Reinforcement learning (RL) provides an effective means to capture users' evolving preferences, leading to improved recommendation performance over time. However, existing RL approaches primarily rely on standard exploration strategies, which are less effective for a large item space with sparse reward signals given the limited interactions for most users. Therefore, they may not be able to learn the optimal policy that effectively captures user's evolving preferences and achieves the maximum expected reward over the long term. In this paper, we propose a novel evidential conservative Q-learning framework (ECQL) that learns an effective and conservative recommendation policy by integrating evidence-based uncertainty and conservative learning. ECQL conducts evidence-aware explorations to discover items that are located beyond current observations but reflect users' long-term interests. It offers an uncertainty-aware conservative view on policy evaluation to discourage deviating too much from users' current interests. Two central components of ECQL include a uniquely designed sequential state encoder and a novel conservative evidential-actor-critic (CEAC) module. The former generates the current state of the environment by aggregating historical information and a sliding window that contains the current user interactions as well as newly recommended items from RL exploration that may represent short and long-term interests respectively. The latter performs an evidence-based rating prediction by maximizing the conservative evidential Q-value and leverages an uncertainty-aware ranking score to explore the item space for a more diverse and valuable recommendation. Experiments on multiple real-world dynamic datasets demonstrate the state-of-the-art performance of ECQL and its capability to capture users' long-term interests.

Artificial intelligence (AI) is becoming increasingly prevalent in our society, leading to a growing demand for a skilled workforce in AI. Unfortunately, this demand remains unmet, particularly among smaller institutions and those serving underrepresented groups, due to resource limitations. This initiative offers two hands-on educational activities in Artificial Intelligence and Machine Learning to facilitate the integration of AI/ML concepts into foundational computing and non-computing courses. While primarily aimed at undergraduate students, the materials created can also benefit high school (grades 9–12) and graduate students in various educational settings, including traditional classrooms and outreach or after-school programs. Labs are accessible through a web browser, making them easy to adopt at all institutions, especially those with limited resources. The self-contained and hosted nature of the labs ensures that they can be adopted by institutions facing resource constraints. The complete project material is publicly available on the project website: https://all.rit.edu

Accessibility is a key aspect in ensuring the development of inclusive software. Unfortunately, research demonstrates that a large portion of software today is not created in an accessible manner. Problematically, students may not understand how to create accessible software, additionally misunderstanding the importance of creating accessible software. To fill the gap in accessibility education, we have created a comprehensive collection of labs, collectively referred to as Accessible Learning Labs (ALL). They have the primary objectives of educating participants on how to properly create accessible software, simultaneously illustrating the need to create inclusive and accessible software. In addition, the lab activities enable students to experience the implications of inaccessible software and make repairs based on their experience, making the software in the labs accessible. This will benefit many members of the software engineering community, ranging from beginning-level students to experienced practitioners who want to ensure that they are properly creating accessible and inclusive software. Complete project material is publicly available on the project website: https://all.rit.edu

Prior research on neural architecture search (NAS) for adversarial robustness has revealed that a lightweight and adversarially robust sub-network could exist in a non-robust large teacher network. Such a sub-network is generally discovered based on heuristic rules to perform neural architecture search. However, heuristic rules are inadequate to handle diverse adversarial attacks and different "teacher" network capacity. To address this key challenge, we propose Reinforced Compressive Neural Architecture Search (RC-NAS), aiming to achieve Versatile Adversarial Robustness. Specifically, we define novel task settings that compose datasets, adversarial attacks, and teacher network configuration. Given diverse tasks, we develop an innovative dual-level training paradigm that consists of a meta-training and a fine-tuning phase to effectively expose the RL agent to diverse attack scenarios (in meta-training), and make it adapt quickly to locate an optimal sub-network (in fine-tuning) for previously unseen scenarios. Experiments show that our framework could achieve adaptive compression towards different initial teacher networks, datasets, and adversarial attacks, resulting in more lightweight and adversarially robust architectures. We also provide a theoretical analysis to explain why the reinforcement learning (RL)-guided adversarial architectural search helps adversarial robustness over standard adversarial training methods.

Existing state-of-the-art dense object detection techniques tend to produce a large number of false positive detections on difficult images with complex scenes because they focus on ensuring a high recall. To improve the detection accuracy, we propose an Adaptive Important Region Selection (AIRS) framework guided by Evidential Q-learning coupled with a uniquely designed reward function. Inspired by human visual attention, our detection model conducts object search in a top-down, hierarchical fashion. It starts from the top of the hierarchy with the coarsest granularity and then identifies the potential patches likely to contain objects of interest. It then discards non-informative patches and progressively moves downward on the selected ones for a fine-grained search. The proposed evidential Q-learning systematically encodes epistemic uncertainty in its evidential-Q value to encourage the exploration of unknown patches, especially in the early phase of model training. In this way, the proposed model dynamically balances exploration-exploitation to cover both highly valuable and informative patches. Theoretical analysis and extensive experiments on multiple datasets demonstrate that our proposed framework outperforms the SOTA models.

