Sharon Li

About

I am an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin Madison. Previously I was as a postdoc researcher in the Computer Science department at Stanford University, working with Chris Ré. I completed my PhD from Cornell University in 2017, where I was fortunate to be advised by John E. Hopcroft. I have received Facebook Research Award, JPMorgan early-career faculty award, and was named Forbes 30 Under 30 in Science. I am currently a faculty fellow at the Madison Teaching and Learning Excellence (MTLE) program.

My broad research interests are in deep learning, a branch of machine learning. My time in both academia and industry has shaped my view and approach in research. My research develops algorithms and fundamental understandings to enable reliable open-world learning, which can function safely and adaptively in the presence of evolving and unpredictable data stream. Research topics that I am currently focusing on include:

Learning and inference under distribution shift;

Open-world machine learning;

Human-centered machine learning.

News

5/15/2022: 4 papers accepted by ICML, congratulations to the team!
4/15/2022: Received ICLR Outstanding Paper Award Honorable Mention.
3/30/2022: Will serve as an area chair for NeurIPS 2022.
3/21/2022: Will be co-organizing ICML'22 workshop on Distribution-free Uncertainty Quantification.
3/4/2022: Received gift funding from Adobe Research.
3/1/2022: Paper on unknown-aware object detection accepted by CVPR 2022 as an oral presentation.
1/20/2022: Two papers accepted by ICLR 2022,including one oral presentation (1.6%).
12/3/2021: Will serve as an area chair for ICML 2022.
12/1/2021: Two papers on OOD detection accepted by AAAI 2022 as oral presentations.
11/1/2021: Received Facebook Research Award.
10/25/2021: Received gift funding from Google Brain.
10/22/2021: We released a comprehensive survey on generalized OOD detection.
10/18/2021: Received gift funding from Facebook.
10/17/2021: Research is featured in Fueling Discovery by Wisconsin State Journal.
9/28/2021: Three papers on out-of-distribution detection accepted to NeurIPS 2021. Congrats to the team!
7/22/2021: Paper on frequency-domain image translation (FDIT) accepted to ICCV 2021.
7/15/2021: Received Madison Teaching and Learning Excellence Fellowship.
6/18/2021: Paper on outlier mining for out-of-distribution detection is accepted to ECML 2021.
6/4/2021: Received JPMorgan Chase early career outstanding faculty award.
6/2021: Received American Family Funding Intiative Awards.
4/4/2021: Will be co-organizing two ICML'21 workshops on uncertainty quantification.
2/28/2021: Two papers on OOD detection accepted to CVPR 2021 (including one oral presentation).
1/12/2021: Paper on Model Patching (for handling subgroup shift) accepted to ICLR 2021.
10/28/2020: Will serve as a mentor at the Women in Machine Learning (WiML) workshop at NeurIPS 2020.
9/25/2020: Paper on Energy-based Out-of-distribution Detection accepted to NeurIPS 2020.
8/17/2020: Joined the CS Department at UW-Madison as an Assistant Professor. Here is a featured interview article.
05/19/2020: Gave an invited talk on Out-of-distribution Uncertainty Estimation and Robustness in Open-World Machine Learning at Air Force Research Laboratory’s Workshop.
4/23/2020: Selected Young Researcher to participate in the 8th Heidelberg Laureate Forum.
3/14/2020: Will serve as Program Chair for ICML'20 workshop on Uncertainty and Robustness in Deep Learning.
12/3/2019: My talk is listed as one of 30 Influential AI Presentations in 2019.
12/3/2019: Honored and thrilled to be featured in Forbes 30 Under 30 list in Science.
3/8/2019: Honored to be featured in 30 Under 30 Leading Women in AI.
2/24/2019: Paper on KNN-based robustness accepted as an oral presentation at CVPR 2019.
2/24/2019: Will serve as Program Chair for ICML'19 workshop on Uncertainty and Robustness in Deep Learning.
1/2019: Gave a talk at Deep Learning Summit San Francisco .

[Openings]: I am looking for highly motivated Ph.D. and M.S. students to join my lab. The admission decision at UW-Madison is committee-based. For questions regarding the application process, please read the FAQ.

[Services]:

Program chair: ICML workshop on Uncertainty and Robustness in Deep Learning (UDL) 2019 & 2020.

Area chair and senior program committee: NeurIPS, ICLR, ICML and AAAI.

Co-organizer: ICML workshop on Uncertainty and Robustness in Deep Learning (UDL) 2021.

Co-organizer: ICML workshop on Distribution-free Uncertainty Quantification (DFUQ) 2021.

Co-organizer: ICML workshop on Distribution-free Uncertainty Quantification (DFUQ) 2022.

