Author name cluster

Lili Wu

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7 papers

2 author rows

YNIMG Journal 2025 Journal Article

Right inferior frontal cortex and preSMA in response inhibition: An investigation based on PTC model

Lili Wu
Mengjie Jiang
Min Zhao
Xin Hu
Jing Wang
Kaihua Zhang
Ke Jia
Fuxin Ren

Response inhibition is an essential component of cognitive function. A large body of literature has used neuroimaging data to uncover the neural architecture that regulates inhibitory control in general and movement cancelation. The presupplementary motor area (preSMA) and the right inferior frontal cortex (rIFC) are the key nodes in the inhibitory control network. However, how these two regions contribute to response inhibition remains controversial. Based on the Pause-then-Cancel Model (PTC), this study employed functional magnetic resonance imaging (fMRI) to investigate the functional specificity of two regions in the stopping process. The Go/No-Go task (GNGT) and the Stop Signal Task (SST) were administered to the same group of participants. We used the GNGT to dissociate the pause process and both the GNGT and the SST to investigate the inhibition mechanism. Imaging data revealed that response inhibition produced by both tasks activated the preSMA and rIFC. Furthermore, an across-participants analysis showed that increased activation in the rIFC was associated with a delay in the go response in the GNGT. In contrast, increased activation in the preSMA was associated with good inhibition efficiency via the striatum in both GNGT and SST. These behavioral and imaging findings support the PTC model of the role of rIFC and preSMA, that the former is involved in a pause process to delay motor responses, whereas the preSMA is involved in the stopping of motor responses.

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ICML Conference 2024 Conference Paper

PcLast: Discovering Plannable Continuous Latent States

Anurag Koul
Shivakanth Sujit
Shaoru Chen
Ben Evans
Lili Wu
Byron Xu
Rajan Chari
Riashat Islam

Goal-conditioned planning benefits from learned low-dimensional representations of rich observations. While compact latent representations typically learned from variational autoencoders or inverse dynamics enable goal-conditioned decision making, they ignore state reachability, hampering their performance. In this paper, we learn a representation that associates reachable states together for effective planning and goal-conditioned policy learning. We first learn a latent representation with multi-step inverse dynamics (to remove distracting information), and then transform this representation to associate reachable states together in $\ell_2$ space. Our proposals are rigorously tested in various simulation testbeds. Numerical results in reward-based settings show significant improvements in sampling efficiency. Further, in reward-free settings this approach yields layered state abstractions that enable computationally efficient hierarchical planning for reaching ad hoc goals with zero additional samples.

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ICML Conference 2024 Conference Paper

Rich-Observation Reinforcement Learning with Continuous Latent Dynamics

Yuda Song 0001
Lili Wu
Dylan J. Foster
Akshay Krishnamurthy

Sample-efficiency and reliability remain major bottlenecks toward wide adoption of reinforcement learning algorithms in continuous settings with high-dimensional perceptual inputs. Toward addressing these challenges, we introduce a new theoretical framework, RichCLD (“Rich-Observation RL with Continuous Latent Dynamics”), in which the agent performs control based on high-dimensional observations, but the environment is governed by low-dimensional latent states and Lipschitz continuous dynamics. Our main contribution is a new algorithm for this setting that is provably statistically and computationally efficient. The core of our algorithm is a new representation learning objective; we show that prior representation learning schemes tailored to discrete dynamics do not naturally extend to the continuous setting. Our new objective is amenable to practical implementation, and empirically, we find that it compares favorably to prior schemes in a standard evaluation protocol. We further provide several insights into the statistical complexity of the RichCLD framework, in particular proving that certain notions of Lipschitzness that admit sample-efficient learning in the absence of rich observations are insufficient in the rich-observation setting.

