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Yan Liu 0002

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19 papers
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19

ICLR Conference 2025 Conference Paper

A Large-scale Dataset and Benchmark for Commuting Origin-Destination Flow Generation

  • Can Rong
  • Jingtao Ding
  • Yan Liu 0002
  • Yong Li 0008

Commuting Origin-Destination~(OD) flows are critical inputs for urban planning and transportation, providing crucial information about the population residing in one region and working in another within an interested area. Due to the high cost of data collection, researchers have developed physical and computational models to generate commuting OD flows using readily available urban attributes, such as sociodemographics and points of interest, for cities lacking historical OD flows \textemdash commuting OD flow generation. Existing works developed models based on different techniques and achieved improvement on different datasets with different evaluation metrics, which hinderes establishing a unified standard for comparing model performance. To bridge this gap, we introduce a large-scale dataset containing commuting OD flows for 3,333 areas including a wide range of urban environments around the United States. Based on that, we benchmark widely used models for commuting OD flow generation. We surprisingly find that the network-based generative models achieve the optimal performance in terms of both precision and generalization ability, which may inspire new research directions of graph generative modeling in this field. The dataset and benchmark are available at https://anonymous.4open.science/r/CommutingODGen-Dataset-0D4C/.

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ICML Conference 2024 Conference Paper

An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series

  • Qiang Huang
  • Chuizheng Meng
  • Defu Cao
  • Biwei Huang
  • Yi Chang 0001
  • Yan Liu 0002

Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings.

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ICML Conference 2024 Conference Paper

Position: TrustLLM: Trustworthiness in Large Language Models

  • Yue Huang 0001
  • Lichao Sun 0001
  • Haoran Wang 0005
  • Siyuan Wu 0001
  • Qihui Zhang
  • Yuan Li
  • Chujie Gao
  • Yixin Huang

Large language models (LLMs) have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs present many challenges, particularly in the realm of trustworthiness. This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. Our findings firstly show that in general trustworthiness and capability (i. e. , functional effectiveness) are positively related. Secondly, our observations reveal that proprietary LLMs generally outperform most open-source counterparts in terms of trustworthiness, raising concerns about the potential risks of widely accessible open-source LLMs. However, a few open-source LLMs come very close to proprietary ones, suggesting that open-source models can achieve high levels of trustworthiness without additional mechanisms like moderator, offering valuable insights for developers in this field. Thirdly, it is important to note that some LLMs may be overly calibrated towards exhibiting trustworthiness, to the extent that they compromise their utility by mistakenly treating benign prompts as harmful and consequently not responding. Besides these observations, we’ve uncovered key insights into the multifaceted trustworthiness in LLMs. We emphasize the importance of ensuring transparency not only in the models themselves but also in the technologies that underpin trustworthiness. We advocate that the establishment of an AI alliance between industry, academia, the open-source community to foster collaboration is imperative to advance the trustworthiness of LLMs.

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ICLR Conference 2024 Conference Paper

TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting

  • Defu Cao
  • Furong Jia 0002
  • Sercan Ömer Arik
  • Tomas Pfister
  • Yixiang Zheng
  • Wen Ye 0001
  • Yan Liu 0002

The past decade has witnessed significant advances in time series modeling with deep learning. While achieving state-of-the-art results, the best-performing architectures vary highly across applications and domains. Meanwhile, for natural language processing, the Generative Pre-trained Transformer (GPT) has demonstrated impressive performance via training one general-purpose model across various textual datasets. It is intriguing to explore whether GPT-type architectures can be effective for time series, capturing the intrinsic dynamic attributes and leading to significant accuracy improvements. In this paper, we propose a novel framework, TEMPO, that can effectively learn time series representations. We focus on utilizing two essential inductive biases of the time series task for pre-trained models: (i) decomposition of the complex interaction between trend, seasonal and residual components; and (ii) introducing the design of prompts to facilitate distribution adaptation in different types of time series. TEMPO expands the capability for dynamically modeling real-world temporal phenomena from data within diverse domains. Our experiments demonstrate the superior performance of TEMPO over state-of-the-art methods on zero shot setting for a number of time series benchmark datasets. This performance gain is observed not only in scenarios involving previously unseen datasets but also in scenarios with multi-modal inputs. This compelling finding highlights TEMPO's potential to constitute a foundational model-building framework.

