Author name cluster

Tian Li

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.

11 papers

2 author rows

AAAI Conference 2025 Conference Paper

AI-Powered Algorithm-Centric Quantum Processor Topology Design

Tian Li
Xiao-Yue Xu
Chen Ding
Tian-Ci Tian
Wei-You Liao
Shuo Zhang
He-Liang Huang

Quantum computing promises to revolutionize various fields, yet the execution of quantum programs necessitates an effective compilation process. This involves strategically mapping quantum circuits onto the physical qubits of a quantum processor. The qubits' arrangement, or topology, is pivotal to the circuit's performance, a factor that often defies traditional heuristic or manual optimization methods due to its complexity. In this study, we introduce a novel approach leveraging reinforcement learning to dynamically tailor qubit topologies to the unique specifications of individual quantum circuits, guiding algorithm-driven quantum processor topology design for reducing the depth of mapped circuit, which is particularly critical for the output accuracy on noisy quantum processors. Our method marks a significant departure from previous methods that have been constrained to mapping circuits onto a fixed processor topology. Experiments demonstrate that we have achieved notable enhancements in circuit performance, with a minimum of 20% reduction in circuit depth in 60% of the cases examined, and a maximum enhancement of up to 46%. Furthermore, the pronounced benefits of our approach in reducing circuit depth become increasingly evident as the scale of the quantum circuits increases, exhibiting the scalability of our method in terms of problem size. This work advances the co-design of quantum processor architecture and algorithm mapping, offering a promising avenue for future research and development in the field.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

Efficient Adaptive Federated Optimization

Su Hyeong Lee
Sidharth Sharma
Manzil Zaheer
Tian Li

Adaptive optimization is critical in federated learning, where enabling adaptivity on both the server and client sides has proven essential for achieving optimal performance. However, the scalability of such jointly adaptive systems is often hindered by resource limitations in communication and memory. In this paper, we introduce a class of efficient adaptive algorithms, named $FedAda^2$ and its enhanced version $FedAda^2$++, designed specifically for large-scale, cross-device federated environments. $FedAda^2$ optimizes communication efficiency by avoiding the transfer of preconditioners between the server and clients. Additionally, $FedAda^2$++ extends this approach by incorporating memory-efficient adaptive optimizers on the client side, further reducing on-device memory usage. Theoretically, we demonstrate that $FedAda^2$ and $FedAda^2$++ achieve the same convergence rates for general, non-convex objectives as its more resource-intensive counterparts that directly integrate joint adaptivity. Extensive empirical evaluations on image and text datasets demonstrate both the advantages of joint adaptivity and the effectiveness and efficiency of $FedAda^2$/$FedAda^2$++.

PDF Details

NeurIPS Conference 2025 Conference Paper

Private Zeroth-Order Optimization with Public Data

Xuchen Gong
Tian Li

One of the major bottlenecks for deploying popular first-order differentially private (DP) machine learning algorithms (e. g. , DP-SGD) lies in their high computation and memory cost, despite the existence of optimized implementations. Zeroth-order methods have promise in mitigating the overhead, as they leverage function evaluations to approximate the gradients, hence significantly easier to privatize. While recent works have explored zeroth-order approaches in both private and non-private settings, they still suffer from relatively low utilities compared with DP-SGD, and have only been evaluated in limited application domains. In this work, we propose to leverage public information to guide and improve gradient approximation of private zeroth-order algorithms. We explore a suite of \underline{p}ublic-data-\underline{a}ssisted \underline{z}eroth-\underline{o}rder optimizers (PAZO) with minimal overhead. We provide theoretical analyses of the PAZO framework under an assumption of the similarity between public and private data. Empirically, we demonstrate that PAZO achieves superior privacy/utility tradeoffs across vision and text tasks in both pre-training and fine-tuning settings, outperforming the best first-order baselines (with public data) especially in highly private regimes, while offering up to $16\times$ runtime speedup.

PDF Details

AAAI Conference 2025 Conference Paper

Scalable and Trustworthy Learning in Heterogeneous Networks

Tian Li

To build a responsible data economy and protect data ownerhip, it is crucial to enable learning models from separate, heterogeneous data sources without centralization. For example, federated learning (FL) aims to train models across massive remote devices or isolated organizations, while keeping user data local. However, federated learning can face critical practical issues such as scalability, noisy samples, biased learning systems or procedures, and privacy leakage. At the intersection between optimization, trustworthy (fair, robust, and private) ML, and learning in heterogeneous environments, my research aims to support scalable and responsible data sharing to collectively build intelligent models.

