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Victor Li

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4 papers

1 author row

NeurIPS Conference 2025 Conference Paper

Performative Risk Control: Calibrating Models for Reliable Deployment under Performativity

Victor Li
Baiting Chen
Yuzhen Mao
Qi Lei
Zhun Deng

Calibrating blackbox machine learning models to achieve risk control is crucial to ensure reliable decision-making. A rich line of literature has been studying how to calibrate a model so that its predictions satisfy explicit finite-sample statistical guarantees under a fixed, static, and unknown data-generating distribution. However, prediction-supported decisions may influence the outcome they aim to predict, a phenomenon named performativity of predictions, which is commonly seen in social science and economics. In this paper, we introduce Performative Risk Control, a framework to calibrate models to achieve risk control under performativity with provable theoretical guarantees. Specifically, we provide an iteratively refined calibration process, where we ensure the predictions are improved and risk-controlled throughout the process. We also study different types of risk measures and choices of tail bounds. Lastly, we demonstrate the effectiveness of our framework by numerical experiments on the task of predicting credit default risk. To the best of our knowledge, this work is the first one to study statistically rigorous risk control under performativity, which will serve as an important safeguard against a wide range of strategic manipulation in decision-making processes.

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NeurIPS Conference 2023 Conference Paper

SustainGym: Reinforcement Learning Environments for Sustainable Energy Systems

Christopher Yeh
Victor Li
Rajeev Datta
Julio Arroyo
Nicolas Christianson
Chi Zhang
Yize Chen
Mohammad Mehdi Hosseini

The lack of standardized benchmarks for reinforcement learning (RL) in sustainability applications has made it difficult to both track progress on specific domains and identify bottlenecks for researchers to focus their efforts. In this paper, we present SustainGym, a suite of five environments designed to test the performance of RL algorithms on realistic sustainable energy system tasks, ranging from electric vehicle charging to carbon-aware data center job scheduling. The environments test RL algorithms under realistic distribution shifts as well as in multi-agent settings. We show that standard off-the-shelf RL algorithms leave significant room for improving performance and highlight the challenges ahead for introducing RL to real-world sustainability tasks.

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AAAI Conference 2020 Conference Paper

Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution

Yingruo Fan
Jacqueline Lam
Victor Li

The intensity estimation of facial action units (AUs) is challenging due to subtle changes in the person’s facial appearance. Previous approaches mainly rely on probabilistic models or predeﬁned rules for modeling co-occurrence relationships among AUs, leading to limited generalization. In contrast, we present a new learning framework that automatically learns the latent relationships of AUs via establishing semantic correspondences between feature maps. In the heatmap regression-based network, feature maps preserve rich semantic information associated with AU intensities and locations. Moreover, the AU co-occurring pattern can be reﬂected by activating a set of feature channels, where each channel encodes a speciﬁc visual pattern of AU. This motivates us to model the correlation among feature channels, which implicitly represents the co-occurrence relationship of AU intensity levels. Speciﬁcally, we introduce a semantic correspondence convolution (SCC) module to dynamically compute the correspondences from deep and low resolution feature maps, and thus enhancing the discriminability of features. The experimental results demonstrate the effectiveness and the superior performance of our method on two benchmark datasets.

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AAAI Conference 2018 Conference Paper

Zero-Resource Neural Machine Translation with Multi-Agent Communication Game

Yun Chen
Yang Liu
Victor Li

While end-to-end neural machine translation (NMT) has achieved notable success in the past years in translating a handful of resource-rich language pairs, it still suffers from the data scarcity problem for low-resource language pairs and domains. To tackle this problem, we propose an interactive multimodal framework for zero-resource neural machine translation. Instead of being passively exposed to large amounts of parallel corpora, our learners (implemented as encoder-decoder architecture) engage in cooperative image description games, and thus develop their own image captioning or neural machine translation model from the need to communicate in order to succeed at the game. Experimental results on the IAPR-TC12 and Multi30K datasets show that the proposed learning mechanism signiﬁcantly improves over the state-of-the-art methods.

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