[Recent/upcoming talks]:

10/2022: Keynote at ECCV workshop on Learning from Limited and Imperfect Data (L2ID)

7/2022: Invited talk at ICML'22 DataPerf Workshop

3/2022: Talk at UT-Austin

3/9/2022: AI Seminar at Oregon State University

2/10/2022: Anomaly Detection for Scientific Discovery (AD4SD) Seminar [Recording]

12/2021: NeurIPS'21 workshop on ImageNet: past, present, and future

12/1/2021: PhysicsMeetsML seminar

9/28/2021: Future of Data-Centric AI virtual event

8/27/2021: Talk at Facebook

8/21/2021: Keynote @ Artificial Intelligence for Anomalies and Novelties (AI4AN 2021)

8/22/2021: Keynote @ Weakly-supervised Representation Learning (WSRL 2021 workshop)

8/2/2021: IFDS workshop on Statistical Approaches to Understanding Modern ML Methods

4/27/2021: MINDS & CIS Seminar at John Hopkins University

4/17/2021: Women in Scientific Education and Research (WISER) at UW-Madison

4/1/2021: Open Data Science Conference (ODSC) East 2021

11/4/2020: Women in Computer Science (WiCS) Seminar at Stanford

10/30/2020: Information Systems Seminar at UC Berkeley

10/28/2020: SILO Seminar at UW-Madison. [Recording]

9/21/2020: MLOS Seminar at UW-Madison+Microsoft. [Recording]

[Teaching]:

Spring 2022: CS540 Introduction to Artificial Intelligence .

Fall 2021: CS762 Advanced Deep Learning

Spring 2021: CS540 Introduction to Artificial Intelligence .

Fall 2020: CS839 Advanced Topics in Deep Learning (a newly developed graduate-level course).

Misc
I travel and occasionally take photos. Here is my pictorial Travel Memo. This is the treasure I shoot with.

Research Highlights

(see full publication list)

Unknown-Aware Object Detection: Learning What You Don’t Know from Videos in the Wild

Xuefeng Du, Xin Wang, Gabriel Gozum, and Yixuan Li

We propose a new unknown-aware object detection framework through Spatial-Temporal Unknown Distillation (STUD), which distills unknown objects from videos in the wild and meaningfully regularizes the model’s decision boundary. STUD first identifies the unknown candidate object proposals in the spatial dimension, and then aggregates the candidates across multiple video frames to form a diverse set of unknown objects near the decision boundary. Alongside, we employ an energy-based uncertainty regularization loss, which contrastively shapes the uncertainty space between the in-distribution and distilled unknown objects.

CVPR 2022, Oral Presentation
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VOS: Learning What You Don’t Know by Virtual Outlier Synthesis

Xuefeng Du, Zhaoning Wang, Mu Cai, and Yixuan Li

One of the key challenges in OOD detection is that models lack supervision signals from unknown data. Previous approaches rely on real outlier datasets for model regularization, which can be costly and sometimes infeasible to obtain in practice. In this paper, we present VOS, a novel framework for OOD detection by adaptively synthesizing virtual outliers that can meaningfully regularize the model's decision boundary during training. Specifically, VOS samples virtual outliers from the low-likelihood region of the class-conditional distribution estimated in the feature space. VOS achieves strong performance on both object detection and image classification models.

ICLR 2022
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PiCO: Contrastive Label Disambiguation for Partial Label Learning

Haobo Wang, Ruixuan Xiao, Yixuan Li, Lei Feng, Gang Niu, Gang Chen, and Junbo Zhao

Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity. Despite the promise, the performance of PLL often lags behind the supervised counterpart. In this work, we bridge the gap by addressing two key research challenges in PLL -- representation learning and label disambiguation -- in one coherent framework. Specifically, our proposed framework PiCO consists of a contrastive learning module along with a novel class prototype-based label disambiguation algorithm. PiCO produces closely aligned representations for examples from the same classes and facilitates label disambiguation. Extensive experiments demonstrate that PiCO significantly outperforms the current state-of-the-art approaches in PLL and even achieves comparable results to fully supervised learning.

ICLR 2022, Outstanding Paper Honorable Mention
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Out-of-Distribution Detection with Posterior Sampling

Yifei Ming*, Ying Fan*, and Yixuan Li

We propose a novel posterior sampling based outlier mining framework, POEM, which facilitates efficient use of outlier data and promotes learning a compact decision boundary between ID and OOD data for improved detection. Compared to the current best method that uses a greedy sampling strategy, POEM improves the relative performance by 42.0% and 24.2% (FPR95) on CIFAR-10 and CIFAR-100, respectively. We further provide theoretical insights on the effectiveness of POEM for OOD detection.

