuo
Zihan Wang
Bowen Song
Qiang Lou
Jian Jiao 0007
Denis Charles

Large language models (LLMs) have made impressive progress in natural language processing. These models rely on proper human instructions (or prompts) to generate suitable responses. However, the potential of LLMs are not fully harnessed by commonly-used prompting methods: many human-in-the-loop algorithms employ ad-hoc procedures for prompt selection; while auto prompt generation approaches are essentially searching all possible prompts randomly and inefficiently. We propose Evoke, an automatic prompt refinement framework. In Evoke, there are two instances of a same LLM: one as a reviewer (LLM-Reviewer), it scores the current prompt; the other as an author (LLM-Author), it edits the prompt by considering the edit history and the reviewer's feedback. Such an author-reviewer feedback loop ensures that the prompt is refined in each iteration. We further aggregate a data selection approach to Evoke, where only the hard samples are exposed to the LLM. The hard samples are more important because the LLM can develop deeper understanding of the tasks out of them, while the model may already know how to solve the easier cases. Experimental results show that Evoke significantly outperforms existing methods. For instance, in the challenging task of logical fallacy detection, Evoke scores above 80, while all other baseline methods struggle to reach 20.

Details

AAAI Conference 2012 Conference Paper

Margin-Based Feature Selection in Incomplete Data

Qiang Lou
Zoran Obradovic

This study considers the problem of feature selection in in complete data. The intuitive approach is to first impute the missing values, and then apply a standard feature selection method to select relevant features. In this study, we show how to perform feature selection directly, without imputing missing values. We define the objective function of the un certainty margin based feature selection method to maxim ize each instance’s uncertainty margin in its own relevant subspace. In optimization, we take into account the uncer tainty of each instance due to the missing values. The exper imental results on synthetic and 6 benchmark data sets with few missing values (less than 25%) provide evidence that our method can select the same accurate features as the al ternative methods which apply an imputation method first. However, when there is a large fraction of missing values (more than 25%) in data, our feature selection method out performs the alternatives, which impute missing values first.

PDF Details

IJCAI Conference 2011 Conference Paper

Modeling Multivariate Spatio-Temporal Remote Sensing Data with Large Gaps

Qiang Lou
Zoran Obradovic

Prediction models for multivariate spatio-temporal functions in geosciences are typically developed using supervised learning from attributes collected by remote sensing instruments collocated with the outcome variable provided at sparsely located sites. In such collocated data there are often large temporal gaps due to missing attribute values at sites where outcome labels are available. Our objective is to develop more accurate spatio-temporal predictors by using enlarged collocated data obtained by imputing missing attributes at time and locations where outcome labels are available. The proposed method for large gaps estimation in space and time (called LarGEST) exploits temporal correlation of attributes, correlations among multiple attributes collected at the same time and space, and spatial correlations among attributes from multiple sites. LarGEST outperformed alternative methods in imputing up to 80% of randomly missing observations at a synthetic spatio-temporal signal and at a model of fluoride content in a water distribution system. LarGEST was also applied for imputing 80% of nonrandom missing values in data from one of the most challenging Earth science problems related to aerosol properties. Using such enlarged data a predictor of aerosol optical depth is developed that was much more accurate than predictors based on alternative imputation methods when tested rigorously over entire continental US in year 2005.

PDF Details DOI

ECAI Conference 2010 Conference Paper

Feature Selection by Approximating the Markov Blanket in a Kernel-Induced Space

Qiang Lou
Zoran Obradovic

The proposed feature selection method aims to find a minimum subset of the most informative variables for classification/regression by efficiently approximating the Markov Blanket which is a set of variables that can shield a certain variable from the target. Instead of relying on the conditional independence test or network structure learning, the new method uses Hilbert-Schmidt Independence criterion as a measure of dependence among variables in a kernel-induced space. This allows effective approximation of the Markov Blanket that consists of multiple dependent features rather than being limited to a single feature. In addition, the new method can remove both irrelevant and redundant features at the same time. This method for discovering the Markov Blanket is applicable to both discrete and continuous variables, whereas previous methods cannot be used directly for continuous features and therefore are not applicable to regression problems. Experimental evaluations on synthetic and benchmark classification and regression datasets provide evidence that the new feature selection method can remove useless variables in low and in high dimensional problems more accurately than existing Markov Blanket based alternatives.

Details