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Chacha Chen

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4 papers

2 author rows

ICLR Conference 2023 Conference Paper

Learning Human-Compatible Representations for Case-Based Decision Support

Han Liu
Yizhou Tian
Chacha Chen
Shi Feng 0005
Yuxin Chen 0001
Chenhao Tan

Algorithmic case-based decision support provides examples to help human make sense of predicted labels and aid human in decision-making tasks. Despite the promising performance of supervised learning, representations learned by supervised models may not align well with human intuitions: what models consider as similar examples can be perceived as distinct by humans. As a result, they have limited effectiveness in case-based decision support. In this work, we incorporate ideas from metric learning with supervised learning to examine the importance of alignment for effective decision support. In addition to instance-level labels, we use human-provided triplet judgments to learn human-compatible decision-focused representations. Using both synthetic data and human subject experiments in multiple classification tasks, we demonstrate that such representation is better aligned with human perception than representation solely optimized for classification. Human-compatible representations identify nearest neighbors that are perceived as more similar by humans and allow humans to make more accurate predictions, leading to substantial improvements in human decision accuracies (17.8% in butterfly vs. moth classification and 13.2% in pneumonia classification).

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TMLR Journal 2023 Journal Article

Machine Explanations and Human Understanding

Chacha Chen
Shi Feng
Amit Sharma
Chenhao Tan

Explanations are hypothesized to improve human understanding of machine learning models and achieve a variety of desirable outcomes, ranging from model debugging to enhancing human decision making. However, empirical studies have found mixed and even negative results. An open question, therefore, is under what conditions explanations can improve human understanding and in what way. To address this question, we first identify three core concepts that cover most existing quantitative measures of understanding: task decision boundary, model decision boundary, and model error. Using adapted causal diagrams, we provide a formal characterization of the relationship between these concepts and human approximations (i.e., understanding) of them. The relationship varies by the level of human intuition in different task types, such as emulation and discovery, which are often ignored when building or evaluating explanation methods. Our key result is that human intuitions are necessary for generating and evaluating machine explanations in human-AI decision making: without assumptions about human intuitions, explanations may improve human understanding of model decision boundary, but cannot improve human understanding of task decision boundary or model error. To validate our theoretical claims, we conduct human subject studies to show the importance of human intuitions. Together with our theoretical contributions, we provide a new paradigm for designing behavioral studies towards a rigorous view of the role of machine explanations across different tasks of human-AI decision making.

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IJCAI Conference 2021 Conference Paper

Knowledge-based Residual Learning

Guanjie Zheng
Chang Liu
Hua Wei
Porter Jenkins
Chacha Chen
Tao Wen
Zhenhui Li

Small data has been a barrier for many machine learning tasks, especially when applied in scientific domains. Fortunately, we can utilize domain knowledge to make up the lack of data. Hence, in this paper, we propose a hybrid model KRL that treats domain knowledge model as a weak learner and uses another neural net model to boost it. We prove that KRL is guaranteed to improve over pure domain knowledge model and pure neural net model under certain loss functions. Extensive experiments have shown the superior performance of KRL over baselines. In addition, several case studies have explained how the domain knowledge can assist the prediction.

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AAAI Conference 2020 Conference Paper

Toward A Thousand Lights: Decentralized Deep Reinforcement Learning for Large-Scale Traffic Signal Control

Chacha Chen
Hua Wei
Nan Xu
Guanjie Zheng
Ming Yang
Yuanhao Xiong
Kai Xu
Zhenhui Li

Trafﬁc congestion plagues cities around the world. Recent years have witnessed an unprecedented trend in applying reinforcement learning for trafﬁc signal control. However, the primary challenge is to control and coordinate trafﬁc lights in large-scale urban networks. No one has ever tested RL models on a network of more than a thousand trafﬁc lights. In this paper, we tackle the problem of multi-intersection traf- ﬁc signal control, especially for large-scale networks, based on RL techniques and transportation theories. This problem is quite difﬁcult because there are challenges such as scalability, signal coordination, data feasibility, etc. To address these challenges, we (1) design our RL agents utilizing ‘pressure’ concept to achieve signal coordination in region-level; (2) show that implicit coordination could be achieved by individual control agents with well-crafted reward design thus reducing the dimensionality; and (3) conduct extensive experiments on multiple scenarios, including a real-world scenario with 2510 trafﬁc lights in Manhattan, New York City 1 2.

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