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ICLR Conference 2024 Conference Paper

The Devil is in the Neurons: Interpreting and Mitigating Social Biases in Language Models

  • Yan Liu 0002
  • Yu Liu
  • Xiaokang Chen
  • Pin-Yu Chen
  • Daoguang Zan
  • Min-Yen Kan
  • Tsung-Yi Ho

Pre-trained Language models (PLMs) have been acknowledged to contain harmful information, such as social biases, which may cause negative social impacts or even bring catastrophic results in application. Previous works on this problem mainly focused on using black-box methods such as probing to detect and quantify social biases in PLMs by observing model outputs. As a result, previous debiasing methods mainly finetune or even pre-train PLMs on newly constructed anti-stereotypical datasets, which are high-cost. In this work, we try to unveil the mystery of social bias inside language models by introducing the concept of {\sc Social Bias Neurons}. Specifically, we propose {\sc Integrated Gap Gradients (IG$^2$)} to accurately pinpoint units (i.e., neurons) in a language model that can be attributed to undesirable behavior, such as social bias. By formalizing undesirable behavior as a distributional property of language, we employ sentiment-bearing prompts to elicit classes of sensitive words (demographics) correlated with such sentiments. Our IG$^2$ thus attributes the uneven distribution for different demographics to specific Social Bias Neurons, which track the trail of unwanted behavior inside PLM units to achieve interoperability. Moreover, derived from our interpretable technique, {\sc Bias Neuron Suppression (BNS)} is further proposed to mitigate social biases. By studying BERT, RoBERTa, and their attributable differences from debiased FairBERTa, IG$^2$ allows us to locate and suppress identified neurons, and further mitigate undesired behaviors. As measured by prior metrics from StereoSet, our model achieves a higher degree of fairness while maintaining language modeling ability with low cost\footnote{This work contains examples that potentially implicate stereotypes, associations, and other harms that could be offensive to individuals in certain social groups.}.

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UAI Conference 2021 Conference Paper

Gradient-based optimization for multi-resource spatial coverage problems

  • Nitin Kamra
  • Yan Liu 0002

Resource allocation for coverage of geographical spaces is a challenging problem in robotics, sensor networks and security domains. Conventional solution approaches either: (a) rely on exploiting spatio-temporal structure of specific coverage problems, or (b) use genetic algorithms when targeting general coverage problems where no special exploitable structure exists. In this work, we propose the coverage gradient theorem, which provides a gradient estimator for a broad class of spatial coverage objectives using a combination of Newton-Leibniz theorem and implicit boundary differentiation. We also propose a tractable framework to approximate the coverage objectives and their gradients using spatial discretization and empirically demonstrate the efficacy of our framework on multi-resource spatial coverage problems.

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ICLR Conference 2020 Conference Paper

Feature Interaction Interpretability: A Case for Explaining Ad-Recommendation Systems via Neural Interaction Detection

  • Michael Tsang
  • Dehua Cheng
  • Hanpeng Liu
  • Xue Feng
  • Eric Zhou
  • Yan Liu 0002

Recommendation is a prevalent application of machine learning that affects many users; therefore, it is important for recommender models to be accurate and interpretable. In this work, we propose a method to both interpret and augment the predictions of black-box recommender systems. In particular, we propose to interpret feature interactions from a source recommender model and explicitly encode these interactions in a target recommender model, where both source and target models are black-boxes. By not assuming the structure of the recommender system, our approach can be used in general settings. In our experiments, we focus on a prominent use of machine learning recommendation: ad-click prediction. We found that our interaction interpretations are both informative and predictive, e.g., significantly outperforming existing recommender models. What's more, the same approach to interpret interactions can provide new insights into domains even beyond recommendation, such as text and image classification.

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ICLR Conference 2020 Conference Paper

Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics

  • Sungyong Seo
  • Chuizheng Meng
  • Yan Liu 0002

Sparsely available data points cause numerical error on finite differences which hinders us from modeling the dynamics of physical systems. The discretization error becomes even larger when the sparse data are irregularly distributed or defined on an unstructured grid, making it hard to build deep learning models to handle physics-governing observations on the unstructured grid. In this paper, we propose a novel architecture, Physics-aware Difference Graph Networks (PA-DGN), which exploits neighboring information to learn finite differences inspired by physics equations. PA-DGN leverages data-driven end-to-end learning to discover underlying dynamical relations between the spatial and temporal differences in given sequential observations. We demonstrate the superiority of PA-DGN in the approximation of directional derivatives and the prediction of graph signals on the synthetic data and the real-world climate observations from weather stations.

