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Peng Ding

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3 papers

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ICML Conference 2024 Conference Paper

Entropy-Reinforced Planning with Large Language Models for Drug Discovery

Xuefeng Liu
Chih-chan Tien
Peng Ding
Songhao Jiang
Rick L. Stevens

The objective of drug discovery is to identify chemical compounds that possess specific pharmaceutical properties toward a binding target. Existing large language models (LLMS) can achieve high token matching scores in terms of likelihood for molecule generation. However, relying solely on LLM decoding often results in the generation of molecules that are either invalid due to a single misused token, or suboptimal due to unbalanced exploration and exploitation as a consequence of the LLM’s prior experience. Here we propose ERP, Entropy-Reinforced Planning for Transformer Decoding, which employs an entropy-reinforced planning algorithm to enhance the Transformer decoding process and strike a balance between exploitation and exploration. ERP aims to achieve improvements in multiple properties compared to direct sampling from the Transformer. We evaluated ERP on the SARS-CoV-2 virus (3CLPro) and human cancer cell target protein (RTCB) benchmarks and demonstrated that, in both benchmarks, ERP consistently outperforms the current state-of-the-art algorithm by 1-5 percent, and baselines by 5-10 percent, respectively. Moreover, such improvement is robust across Transformer models trained with different objectives. Finally, to further illustrate the capabilities of ERP, we tested our algorithm on three code generation benchmarks and outperformed the current state-of-the-art approach as well. Our code is publicly available at: https: //github. com/xuefeng-cs/ERP.

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TMLR Journal 2022 Journal Article

Multi-Source Causal Inference Using Control Variates under Outcome Selection Bias

Wenshuo Guo
Serena Lutong Wang
Peng Ding
Yixin Wang
Michael Jordan

While many areas of machine learning have benefited from the increasing availability of large and varied datasets, the benefit to causal inference has been limited given the strong assumptions needed to ensure the identifiability of causal effects -- which are often not satisfied in real-world datasets. For example, many large observational datasets (e.g., case-control studies in epidemiology, click-through data in recommender systems) suffer from selection bias on the outcome, which makes the average treatment effect (ATE) non-identifiable. We propose an algorithm to estimate causal effects from multiple data sources, where the ATE may be identifiable only in some datasets but not others. The idea is to construct control variates across the datasets in which the ATE may not be identifiable, which provably reduces the variance of the ATE estimate. We focus on a setting where the observational datasets suffer from outcome selection bias, assuming access to an auxiliary small dataset from which we can obtain a consistent estimate of the ATE. We propose a construction of control variate by taking the difference of the conditional odds ratio estimates from the two datasets. Across simulations and two case studies with real data, we show that the control variate-based ATE estimator has consistently and significantly reduced variance against different baselines.

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TIST Journal 2015 Journal Article

Semiparametric Inference of the Complier Average Causal Effect with Nonignorable Missing Outcomes

Hua Chen
Peng Ding
Zhi Geng
Xiao-Hua Zhou

Noncompliance and missing data often occur in randomized trials, which complicate the inference of causal effects. When both noncompliance and missing data are present, previous papers proposed moment and maximum likelihood estimators for binary and normally distributed continuous outcomes under the latent ignorable missing data mechanism. However, the latent ignorable missing data mechanism may be violated in practice, because the missing data mechanism may depend directly on the missing outcome itself. Under noncompliance and an outcome-dependent nonignorable missing data mechanism, previous studies showed the identifiability of complier average causal effect for discrete outcomes. In this article, we study the semiparametric identifiability and estimation of complier average causal effect in randomized clinical trials with both all-or-none noncompliance and outcome-dependent nonignorable missing continuous outcomes, and propose a two-step maximum likelihood estimator in order to eliminate the infinite dimensional nuisance parameter. Our method does not need to specify a parametric form for the missing data mechanism. We also evaluate the finite sample property of our method via extensive simulation studies and sensitivity analysis, with an application to a double-blinded psychiatric clinical trial.

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