Machine learning-driven human behavior analysis is gaining attention in behavioral/mental healthcare, due to its potential to identify behavioral patterns that cannot be recognized by traditional assessments. Real-life applications, such as digital behavioral biomarker identification, often require the discovery of complex spatiotemporal patterns in multimodal data, which is largely under-explored. To fill this gap, we propose a novel model that integrates uniquely designed Deep Temporal Sets (DTS) with Evidential Reinforced Attentions (ERA). DTS captures complex temporal relationships in the input and generates a set-based representation, while ERA captures the policy network’s uncertainty and conducts evidence-aware exploration to locate attentive regions in behavioral data. Using child-computer interaction data as a testing platform, we demonstrate the effectiveness of DTS-ERA in differentiating children with Autism Spectrum Disorder and typically developing children based on sequential multimodal visual and touch behaviors. Comparisons with baseline methods show that our model achieves superior performance and has the potential to provide objective, quantitative, and precise analysis of complex human behaviors.

The recently developed sparse network training methods, such as Lottery Ticket Hypothesis (LTH) and its variants, have shown impressive learning capacity by finding sparse sub-networks from a dense one. While these methods could largely sparsify deep networks, they generally focus more on realizing comparable accuracy to dense counterparts yet neglect network calibration. However, how to achieve calibrated network predictions lies at the core of improving model reliability, especially when it comes to addressing the overconfident issue and out-of-distribution cases. In this study, we propose a novel Distributionally Robust Optimization (DRO) framework to achieve an ensemble of lottery tickets towards calibrated network sparsification. Specifically, the proposed DRO ensemble aims to learn multiple diverse and complementary sparse sub-networks (tickets) with the guidance of uncertainty sets, which encourage tickets to gradually capture different data distributions from easy to hard and naturally complement each other. We theoretically justify the strong calibration performance by showing how the proposed robust training process guarantees to lower the confidence of incorrect predictions. Extensive experimental results on several benchmarks show that our proposed lottery ticket ensemble leads to a clear calibration improvement without sacrificing accuracy and burdening inference costs. Furthermore, experiments on OOD datasets demonstrate the robustness of our approach in the open-set environment.

Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) aims at finding a specific image from a large gallery given a query sketch. Despite the widespread applicability of FG-SBIR in many critical domains (e.g., crime activity tracking), existing approaches still suffer from a low accuracy while being sensitive to external noises such as unnecessary strokes in the sketch. The retrieval performance will further deteriorate under a more practical on-the-fly setting, where only a partially complete sketch with only a few (noisy) strokes are available to retrieve corresponding images. We propose a novel framework that leverages a uniquely designed deep reinforcement learning model that performs a dual-level exploration to deal with partial sketch training and attention region selection. By enforcing the model’s attention on the important regions of the original sketches, it remains robust to unnecessary stroke noises and improve the retrieval accuracy by a large margin. To sufficiently explore partial sketches and locate the important regions to attend, the model performs bootstrapped policy gradient for global exploration while adjusting a standard deviation term that governs a locator network for local exploration. The training process is guided by a hybrid loss that integrates a reinforcement loss and a supervised loss. A dynamic ranking reward is developed to fit the on-the-fly image retrieval process using partial sketches. The extensive experimentation performed on three public datasets shows that our proposed approach achieves the state-of-the-art performance on partial sketch based image retrieval.

Recognizing freehand sketches with high arbitrariness is such a great challenge that the automatic recognition rate has reached a ceiling in recent years. In this paper, we explicitly explore the shape properties of sketches, which has almost been neglected before in the context of deep learning, and propose a sequential dual learning strategy that combines both shape and texture features. We devise a two-stage recurrent neural network to balance these two types of features. Our architecture also considers stroke orders of sketches to reduce the intra-class variations of input features. Extensive experiments on the TU-Berlin benchmark set show that our method achieves over 90% recognition rate for the first time on this task, outperforming both humans and state-of-the-art algorithms by over 19 and 7.5 percentage points, respectively. Especially, our approach can distinguish the sketches with similar textures but different shapes more effectively than recent deep networks. Based on the proposed method, we develop an on-line sketch retrieval and imitation application to teach children or adults to draw. The application is available as Sketch.Draw.