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UAI Conference 2023 Conference Paper

Enhancing Treatment Effect Estimation: A Model Robust Approach Integrating Randomized Experiments and External Controls using the Double Penalty Integration Estimator

Yuwen Cheng
Lili Wu
Shu Yang

Randomized experiments (REs) are the cornerstone for treatment effect evaluation. However, due to practical considerations, REs may encounter difficulty recruiting sufficient patients. External controls (ECs) can supplement REs to boost estimation efficiency. Yet, there may be incomparability between ECs and concurrent controls (CCs), resulting in misleading treatment effect evaluation. We introduce a novel bias function to measure the difference in the outcome mean functions between ECs and CCs. We show that the ANCOVA model augmented by the bias function for ECs renders a consistent estimator of the average treatment effect, regardless of whether or not the ANCOVA model is correct. To accommodate possibly different structures of the ANCOVA model and the bias function, we propose a double penalty integration estimator (DPIE) with different penalization terms for the two functions. With an appropriate choice of penalty parameters, our DPIE ensures consistency, oracle property, and asymptotic normality even in the presence of model misspecification. DPIE is at least as efficient as the estimator derived from REs alone, validated through theoretical and experimental results.

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YNIMG Journal 2023 Journal Article

Neurochemical and functional reorganization of the cognitive-ear link underlies cognitive impairment in presbycusis

Ning Li
Wen Ma
Fuxin Ren
Xiao Li
Fuyan Li
Wei Zong
Lili Wu
Zongrui Dai

Recent studies suggest that the interaction between presbycusis and cognitive impairment may be partially explained by the cognitive-ear link. However, the underlying neurophysiological mechanisms remain largely unknown. In this study, we combined magnetic resonance spectroscopy (MRS) and resting-state functional magnetic resonance imaging (fMRI) to investigate auditory gamma-aminobutyric acid (GABA) and glutamate (Glu) levels, intra- and inter-network functional connectivity, and their relationships with auditory and cognitive function in 51 presbycusis patients and 51 well-matched healthy controls. Our results confirmed reorganization of the cognitive-ear link in presbycusis, including decreased auditory GABA and Glu levels and aberrant functional connectivity involving auditory networks (AN) and cognitive-related networks, which were associated with reduced speech perception or cognitive impairment. Moreover, mediation analyses revealed that decreased auditory GABA levels and dysconnectivity between the AN and default mode network (DMN) mediated the association between hearing loss and impaired information processing speed in presbycusis. These findings highlight the importance of AN-DMN dysconnectivity in cognitive-ear link reorganization leading to cognitive impairment, and hearing loss may drive reorganization via decreased auditory GABA levels. Modulation of GABA neurotransmission may lead to new treatment strategies for cognitive impairment in presbycusis patients.

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CLeaR Conference 2022 Conference Paper

Integrative $R$-learner of heterogeneous treatment effects combining experimental and observational studies

Lili Wu
Shu Yang

The gold-standard approach to estimating heterogeneous treatment effects (HTEs) is randomized controlled trials (RCTs)/controlled experimental studies, where treatment randomization mitigates confounding biases. However, experimental data are usually small in sample size and limited in subjects’ diversity due to expensive costs. On the other hand, large observational studies (OSs) are becoming increasingly popular and accessible. However, OSs might be subject to hidden confounding whose existence is not testable. We develop an integrative $R$-learner for the HTE and confounding function by leveraging experimental data for identification and observational data for boosting efficiency. We form a regularized loss function for the HTE and confounding function that bears the Neyman orthogonality property, allowing flexible models for the nuisance function estimation. The key novelty of the proposed integrative $R$-learner is to impose different regularization terms for the HTE and confounding function so that the possible smoothness or sparsity of the confounding function can be leveraged to improve the HTE estimation. Our integrative $R$-learner has two benefits: first, it provides a general framework that can accommodate various HTE models for loss minimization; second, without any prior knowledge of hidden confounding in the OS, the proposed integrative $R$-learner is consistent and asymptotically at least as efficient as the estimator using only the RCT. The experiments based on extensive simulation and a real-data application adapted from an educational experiment show that the proposed integrative $R$-learner outperforms alternative approaches.

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RLDM Conference 2019 Conference Abstract

Parameterized Exploration

Jesse Clifton
Lili Wu
Eric Laber

We introduce Parameterized Exploration (PE), a simple family of methods for model-based tun- ing of the exploration schedule in sequential decision problems. Unlike common heuristics for exploration, our method accounts for the time horizon of the decision problem as well as the agent’s current state of knowledge of the dynamics of the decision problem. We show our method as applied to several common exploration techniques has superior performance relative to un-tuned counterparts in Gaussian multi-armed bandits, as well as a Markov decision process based on a mobile health (mHealth) study. We also examine the effects of model accuracy on the performance of PE.

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