ICML 2022, Long Talk
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Out-of-distribution Detection with Deep Nearest Neighbors

Yiyou Sun, Yifei Ming, Xiaojin Zhu, and Yixuan Li

In this paper, we explore the efficacy of non-parametric nearest-neighbor distance for OOD detection, which has been largely overlooked in the literature. Unlike prior works, our method does not impose any distributional assumption, hence providing stronger flexibility and generality. We demonstrate the effectiveness of nearest-neighbor-based OOD detection on several benchmarks and establish superior performance. Under the same model trained on ImageNet-1k, our method reduces the false positive rate (FPR@TPR95) by 24.77% compared to a strong baseline, which uses a parametric approach Mahalanobis distance in detection.

ICML 2022
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Training OOD Detectors in their Natural Habitats

Julian Katz-Samuels*, Julia Nakhleh*, Robert Nowak, and Yixuan Li

(* equal contribution)

Recent methods use auxiliary outlier data to regularize the model for improved OOD detection. However, these approaches make a strong distributional assumption that the auxiliary outlier data is completely separable from the in-distribution (ID) data. In this paper, we propose a novel framework that leverages wild mixture data---that naturally consists of both ID and OOD samples. Such wild data is abundant and arises freely upon deploying a machine learning classifier in their natural habitats. Our key idea is to formulate a constrained optimization problem and to show how to tractably solve it. Our learning objective maximizes the OOD detection rate, subject to constraints on the classification error of ID data and on the OOD error rate of ID examples. We extensively evaluate our approach on common OOD detection tasks and demonstrate superior performance.

ICML 2022
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Mitigating Neural Network Overconfidence with Logit Normalization

Hongxin Wei, Renchunzi Xie, Hao Cheng, Lei Feng, Bo An, and Yixuan Li

Neural networks are known to suffer from the overconfidence issue, where they produce abnormally high confidence for both in- and out-of-distribution inputs. In this work, we show that this issue can be mitigated through Logit Normalization (LogitNorm) --- a simple fix to the cross-entropy loss --- by enforcing a constant vector norm on the logits in training. Trained with LogitNorm, neural networks produce highly distinguishable confidence scores between in- and out-of-distribution data. Extensive experiments demonstrate the superiority of LogitNorm.

ICML 2022
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Provable Guarantees for Understanding Out-of-distribution Detection

Peyman Morteza and Yixuan Li

Out-of-distribution (OOD) detection is important for deploying machine learning models in the real world, where test data from shifted distributions can naturally arise. While a plethora of algorithmic approaches have recently emerged for OOD detection, a critical gap remains in theoretical understanding. In this work, we develop an analytical framework that characterizes and unifies the theoretical understanding for OOD detection. Our analytical framework motivates a novel OOD detection method for neural networks, GEM, which demonstrates both theoretical and empirical superiority. In particular, on CIFAR-100 as in-distribution data, our method outperforms a competitive baseline by 16.57% (FPR95). Lastly, we formally provide provable guarantees and comprehensive analysis of our method, underpinning how various properties of data distribution affect the performance of OOD detection.

AAAI 2022, Oral Presentation
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On the Impact of Spurious Correlation for Out-of-distribution Detection

Yifei Ming, Hang Yin, and Yixuan Li

While much research attention has been placed on designing new out-of-distribution (OOD) detection methods, the precise definition of OOD is often left in vagueness and falls short of the desired notion of OOD in reality. In this paper, we present a new formalization and model the data shifts by taking into account both the invariant and environmental (spurious) features. Under such formalization, we systematically investigate how spurious correlation in the training set impacts OOD detection. Our results suggest that the detection performance is severely worsened when the correlation between spurious features and labels is increased in the training set. We further show insights on detection methods that are more effective in reducing the impact of spurious correlation and provide theoretical analysis on why reliance on environmental features leads to high OOD detection error.

AAAI 2022, Oral Presentation
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ReAct: Out-of-distribution Detection With Rectified Activations

Yiyou Sun, Chuan Guo and Yixuan Li

This paper provides a simple activation rectification strategy termed ReAct, which truncates thehigh activations during test time for OOD detection. We provide both empirical and theoreticalinsights characterizing and explaining the mechanism by which ReAct improves OOD uncertainty estimation. By rectifying the activations, the outsized contribution of hidden units on OOD outputcan be attenuated, resulting in a stronger separability from ID data. Extensive experiments show ReAct can significantly improve the performance of OOD detection on both common benchmarksand large-scale image classification models.

NeurIPS 2021
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Can multi-label classification networks know what they don’t know?