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ICML Conference 2018 Conference Paper

Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series

  • Zhengping Che
  • Sanjay Purushotham
  • Max Guangyu Li
  • Bo Jiang 0001
  • Yan Liu 0002

Multi-Rate Multivariate Time Series (MR-MTS) are the multivariate time series observations which come with various sampling rates and encode multiple temporal dependencies. State-space models such as Kalman filters and deep learning models such as deep Markov models are mainly designed for time series data with the same sampling rate and cannot capture all the dependencies present in the MR-MTS data. To address this challenge, we propose the Multi-Rate Hierarchical Deep Markov Model (MR-HDMM), a novel deep generative model which uses the latent hierarchical structure with a learnable switch mechanism to capture the temporal dependencies of MR-MTS. Experimental results on two real-world datasets demonstrate that our MR-HDMM model outperforms the existing state-of-the-art deep learning and state-space models on forecasting and interpolation tasks. In addition, the latent hierarchies in our model provide a way to show and interpret the multiple temporal dependencies.

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ICML Conference 2016 Conference Paper

Learning from Multiway Data: Simple and Efficient Tensor Regression

  • Rose Yu
  • Yan Liu 0002

Tensor regression has shown to be advantageous in learning tasks with multi-directional relatedness. Given massive multiway data, traditional methods are often too slow to operate on or suffer from memory bottleneck. In this paper, we introduce subsampled tensor projected gradient to solve the problem. Our algorithm is impressively simple and efficient. It is built upon projected gradient method with fast tensor power iterations, leveraging randomized sketching for further acceleration. Theoretical analysis shows that our algorithm converges to the correct solution in fixed number of iterations. The memory requirement grows linearly with the size of the problem. We demonstrate superior empirical performance on both multi-linear multi-task learning and spatio-temporal applications.

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ICML Conference 2015 Conference Paper

Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams

  • Rose Yu
  • Dehua Cheng
  • Yan Liu 0002

Low-rank tensor learning has many applications in machine learning. A series of batch learning algorithms have achieved great successes. However, in many emerging applications, such as climate data analysis, we are confronted with large-scale tensor streams, which poses significant challenges to existing solution in terms of computational costs and limited response time. In this paper, we propose an online accelerated low-rank tensor learning algorithm (ALTO) to solve the problem. At each iteration, we project the current tensor to the subspace of low-rank tensors in order to perform efficient tensor decomposition, then recover the decomposition of the new tensor. By randomly glancing at additional subspaces, we successfully avoid local optima at negligible extra computational cost. We evaluate our method on two tasks in streaming multivariate spatio-temporal analysis: online forecasting and multi-model ensemble, which shows that our method achieves comparable predictive accuracy with significant boost in run time.

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ICML Conference 2015 Conference Paper

Functional Subspace Clustering with Application to Time Series

  • Mohammad Taha Bahadori
  • David C. Kale
  • Yingying Fan
  • Yan Liu 0002

Functional data, where samples are random functions, are increasingly common and important in a variety of applications, such as health care and traffic analysis. They are naturally high dimensional and lie along complex manifolds. These properties warrant use of the subspace assumption, but most state-of-the-art subspace learning algorithms are limited to linear or other simple settings. To address these challenges, we propose a new framework called Functional Subspace Clustering (FSC). FSC assumes that functional samples lie in deformed linear subspaces and formulates the subspace learning problem as a sparse regression over operators. The resulting problem can be efficiently solved via greedy variable selection, given access to a fast deformation oracle. We provide theoretical guarantees for FSC and show how it can be applied to time series with warped alignments. Experimental results on both synthetic data and real clinical time series show that FSC outperforms both standard time series clustering and state-of-the-art subspace clustering.

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ICML Conference 2015 Conference Paper

HawkesTopic: A Joint Model for Network Inference and Topic Modeling from Text-Based Cascades

  • Xinran He
  • Theodoros Rekatsinas
  • James R. Foulds
  • Lise Getoor
  • Yan Liu 0002

Understanding the diffusion of information in social network and social media requires modeling the text diffusion process. In this work, we develop the HawkesTopic model (HTM) for analyzing text-based cascades, such as "retweeting a post" or "publishing a follow-up blog post". HTM combines Hawkes processes and topic modeling to simultaneously reason about the information diffusion pathways and the topics characterizing the observed textual information. We show how to jointly infer them with a mean-field variational inference algorithm and validate our approach on both synthetic and real-world data sets, including a news media dataset for modeling information diffusion, and an ArXiv publication dataset for modeling scientific influence. The results show that HTM is significantly more accurate than several baselines for both tasks.

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