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Weihao Gao

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11 papers

2 author rows

AIIM Journal 2024 Journal Article

OphGLM: An ophthalmology large language-and-vision assistant

Zhuo Deng
Weihao Gao
Chucheng Chen
Zhiyuan Niu
Zheng Gong
Ruiheng Zhang
Zhenjie Cao
Fang Li

Vision computer-aided diagnostic methods have been used in early ophthalmic disease screening and diagnosis. However, the limited output formats of these methods lead to poor human–computer interaction and low clinical applicability value. Thus, ophthalmic visual question answering is worth studying. Unfortunately, no practical solutions exist before Large Language Models(LLMs). In this paper, we investigate the ophthalmic visual diagnostic interaction problem. We construct an ophthalmology large language-and-vision assistant, OphGLM, consisting of an image encoder, a text encoder, a fusion module, and an LLM module. We establish a new Chinese ophthalmic fine-tuning dataset, FundusTuning-CN, including the fundus instruction and conversation sets. Based on FundusTuning-CN, we establish a novel LLM-tuning strategy to introduce visual model understanding and ophthalmic knowledge into LLMs at a low cost and high efficiency. Leveraging the pre-training of the image encoder, OphGLM demonstrates strong visual understanding and surpasses open-source visual language models in common fundus disease classification tasks. The FundusTuning-CN enables OphGLM to surpass open-source medical LLMs in both ophthalmic knowledge and interactive capabilities. Our proposed OphGLM has the potential to revolutionize clinical applications in ophthalmology. The dataset, code, and models will be publicly available at https: //github. com/ML-AILab/OphGLM.

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ICML Conference 2023 Conference Paper

Machine Learning Force Fields with Data Cost Aware Training

Alexander Bukharin
Tianyi Liu
Shengjie Wang 0001
Simiao Zuo
Weihao Gao
Wen Yan
Tuo Zhao

Machine learning force fields (MLFF) have been proposed to accelerate molecular dynamics (MD) simulation, which finds widespread applications in chemistry and biomedical research. Even for the most data-efficient MLFFs, reaching chemical accuracy can require hundreds of frames of force and energy labels generated by expensive quantum mechanical algorithms, which may scale as $O(n^3)$ to $O(n^7)$, with $n$ proportional to the number of basis functions. To address this issue, we propose a multi-stage computational framework – ASTEROID, which lowers the data cost of MLFFs by leveraging a combination of cheap inaccurate data and expensive accurate data. The motivation behind ASTEROID is that inaccurate data, though incurring large bias, can help capture the sophisticated structures of the underlying force field. Therefore, we first train a MLFF model on a large amount of inaccurate training data, employing a bias-aware loss function to prevent the model from overfitting the potential bias of this data. We then fine-tune the obtained model using a small amount of accurate training data, which preserves the knowledge learned from the inaccurate training data while significantly improving the model’s accuracy. Moreover, we propose a variant of ASTEROID based on score matching for the setting where the inaccurate training data are unlabeled. Extensive experiments on MD datasets and downstream tasks validate the efficacy of ASTEROID. Our code and data are available at https: //github. com/abukharin3/asteroid.

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ICLR Conference 2022 Conference Paper

Label Leakage and Protection in Two-party Split Learning

Oscar Li
Jiankai Sun
Xin Yang 0017
Weihao Gao
Hongyi Zhang
Junyuan Xie
Virginia Smith
Chong Wang 0002

Two-party split learning is a popular technique for learning a model across feature-partitioned data. In this work, we explore whether it is possible for one party to steal the private label information from the other party during split training, and whether there are methods that can protect against such attacks. Specifically, we first formulate a realistic threat model and propose a privacy loss metric to quantify label leakage in split learning. We then show that there exist two simple yet effective methods within the threat model that can allow one party to accurately recover private ground-truth labels owned by the other party. To combat these attacks, we propose several random perturbation techniques, including $\texttt{Marvell}$, an approach that strategically finds the structure of the noise perturbation by minimizing the amount of label leakage (measured through our quantification metric) of a worst-case adversary. We empirically demonstrate the effectiveness of our protection techniques against the identified attacks, and show that $\texttt{Marvell}$ in particular has improved privacy-utility tradeoffs relative to baseline approaches.