Haoran Wang, Weitang Liu, Alex Bocchieri and Yixuan Li

Improved methods for OOD detection in multi-class classification have emerged, while OOD detection methods for multi-label classification remain underexplored and use rudimentary techniques. We propose JointEnergy, a simple and effective method, which estimates the OOD indicator scores by aggregating label-wise energy scores from multiple labels. We show that JointEnergy can be mathematically interpreted from a joint likelihood perspective. Our results show consistent improvement over previous methods that are based on the maximum-valued scores, which fail to capture joint information from multiple labels. We demonstrate the effectiveness of our method on three common multi-label classification benchmarks, including MS-COCO, PASCAL-VOC, and NUS-WIDE. We show that JointEnergy can reduce the FPR95 by up to 10.05% compared to the previous best baseline, establishing state-of-the-art performance.

NeurIPS 2021
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On the Importance of Gradients for Detecting Distributional Shifts in the Wild

Rui Huang, Andrew Geng and Yixuan Li

Existing OOD detection approaches primarily rely on the output or feature space for deriving OOD scores, while largely overlooking information from the gradient space. In this paper, we present GradNorm, a simple and effective approach for detecting OOD inputs by utilizing information extracted from the gradient space. GradNorm directly employs the vector norm of gradients, backpropagated from the KL divergence between the softmax output and a uniform probability distribution. Our key idea is that the magnitude of gradients is higher for in-distribution (ID) data than that for OOD data, making it informative for OOD detection. GradNorm demonstrates superior performance, reducing the average FPR95 by up to 10.89% compared to the previous best method.

NeurIPS 2021
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MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space

Rui Huang and Yixuan Li

Existing OOD detection solutions are mainly driven by small datasets, with low resolution and very few class labels (e.g., CIFAR). As a result, OOD detection for large-scale image classification tasks remains largely unexplored. In this paper, we bridge this critical gap by proposing a group-based OOD detection framework, along with a novel OOD scoring function termed MOS. Our key idea is to decompose the large semantic space into smaller groups with similar concepts, which allows simplifying the decision boundaries between in- vs. out-of-distribution data for effective OOD detection. Our method scales substantially better for high-dimensional class space than previous approaches. We evaluate models trained on ImageNet against four carefully curated OOD datasets, spanning diverse semantics. MOS establishes state-of-the-art performance, reducing the average FPR95 by 14.33% while achieving 6x speedup in inference compared to the previous best method.

CVPR 2021, Oral Presentation
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MOOD: Multi-level Out-of-distribution Detection

Ziqian Lin*, Sreya Dutta Roy* and Yixuan Li

(* indicates equal contributions)

While improved OOD detection methods have emerged, they often rely on the final layer outputs and require a full feedforward pass for any given input. In this paper, we propose a novel framework, multi-level out-of-distribution detection MOOD, which is the first to exploit intermediate classifier outputs for dynamic and efficient OOD inference. We explore and establish a direct relationship between the OOD data complexity and optimal exit level, and show that easy OOD examples can be effectively detected early without propagating to deeper layers. We extensively evaluate MOOD across 10 OOD datasets spanning a wide range of complexities. Experiments demonstrate that MOOD achieves up to 71.05% computational reduction in inference, while maintaining competitive OOD detection performance.

CVPR 2021
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Energy-based Out-of-distribution Detection

Weitang Liu, Xiaoyun Wang, John Owens and Yixuan Li

Determining whether inputs are out-of-distribution (OOD) is an essential building block for safely deploying machine learning models in the open world. However, previous methods relying on the softmax confidence score suffer from overconfident posterior distributions for OOD data. We propose a unified framework for OOD detection that uses an energy score, which can better distinguish in- and out-of-distribution samples than the traditional approach using the softmax scores. Within this framework, energy can be flexibly used as a scoring function for any pre-trained neural classifier as well as a trainable cost function to shape the energy surface explicitly for OOD detection. On a CIFAR-10 pre-trained WideResNet, using the energy score reduces the average FPR (at TPR 95%) by 18.03% compared to the softmax confidence score.

NeurIPS 2020
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Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks

Shiyu Liang, Yixuan Li and R. Srikant

Modern neural networks are known to generalize well when the training and testing data are sampled from the same distribution. However, when deploying neural networks in real-world applications, there is often very little control over the testing data distribution. For example, a neural network trained for classifying handwritten digits can assign high confidence labels to images of animal. This has raised great concerns in AI Safety, in particular how can classifiers obtain awareness of uncertainty when shown new kinds of inputs, i.e., out-of-distribution examples. We propose ODIN for detecting out-of-distribution images, which can be applied on any pre-trained neural networks. ODIN reduces the false positive rate from the baseline approach 34.7% to 4.3% on the DenseNet (applied to CIFAR-10) when the true positive rate is 95%.

ICLR 2018
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Sharon Yixuan Li

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