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Researcher profile

Liu

Liu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Machine LearningComputer VisionMethodologyeess.SYGraphicsHuman-Computer InteractionSystems and Control

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Published work

8 published item(s)

preprint2026arXiv

Enhancing Consistency Models for Multi-Agent Trajectory Prediction

Diffusion models for multi-agent trajectory prediction are limited by iterative denoising, which causes inference latency that hinders their use in time-critical settings like autonomous driving. Fast-sampling variants using DDIM and informed initial noise distributions partially alleviate this issue, but they either fail to achieve true single-step generation or are constrained by the chosen noise distribution. Consistency Models (CMs) offer high-quality one-step generation by mapping noise directly to data, but are difficult to train from scratch . We propose ECTraj, an enhanced CM pipeline with improved training and conditional generation for trajectory prediction. Our framework extends the student-teacher consistency training scheme: the student produces standard outputs, while the teacher explicitly fuses its predictions with parts of the ground truth to give stronger supervision. We also exploit CMs' direct denoising for top-K multi-shot generation during training. Combining conditional generation with this enhanced consistency objective yields faster inference and improved prediction accuracy, establishing competitive new benchmarks on the large-scale Argoverse 2 dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Low Overhead Beam Alignment for Mobile Millimeter Channel Based on Continuous-Time Prediction

In millimeter-wave (mmWave) communications, directional transmission based on beamforming is important to compensate for high pathloss. To maintain the desired direction transmission gain, beam scanning that involves the transmitter sending the pilot signal over all available beam directions to find the optimal beam is often considered. Alternatively, beam tracking using partial beams can save the beam training overhead through algorithms such as statistical analysis models and kalman filter (KF). Unfortunately, existing beam tracking solutions are limited to a fixed beam variation pattern. In this work, we propose an adaptive online beam alignment (AOBA) scheme, which aims to reduce training overhead and achieve accurate beam alignment for any movement profile. The proposed AOBA periodically performs beam tracking using a small amount but carefully selected candidate beams and switches to beam scanning using all available beams based on a given switching rule. During the interval without the pilot signal, the optimal beam at an arbitrary time instant is predicted with the aid of the recently proposed ordinary differential equation (ODE)-long short-term memory (LSTM) model. Extensive simulations are conducted to evaluate the performance of the proposed AOBA in comparison with several existing beam alignment schemes.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Kubric: A scalable dataset generator

Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details. But collecting, processing and annotating real data at scale is difficult, expensive, and frequently raises additional privacy, fairness and legal concerns. Synthetic data is a powerful tool with the potential to address these shortcomings: 1) it is cheap 2) supports rich ground-truth annotations 3) offers full control over data and 4) can circumvent or mitigate problems regarding bias, privacy and licensing. Unfortunately, software tools for effective data generation are less mature than those for architecture design and training, which leads to fragmented generation efforts. To address these problems we introduce Kubric, an open-source Python framework that interfaces with PyBullet and Blender to generate photo-realistic scenes, with rich annotations, and seamlessly scales to large jobs distributed over thousands of machines, and generating TBs of data. We demonstrate the effectiveness of Kubric by presenting a series of 13 different generated datasets for tasks ranging from studying 3D NeRF models to optical flow estimation. We release Kubric, the used assets, all of the generation code, as well as the rendered datasets for reuse and modification.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Research on Parallel SVM Algorithm Based on Cascade SVM

Cascade SVM (CSVM) can group datasets and train subsets in parallel, which greatly reduces the training time and memory consumption. However, the model accuracy obtained by using this method has some errors compared with direct training. In order to reduce the error, we analyze the causes of error in grouping training, and summarize the grouping without error under ideal conditions. A Balanced Cascade SVM (BCSVM) algorithm is proposed, which balances the sample proportion in the subset after grouping to ensure that the sample proportion in the subset is the same as the original dataset. At the same time, it proves that the accuracy of the model obtained by BCSVM algorithm is higher than that of CSVM. Finally, two common datasets are used for experimental verification, and the results show that the accuracy error obtained by using BCSVM algorithm is reduced from 1% of CSVM to 0.1%, which is reduced by an order of magnitude.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Robust analyses for longitudinal clinical trials with missing and non-normal continuous outcomes