PDF Details DOI

TMLR Journal 2024 Journal Article

Maximizing Global Model Appeal in Federated Learning

Yae Jee Cho
Divyansh Jhunjhunwala
Tian Li
Virginia Smith
Gauri Joshi

Federated learning (FL) aims to collaboratively train a global model using local data from a network of clients. To warrant collaborative training, each federated client may expect the resulting global model to satisfy some individual requirement, such as achieving a certain loss threshold on their local data. However, in real FL scenarios, the global model may not satisfy the requirements of all clients in the network due to the data heterogeneity across clients. In this work, we explore the problem of global model appeal in FL, which we define as the total number of clients that find that the global model satisfies their individual requirements. We discover that global models trained using traditional FL approaches can result in a significant number of clients unsatisfied with the model based on their local requirements. As a consequence, we show that global model appeal can directly impact how clients participate in training and how the model performs on new clients at inference time. Our work proposes MaxFL, which maximizes the number of clients that find the global model appealing. MaxFL achieves a $22$-$40\%$ and $18$-$50\%$ improvement in the test accuracy of training clients and (unseen) test clients respectively, compared to a wide range of FL approaches that tackle data heterogeneity, aim to incentivize clients, and learn personalized/fair models.

PDF Details

JBHI Journal 2024 Journal Article

Model Generalizability Investigation for GFCE-MRI Synthesis in NPC Radiotherapy Using Multi-Institutional Patient-Based Data Normalization

Wen Li
Saikit Lam
Yinghui Wang
Chenyang Liu
Tian Li
Jens Kleesiek
Andy Lai-Yin Cheung
Ying Sun

Recently, deep learning has been demonstrated to be feasible in eliminating the use of gadoliniumbased contrast agents (GBCAs) through synthesizing gadolinium-free contrast-enhanced MRI (GFCE-MRI) from contrast-free MRI sequences, providing the community with an alternative to get rid of GBCAs-associated safety issues in patients. Nevertheless, generalizability assessment of the GFCE-MRI model has been largely challenged by the high inter-institutional heterogeneity of MRI data, on top of the scarcity of multi-institutional data itself. Although various data normalization methods have been adopted to address the heterogeneity issue, it has been limited to single-institutional investigation and there is no standard normalization approach presently. In this study, we aimed at investigating generalizability of GFCE-MRI model using data from seven institutions by manipulating heterogeneity of MRI data under five popular normalization approaches. Three state-of-the-art neural networks were applied to map from T1-weighted and T2-weighted MRI to contrast-enhanced MRI (CE-MRI) for GFCE-MRI synthesis in patients with nasopharyngeal carcinoma. MRI data from three institutions were used separately to generate three uni-institution models and jointly for a tri-institution model. The five normalization methods were applied to normalize the data of each model. MRI data from the remaining four institutions served as external cohorts for model generalizability assessment. Quality of GFCE-MRI was quantitatively evaluated against ground-truth CE-MRI using mean absolute error (MAE) and peak signal-to-noise ratio(PSNR). Results showed that performance of all uni-institution models remarkably dropped on the external cohorts. By contrast, model trained using multi-institutional data with Z-Score normalization yielded the best model generalizability improvement.

Details DOI

JMLR Journal 2023 Journal Article

On Tilted Losses in Machine Learning: Theory and Applications

Tian Li
Ahmad Beirami
Maziar Sanjabi
Virginia Smith

Exponential tilting is a technique commonly used in fields such as statistics, probability, information theory, and optimization to create parametric distribution shifts. Despite its prevalence in related fields, tilting has not seen widespread use in machine learning. In this work, we aim to bridge this gap by exploring the use of tilting in risk minimization. We study a simple extension to ERM---tilted empirical risk minimization (TERM)---which uses exponential tilting to flexibly tune the impact of individual losses. The resulting framework has several useful properties: We show that TERM can increase or decrease the influence of outliers, respectively, to enable fairness or robustness; has variance-reduction properties that can benefit generalization; and can be viewed as a smooth approximation to the tail probability of losses. Our work makes connections between TERM and related objectives, such as Value-at-Risk, Conditional Value-at-Risk, and distributionally robust optimization (DRO). We develop batch and stochastic first-order optimization methods for solving TERM, provide convergence guarantees for the solvers, and show that the framework can be efficiently solved relative to common alternatives. Finally, we demonstrate that TERM can be used for a multitude of applications in machine learning, such as enforcing fairness between subgroups, mitigating the effect of outliers, and handling class imbalance. Despite the straightforward modification TERM makes to traditional ERM objectives, we find that the framework can consistently outperform ERM and deliver competitive performance with state-of-the-art, problem-specific approaches. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2023. ( edit, beta )

PDF Details

IJCAI Conference 2022 Conference Paper

Domain-Adaptive Text Classification with Structured Knowledge from Unlabeled Data

Tian Li
Xiang Chen
Zhen Dong
Kurt Keutzer
Shanghang Zhang

Domain adaptive text classification is a challenging problem for the large-scale pretrained language models because they often require expensive additional labeled data to adapt to new domains. Existing works usually fails to leverage the implicit relationships among words across domains. In this paper, we propose a novel method, called Domain Adaptation with Structured Knowledge (DASK), to enhance domain adaptation by exploiting word-level semantic relationships. DASK first builds a knowledge graph to capture the relationship between pivot terms (domain-independent words) and non-pivot terms in the target domain. Then during training, DASK injects pivot-related knowledge graph information into source domain texts. For the downstream task, these knowledge-injected texts are fed into a BERT variant capable of processing knowledge-injected textual data. Thanks to the knowledge injection, our model learns domain-invariant features for non-pivots according to their relationships with pivots. DASK ensures the pivots to have domain-invariant behaviors by dynamically inferring via the polarity scores of candidate pivots during training with pseudo-labels. We validate DASK on a wide range of cross-domain sentiment classification tasks and observe up to 2. 9% absolute performance improvement over baselines for 20 different domain pairs. Code is available at https: //github. com/hikaru-nara/DASK.