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UAI Conference 2022 Conference Paper

PathFlow: A normalizing flow generator that finds transition paths

Tianyi Liu
Weihao Gao
Zhirui Wang
Chong Wang 0002

Sampling from a Boltzmann distribution to calculate important macro statistics is one of the central tasks in the study of large atomic and molecular systems. Recently, a one-shot configuration sampler, the Boltzmann generator [Noé et al. , 2019], is introduced. Though a Boltzmann generator can directly generate independent metastable states, it lacks the ability to find transition pathways and describe the whole transition process. In this paper, we propose PathFlow that can function as a one-shot generator as well as a transition pathfinder. More specifically, a normalizing flow model is constructed to map the base distribution and linear interpolated path in the latent space to the Boltzmann distribution and a minimum (free) energy path in the configuration space simultaneously. PathFlow can be trained by standard gradient-based optimizers using the proposed gradient estimator with a theoretical guarantee. PathFlow, validated with the extensively studied examples including a synthetic Müller potential and Alanine dipeptide, shows a remarkable performance.

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AAAI Conference 2020 Conference Paper

Information-Theoretic Understanding of Population Risk Improvement with Model Compression

Yuheng Bu
Weihao Gao
Shaofeng Zou
Venugopal Veeravalli

We show that model compression can improve the population risk of a pre-trained model, by studying the tradeoff between the decrease in the generalization error and the increase in the empirical risk with model compression. We ﬁrst prove that model compression reduces an information-theoretic bound on the generalization error; this allows for an interpretation of model compression as a regularization technique to avoid overﬁtting. We then characterize the increase in empirical risk with model compression using rate distortion theory. These results imply that the population risk could be improved by model compression if the decrease in generalization error exceeds the increase in empirical risk. We show through a linear regression example that such a decrease in population risk due to model compression is indeed possible. Our theoretical results further suggest that the Hessian-weighted K-means clustering compression approach can be improved by regularizing the distance between the clustering centers. We provide experiments with neural networks to support our theoretical assertions.

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ICML Conference 2019 Conference Paper

Rate Distortion For Model Compression: From Theory To Practice

Weihao Gao
Yu-Han Liu
Chong Wang 0002
Sewoong Oh

The enormous size of modern deep neural net-works makes it challenging to deploy those models in memory and communication limited scenarios. Thus, compressing a trained model without a significant loss in performance has become an increasingly important task. Tremendous advances has been made recently, where the main technical building blocks are pruning, quantization, and low-rank factorization. In this paper, we propose principled approaches to improve upon the common heuristics used in those building blocks, by studying the fundamental limit for model compression via the rate distortion theory. We prove a lower bound for the rate distortion function for model compression and prove its achievability for linear models. Although this achievable compression scheme is intractable in practice, this analysis motivates a novel objective function for model compression, which can be used to improve classes of model compressor such as pruning or quantization. Theoretically, we prove that the proposed scheme is optimal for compressing one-hidden-layer ReLU neural networks. Empirically, we show that the proposed scheme improves upon the baseline in the compression-accuracy tradeoff.

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NeurIPS Conference 2018 Conference Paper

The Nearest Neighbor Information Estimator is Adaptively Near Minimax Rate-Optimal

Jiantao Jiao
Weihao Gao
Yanjun Han

We analyze the Kozachenko–Leonenko (KL) fixed k-nearest neighbor estimator for the differential entropy. We obtain the first uniform upper bound on its performance for any fixed k over H\"{o}lder balls on a torus without assuming any conditions on how close the density could be from zero. Accompanying a recent minimax lower bound over the H\"{o}lder ball, we show that the KL estimator for any fixed k is achieving the minimax rates up to logarithmic factors without cognizance of the smoothness parameter s of the H\"{o}lder ball for $s \in (0, 2]$ and arbitrary dimension d, rendering it the first estimator that provably satisfies this property.

