Canada

Unlearnable example attacks are data poisoning attacks aiming to degrade the clean test accuracy of deep learning by adding imperceptible perturbations to the training samples, which can be formulated as a bi-level optimization problem. However, directly solving this optimization problem is intractable for deep neural networks. In this paper, we investigate unlearnable example attacks from a game-theoretic perspective, by formulating the attack as a nonzero sum Stackelberg game. First, the existence of game equilibria is proved under the normal setting and the adversarial training setting.  It is shown that the game equilibrium gives the most powerful poison attack in that the victim has the lowest test accuracy among all networks within the same hypothesis space when certain loss functions are used. Second, we propose a novel attack method, called the Game Unlearnable Example (GUE), which has three main gradients.  (1) The poisons are obtained by directly solving the equilibrium of the Stackelberg game with a first-order algorithm.  (2) We employ an autoencoder-like generative network model as the poison attacker. (3) A novel payoff function is introduced to evaluate the performance of the poison. Comprehensive experiments demonstrate that GUE can effectively poison the model in various scenarios.  Furthermore, the GUE still works by using a relatively small percentage of the training data to train the generator, and the poison generator can generalize to unseen data well. Our implementation code can be found at https://github.com/hong-xian/gue.

AAAI 2024

Game-Theoretic Unlearnable Example Generator | VIDEO

robustness & trustworthiness; game theory; privacy and secondary

safety

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.<br><br><br><br>

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

technical paper

Detecting out-of-distribution (OOD) inputs is critical for reliable machine learning, but deep neural networks often make overconfident predictions, even for OOD inputs that deviate from the distribution of training data. Prior methods relied on the widely used softmax cross-entropy (CE) loss that is adequate for classifying in-distribution (ID) samples but not optimally designed for OOD detection. To address this issue, we propose CASE, a simple and effective OOD detection method by explicitly improving intra-class Compactness And inter-class Separability of feature Embeddings. To enhance the separation between ID and OOD samples, CASE uses a dual-loss framework, which includes a separability loss that maximizes the inter-class Euclidean distance to promote separability among different class centers, along with a compactness loss that minimizes the intra-class Euclidean distance to encourage samples to be close to their class centers. In particular, the class centers are defined as a free optimization parameter of the model and updated by gradient descent, which is simple and further enhances the OOD detection performance. Extensive experiments demonstrate the superiority of CASE, which reduces the average FPR95 by 37.11% and improves the average AUROC by 15.89% compared to the baseline method using a softmax confidence score on the more challenging CIFAR-100 model.

CASE: Exploiting Intra-class Compactness and Inter-class Separability of Feature Embeddings for Out-of-Distribution Detection

The recent advancements in Deep Reinforcement Learning (DRL) have significantly enhanced the performance of adaptive Traffic Signal Control (TSC). However, DRL policies are typically represented by neural networks, which are over-parameterized black-box models. As a result, the learned policies often lack interpretability, and cannot be deployed directly in the real-world edge hardware due to resource constraints. In addition, the DRL methods often exhibit limited generalization performance, struggling to generalize the learned policy to other geographical regions. These factors limit the practical application of learning-based approaches. To address these issues, we suggest the use of an inherently interpretable program for representing the control policy. We present a new approach, Programmatic Interpretable reinforcement learning for traffic signal control (π-light), designed to autonomously discover non-differentiable programs. Specifically, we define a Domain Specific Language (DSL) and transformation rules for constructing programs, and utilize Monte Carlo Tree Search (MCTS) to find the optimal program in a discrete space. Extensive experiments demonstrate that our method consistently outperforms baseline approaches. Moreover, π-Light exhibits superior generalization capabilities compared to DRL, enabling training and evaluation across intersections from different cities. Finally, we analyze how the learned program policies can directly deploy on edge devices with extremely limited resources.