Missing data is unavoidable in longitudinal clinical trials, and outcomes are not always normally distributed. In the presence of outliers or heavy-tailed distributions, the conventional multiple imputation with the mixed model with repeated measures analysis of the average treatment effect (ATE) based on the multivariate normal assumption may produce bias and power loss. Control-based imputation (CBI) is an approach for evaluating the treatment effect under the assumption that participants in both the test and control groups with missing outcome data have a similar outcome profile as those with an identical history in the control group. We develop a general robust framework to handle non-normal outcomes under CBI without imposing any parametric modeling assumptions. Under the proposed framework, sequential weighted robust regressions are applied to protect the constructed imputation model against non-normality in both the covariates and the response variables. Accompanied by the subsequent mean imputation and robust model analysis, the resulting ATE estimator has good theoretical properties in terms of consistency and asymptotic normality. Moreover, our proposed method guarantees the analysis model robustness of the ATE estimation, in the sense that its asymptotic results remain intact even when the analysis model is misspecified. The superiority of the proposed robust method is demonstrated by comprehensive simulation studies and an AIDS clinical trial data application.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Sensitivity analysis in longitudinal clinical trials via distributional imputation

Missing data is inevitable in longitudinal clinical trials. Conventionally, the missing at random assumption is assumed to handle missingness, which however is unverifiable empirically. Thus, sensitivity analysis is critically important to assess the robustness of the study conclusions against untestable assumptions. Toward this end, regulatory agencies often request using imputation models such as return-to-baseline, control-based, and washout imputation. Multiple imputation is popular in sensitivity analysis; however, it may be inefficient and result in an unsatisfying interval estimation by Rubin's combining rule. We propose distributional imputation (DI) in sensitivity analysis, which imputes each missing value by samples from its target imputation model given the observed data. Drawn on the idea of Monte Carlo integration, the DI estimator solves the mean estimating equations of the imputed dataset. It is fully efficient with theoretical guarantees. Moreover, we propose weighted bootstrap to obtain a consistent variance estimator, taking into account the variabilities due to model parameter estimation and target parameter estimation. The finite-sample performance of DI inference is assessed in the simulation study. We apply the proposed framework to an antidepressant longitudinal clinical trial involving missing data to investigate the robustness of the treatment effect. Our proposed DI approach detects a statistically significant treatment effect in both the primary analysis and sensitivity analysis under certain prespecified sensitivity models in terms of the average treatment effect, the risk difference, and the quantile treatment effect in lower quantiles of the responses, uncovering the benefit of the test drug for curing depression.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Understanding the Role of Context in Creating Enjoyable Co-Located Interactions

In recent years, public discourse has blamed digital technologies for making people feel "alone together," distracting us from engaging with one another, even when we are interacting in-person. We argue that in order to design technologies that foster and augment co-located interactions, we need to first understand the context in which enjoyable co-located socialization takes place. We address this gap by surveying and interviewing over 1,000 U.S.-based participants to understand what, where, with whom, how, and why people enjoy spending time in-person. Our findings suggest that people enjoy engaging in everyday activities with individuals with whom they have strong social ties because it helps enable nonverbal cues, facilitate spontaneity, support authenticity, encourage undivided attention, and leverage the physicality of their bodies and the environment. We conclude by providing a set of recommendations for designers interested in creating co-located technologies that encourage social engagement and relationship building.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Smoothed Analysis of Online Lasso for the Sparse Linear Contextual Bandit Problem

We investigate the sparse linear contextual bandit problem where the parameter $θ$ is sparse. To relieve the sampling inefficiency, we utilize the "perturbed adversary" where the context is generated adversarilly but with small random non-adaptive perturbations. We prove that the simple online Lasso supports sparse linear contextual bandit with regret bound $\mathcal{O}(\sqrt{kT\log d})$ even when $d \gg T$ where $k$ and $d$ are the number of effective and ambient dimension, respectively. Compared to the recent work from Sivakumar et al. (2020), our analysis does not rely on the precondition processing, adaptive perturbation (the adaptive perturbation violates the i.i.d perturbation setting) or truncation on the error set. Moreover, the special structures in our results explicitly characterize how the perturbation affects exploration length, guide the design of perturbation together with the fundamental performance limit of perturbation method. Numerical experiments are provided to complement the theoretical analysis.

0 citations0 reviewsNo code linkedOpen access