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NeurIPS Conference 2017 Conference Paper

Discovering Potential Correlations via Hypercontractivity

Hyeji Kim
Weihao Gao
Sreeram Kannan
Sewoong Oh
Pramod Viswanath

Discovering a correlation from one variable to another variable is of fundamental scientific and practical interest. While existing correlation measures are suitable for discovering average correlation, they fail to discover hidden or potential correlations. To bridge this gap, (i) we postulate a set of natural axioms that we expect a measure of potential correlation to satisfy; (ii) we show that the rate of information bottleneck, i. e. , the hypercontractivity coefficient, satisfies all the proposed axioms; (iii) we provide a novel estimator to estimate the hypercontractivity coefficient from samples; and (iv) we provide numerical experiments demonstrating that this proposed estimator discovers potential correlations among various indicators of WHO datasets, is robust in discovering gene interactions from gene expression time series data, and is statistically more powerful than the estimators for other correlation measures in binary hypothesis testing of canonical examples of potential correlations.

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NeurIPS Conference 2017 Conference Paper

Estimating Mutual Information for Discrete-Continuous Mixtures

Weihao Gao
Sreeram Kannan
Sewoong Oh
Pramod Viswanath

Estimation of mutual information from observed samples is a basic primitive in machine learning, useful in several learning tasks including correlation mining, information bottleneck, Chow-Liu tree, and conditional independence testing in (causal) graphical models. While mutual information is a quantity well-defined for general probability spaces, estimators have been developed only in the special case of discrete or continuous pairs of random variables. Most of these estimators operate using the 3H -principle, i. e. , by calculating the three (differential) entropies of X, Y and the pair (X, Y). However, in general mixture spaces, such individual entropies are not well defined, even though mutual information is. In this paper, we develop a novel estimator for estimating mutual information in discrete-continuous mixtures. We prove the consistency of this estimator theoretically as well as demonstrate its excellent empirical performance. This problem is relevant in a wide-array of applications, where some variables are discrete, some continuous, and others are a mixture between continuous and discrete components.

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NeurIPS Conference 2016 Conference Paper

Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation

Weihao Gao
Sewoong Oh
Pramod Viswanath

Estimators of information theoretic measures such as entropy and mutual information from samples are a basic workhorse for many downstream applications in modern data science. State of the art approaches have been either geometric (nearest neighbor (NN) based) or kernel based (with bandwidth chosen to be data independent and vanishing sub linearly in the sample size). In this paper we combine both these approaches to design new estimators of entropy and mutual information that strongly outperform all state of the art methods. Our estimator uses bandwidth choice of fixed $k$-NN distances; such a choice is both data dependent and linearly vanishing in the sample size and necessitates a bias cancellation term that is universal and independent of the underlying distribution. As a byproduct, we obtain a unified way of obtaining both kernel and NN estimators. The corresponding theoretical contribution relating the geometry of NN distances to asymptotic order statistics is of independent mathematical interest.

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ICML Conference 2016 Conference Paper

Conditional Dependence via Shannon Capacity: Axioms, Estimators and Applications

Weihao Gao
Sreeram Kannan
Sewoong Oh
Pramod Viswanath

We consider axiomatically the problem of estimating the strength of a conditional dependence relationship P_Y|X from a random variables X to a random variable Y. This has applications in determining the strength of a known causal relationship, where the strength depends only on the conditional distribution of the effect given the cause (and not on the driving distribution of the cause). Shannon capacity, appropriately regularized, emerges as a natural measure under these axioms. We examine the problem of calculating Shannon capacity from the observed samples and propose a novel fixed-k nearest neighbor estimator, and demonstrate its consistency. Finally, we demonstrate an application to single-cell flow-cytometry, where the proposed estimators significantly reduce sample complexity.

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