PDF Details DOI

NeurIPS Conference 2021 Conference Paper

Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing

Mikhail Khodak
Renbo Tu
Tian Li
Liam Li
Maria-Florina F. Balcan
Virginia Smith
Ameet Talwalkar

Tuning hyperparameters is a crucial but arduous part of the machine learning pipeline. Hyperparameter optimization is even more challenging in federated learning, where models are learned over a distributed network of heterogeneous devices; here, the need to keep data on device and perform local training makes it difficult to efficiently train and evaluate configurations. In this work, we investigate the problem of federated hyperparameter tuning. We first identify key challenges and show how standard approaches may be adapted to form baselines for the federated setting. Then, by making a novel connection to the neural architecture search technique of weight-sharing, we introduce a new method, FedEx, to accelerate federated hyperparameter tuning that is applicable to widely-used federated optimization methods such as FedAvg and recent variants. Theoretically, we show that a FedEx variant correctly tunes the on-device learning rate in the setting of online convex optimization across devices. Empirically, we show that FedEx can outperform natural baselines for federated hyperparameter tuning by several percentage points on the Shakespeare, FEMNIST, and CIFAR-10 benchmarks—obtaining higher accuracy using the same training budget.

PDF Details

IROS Conference 2021 Conference Paper

You Only Group Once: Efficient Point-Cloud Processing with Token Representation and Relation Inference Module

Chenfeng Xu
Bohan Zhai
Bichen Wu
Tian Li
Wei Zhan
Peter Vajda
Kurt Keutzer
Masayoshi Tomizuka

3D perception on point-cloud is a challenging and crucial computer vision task. A point-cloud consists of a sparse, unstructured, and unordered set of points. To understand a point-cloud, previous point-based methods, such as PointNet++, extract visual features through the hierarchical aggregation of local features. However, such methods have several critical limitations: 1) They require considerable sampling and grouping operations, which leads to low inference speed. 2) Despite redundancy among adjacent points, they treat all points alike with an equal amount of computation. 3) They aggregate local features together through downsampling, which causes information loss and hurts perception capability. To overcome these challenges, we propose a novel, simple, and elegant deep learning model called YOGO (You Only Group Once). YOGO divides a point-cloud into a small number of parts and extracts a high-dimensional token to represent points within each sub-region. Next, we use self-attention to capture token-to-token relations, and project the token features back to the point features. We formulate such a series of operations as a relation inference module (RIM). Compared with previous methods, YOGO is very efficient because it only needs to sample and group a point-cloud once. Instead of operating on points, YOGO operates on a small number of tokens, each of which summarizes the point features in a sub-region. This allows us to avoid redundant computation and thus boosts efficiency. Moreover, YOGO preserves pointwise features by projecting token features to point features although the RIM computes on tokens. This avoids information loss and enhances point-wise perception capability. We conduct thorough experiments to demonstrate that YOGO achieves at least 3. 0x speedup over point-based baselines while delivering competitive classification and segmentation performance on a classification dataset and a segmentation dataset based on 3D Wharehouse, and S3DIS datasets. The code is available at https://github.com/chenfengxu714/YOGO.git.

Details

ICRA Conference 2011 Conference Paper

A novel optimal calibration algorithm on a dexterous 6 DOF serial robot-with the optimization of measurement poses number

Tian Li
Kui Sun
Yue Jin
Hong Liu 0002

Normally, people always believe that the more measurement poses used in a robot calibration process, the more accurate result can be obtained. However, the accuracy improvement converges to a threshold after a number of measurement poses. Moreover, robot calibration is a time consuming process, too many poses would seriously complicate the process and consumedly increase the spending time. In this paper, an optimal measurement pose number searching method was proposed to improve the calibration method in time spending aspect. Optimal robot poses were added to an initial pose set one by one to establish a new pose set for the robot calibration. The root mean squares (RMS) of the end-effector pose errors after being calibrated by using these pose sets were calculated. The optimal number of the configuration set which correspond to the least RMS of pose error can then be obtained. This algorithm can get higher end-effector accuracy, meanwhile consume less time. The simulation on a serial robot manipulator with 24 unknown kinematic parameters shows that the end-effector pose accuracy after calibrated by the using of the optimal pose set is much better than the result before calibration, and is better than the using of a random pose set.

Details