π-Light: Programmatic Interpretable Reinforcement Learning for Resource-Limited Traffic Signal Control

Concept Bottleneck Models (CBMs), which break down the reasoning process into the input-to-concept mapping and the concept-to-label prediction, have garnered significant attention due to their remarkable interpretability achieved by the interpretable concept bottleneck. However, despite the transparency of the concept-to-label prediction, the mapping from the input to the intermediate concept remains a black box, giving rise to concerns about the trustworthiness of the learned concepts~(i.e., these concepts may be predicted based on spurious cues). The issue of concept untrustworthiness greatly hampers the interpretability of CBMs, thereby hindering their further advancement. To conduct a comprehensive analysis on this issue, in this study we establish a benchmark to assess the trustworthiness of concepts in CBMs. A pioneering metric, referred to as concept trustworthiness score, is proposed to gauge whether the concepts are derived from relevant regions. Additionally, an enhanced CBM is introduced, enabling concept predictions to be made specifically from distinct parts of the feature map, thereby facilitating the exploration of their related regions. Besides, we introduce three modules, namely the cross-layer alignment (CLA) module, the cross-image alignment (CIA) module, and the prediction alignment (PA) module, to further enhance the concept trustworthiness within the elaborated CBM. The experiments on five datasets across ten architectures demonstrate that without using any concept localization annotations during training, our model improves the concept trustworthiness by a large margin, meanwhile achieving superior accuracy to the state-of-the-arts. Our code is available at https://github.com/hqhQAQ/ProtoCBM.

On the Concept Trustworthiness in Concept Bottleneck Models

Safe Reinforcement Learning (SRL) algorithms aim to learn a policy that maximizes the reward while satisfying the safety constraints. One of the challenges in SRL is that it is often difficult to balance the two objectives of reward maximization and safety constraint satisfaction. Existing algorithms utilize constraint optimization techniques like penalty-based, barrier penalty-based, and Lagrangian-based dual or primal policy optimizations methods. However, they suffer from training oscillations and approximation errors, which impact the overall learning objectives.

This paper proposes the Permeable Penalty Barrier-based Policy Optimization (P2BPO) algorithm that addresses this issue by allowing a small fraction of penalty beyond the penalty barrier, and a parameter is used to control this permeability. In addition, an adaptive penalty parameter is used instead of a constant one, which is initialized with a low value and increased gradually as the agent violates the safety constraints. We have also provided a theoretical proof of the proposed method's performance guarantee bound, which ensures that P2BPO can learn a policy satisfying the safety constraints with high probability while achieving a higher expected reward. Furthermore, we compare P2BPO with other SRL algorithms on various SRL tasks and demonstrate that it achieves better rewards while adhering to the constraints.

P2BPO: Permeable Penalty Barrier-Based Policy Optimization for Safe RL

Artificial Intelligence (AI) models have become an integral part of modern society, significantly improving human lives. However, ensuring the reliability and safety of these models is of paramount importance. One critical aspect is the continuous monitoring and verification of model performance to prevent any potential risks. Real-time online evaluation of AI models is necessary to maintain their effectiveness and mitigate any harm caused by performance degradation. The traditional approach to model evaluation involves supervised methods that rely on manual labeling to compare results with model predictions. Unfortunately, this method is not suitable for online model monitoring due to its inherent lag and high cost. While there have been attempts to explore free-label model evaluation, these approaches often consider only the global features of the entire dataset. Additionally, they can only perform model evaluation based on a single dimension of model confidence or features. In this paper, we propose a novel approach called Divide-and-Aggregate Learning (DAL) for unsupervised model evaluation. Our method addresses the limitations of previous approaches by dividing the output of the model into buckets, capturing local information of the distribution. We then aggregate this local information to obtain global information and further represent the relationship between the distribution and model performance. Importantly, our method can simultaneously handle the confidence distribution and feature distribution of the model output. Extensive experiments have been conducted to demonstrate the effectiveness of our DAL model. The results show that our approach outperforms previous methods on four widely used datasets. We will make our source code publicly available.

Divide-and-Aggregate Learning for Evaluating Performance on Unlabeled Data

Most existing evaluations of explainable machine learning (ML) methods rely on simplifying assumptions or proxies that do not reflect real-world use cases; the handful of more robust evaluations on real-world settings have shortcomings in their design, generally leading to overestimation of methods' real-world utility.  In this work, we seek to address this by conducting a study that evaluates post-hoc explainable ML methods in a setting consistent with the application context and provide a template for future evaluation studies. We modify and improve a prior study on e-commerce fraud detection by relaxing the original work's simplifying assumptions that departed from the deployment context. Our study finds no evidence for the utility of the tested explainable ML methods in the context, which is a drastically different conclusion from the earlier work. This highlights how seemingly trivial experimental design choices can yield misleading conclusions about method utility. In addition, our work carries lessons about the necessity of not only evaluating explainable ML methods using tasks, data, users, and metrics grounded in the intended application context but also developing methods tailored to specific applications, moving beyond general-purpose explainable ML methods.

On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods

Dear Chairs,
I am writing to express my strong interest in the AAAI
2024 New Faculty Highlights opportunity. Currently serving
as an Assistant Professor at the University of Texas at Dal-
las since August 2022, I am working on computer vision and
machine learning. My research focuses are transfer learning,
continual learning, multi-task learning, and geometric learn-
ing. I have published more than 30 papers with a total cita-
tions over 1300 (https://scholar.google.com/citations?user=
BxIXuZYAAAAJ&hl=en). Most of them are at top confer-
ences such as CVPR, ECCV, ICCV, NeurIPS, and AAAI.
In the following sections, I will provide an overview of my
research accomplishments to date and outline my future re-
search plans.

Application for New Faculty Highlights

"This Model Assignment consists of two “sister” lab exercises related to counting LEGO pieces using computer vision techniques.

In the first lab, students are tasked with using traditional computer vision techniques to count and classify LEGO pieces from a small image dataset. All images feature several LEGO pieces against a relatively simple background. Students need to apply three techniques such as image thresholding, edge detection (e.g., Canny), and rg chromaticity to segment, count and classify the pieces (by color). Students are expected to identify the most suitable technique and provide evidence to support their choice. They should also provide a report containing method descriptions, results, discussion, and conclusion.

For the second lab, students work with a larger and more complex dataset to detect and count LEGO pieces. Students need to train a convolutional neural network to identify and count LEGO pieces. They can choose any neural network architecture and framework (e.g., Inception/SSD with TensorFlow, Mask R-CNN with PyTorch, YOLO with Darknet). The performance assessment for the object detection task involves using mean average precision (mAP) with an IoU threshold of 0.5. The assignment requires a similar report structure as the first lab."

Using Computer Vision Techniques to Count LEGO Pieces

For more than 15 years, my team and I have been focused on AI for social impact, deploying end-to-end systems in areas of public health, conservation and public safety. In this talk, I will highlight the results from our deployments for social impact in public health and conservation, as well as required innovations in integrating machine learning and optimization. Within public health, I will present recent results from our work in India with the world’s two largest mobile health programs for maternal and child care that have served millions of beneficiaries. Additionally, I will highlight results from an earlier project on HIV prevention among youth experiencing homelessness in Los Angeles. Turning to conservation, I will highlight efforts for protecting endangered wildlife in national parks around the globe. To address challenges of ML+optimization common to all of these applications, we have advanced the state of the art in decision-focused learning, restless multi-armed bandits, influence maximization in social networks and green security games. In pushing this research agenda, our ultimate goal is to empower local communities and non-profits to directly benefit from advances in AI tools and techniques.

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Next from AAAI 2024

CASE: Exploiting Intra-class Compactness and Inter-class Separability of Feature Embeddings for Out-of-Distribution Detection

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Detection and Defense of Unlearnable Examples