Mostofa Akbar Department of CSE Bangladesh University of Engineering & T echnology Dhaka, Bangladesh mostofa@cse.buet.ac.bd Abstract --Emotion artificial intelligence is a field of study that focuses on figuring out how to recognize emotions, especially in the area of text mining. T oday is the age of social media which has opened a door for us to share our individual expressions, emotions, and perspectives on any event. We can analyze sentiment on social media posts to detect positive, negative, or emotional behavior toward society. One of the key challenges in sentiment analysis is to identify depressed text from social media text that is a root cause of mental ill-health. Furthermore, depression leads to severe impairment in day-to-day living and is a major source of suicide incidents. In this paper, we apply natural language processing techniques on Facebook texts for conducting emotion analysis focusing on depression using multiple machine learning algorithms. Preprocessing steps like stemming, stop word removal, etc. are used to clean the collected data, and feature extraction techniques like stylometric feature, TF-IDF, word embedding, etc. are applied to the collected dataset which consists of 983 texts collected from social media posts. In the process of class prediction, LSTM, GRU, support vector machine, and Naive-Bayes classifiers have been used. We have presented the results using the primary classification metrics including F1-score, and accuracy. This work focuses on depression detection from social media posts to help psychologists to analyze sentiment from shared posts which may reduce the undesirable behaviors of depressed individuals through diagnosis and treatment. I NTRODUCTION Text is the most important means of communication in today's world. Popular online social networking sites such as Facebook, Twitter, MySpace, etc. are mainly text-based. The rapid growth of Social Media has created enough opportunities to share information across time and space. Users are now comfortable contributing more to the content of social media websites and posting their own material. The emergence of internet-based media sources has resulted in the availability of substantial user data for the emotional analysis of text and images.

Explainable Artificial Intelligence (XAI) addresses the growing need for transparency and interpretability in AI systems, enabling trust and accountability in decision-making processes. This book offers a comprehensive guide to XAI, bridging foundational concepts with advanced methodologies. It explores interpretability in traditional models such as Decision Trees, Linear Regression, and Support Vector Machines, alongside the challenges of explaining deep learning architectures like CNNs, RNNs, and Large Language Models (LLMs), including BERT, GPT, and T5. The book presents practical techniques such as SHAP, LIME, Grad-CAM, counterfactual explanations, and causal inference, supported by Python code examples for real-world applications. Case studies illustrate XAI's role in healthcare, finance, and policymaking, demonstrating its impact on fairness and decision support. The book also covers evaluation metrics for explanation quality, an overview of cutting-edge XAI tools and frameworks, and emerging research directions, such as interpretability in federated learning and ethical AI considerations. Designed for a broad audience, this resource equips readers with the theoretical insights and practical skills needed to master XAI. Hands-on examples and additional resources are available at the companion GitHub repository: https://github.com/Echoslayer/XAI_From_Classical_Models_to_LLMs.

Estimating the uncertainty of responses of Large Language Models~(LLMs) remains a critical challenge. While recent Bayesian methods have demonstrated effectiveness in quantifying uncertainty through low-rank weight updates, they typically require complex fine-tuning or post-training procedures. In this paper, we propose Training-Free Bayesianization~(TFB), a novel framework that transforms existing off-the-shelf trained LoRA adapters into Bayesian ones without additional training. TFB systematically searches for the maximally acceptable level of variance in the weight posterior, constrained within a family of low-rank isotropic Gaussian distributions. We theoretically demonstrate that under mild conditions, this search process is equivalent to variational inference for the weights. Through comprehensive experiments, we show that TFB achieves superior uncertainty estimation and generalization compared to existing methods while eliminating the need for complex training procedures. Code will be available at https://github.com/Wang-ML-Lab/bayesian-peft.

Channel estimation is a fundamental task in communication systems and is critical for effective demodulation. While most works deal with a simple scenario where the measurements are corrupted by the additive white Gaussian noise (AWGN), this work addresses the more challenging scenario where both AWGN and structured interference coexist. Such conditions arise, for example, when a sonar/radar transmitter and a communication receiver operate simultaneously within the same bandwidth. To ensure accurate channel estimation in these scenarios, the sparsity of the channel in the delay domain and the complicate structure of the interference are jointly exploited. Firstly, the score of the structured interference is learned via a neural network based on the diffusion model (DM), while the channel prior is modeled as a Gaussian distribution, with its variance controlling channel sparsity, similar to the setup of the sparse Bayesian learning (SBL). Then, two efficient posterior sampling methods are proposed to jointly estimate the sparse channel and the interference. Nuisance parameters, such as the variance of the prior are estimated via the expectation maximization (EM) algorithm. The proposed method is termed as DM based SBL (DM-SBL). Numerical simulations demonstrate that DM-SBL significantly outperforms conventional approaches that deal with the AWGN scenario, particularly under low signal-to-interference ratio (SIR) conditions. Beyond channel estimation, DM-SBL also shows promise for addressing other linear inverse problems involving structured interference.

Computer simulations have long presented the exciting possibility of scientific insight into complex real-world processes. Despite the power of modern computing, however, it remains challenging to systematically perform inference under simulation models. This has led to the rise of simulation-based inference (SBI), a class of machine learning-enabled techniques for approaching inverse problems with stochastic simulators. Many such methods, however, require large numbers of simulation samples and face difficulty scaling to high-dimensional settings, often making inference prohibitive under resource-intensive simulators. To mitigate these drawbacks, we introduce active sequential neural posterior estimation (ASNPE). ASNPE brings an active learning scheme into the inference loop to estimate the utility of simulation parameter candidates to the underlying probabilistic model. The proposed acquisition scheme is easily integrated into existing posterior estimation pipelines, allowing for improved sample efficiency with low computational overhead. We further demonstrate the effectiveness of the proposed method in the travel demand calibration setting, a high-dimensional inverse problem commonly requiring computationally expensive traffic simulators. Our method outperforms well-tuned benchmarks and state-of-the-art posterior estimation methods on a large-scale real-world traffic network, as well as demonstrates a performance advantage over non-active counterparts on a suite of SBI benchmark environments.

Differential privacy is the dominant standard for formal and quantifiable privacy and has been used in major deployments that impact millions of people. Many differentially private algorithms for query release and synthetic data contain steps that reconstruct answers to queries from answers to other queries that have been measured privately. Reconstruction is an important subproblem for such mechanisms to economize the privacy budget, minimize error on reconstructed answers, and allow for scalability to high-dimensional datasets. In this paper, we introduce a principled and efficient postprocessing method ReM (Residuals-to-Marginals) for reconstructing answers to marginal queries. Our method builds on recent work on efficient mechanisms for marginal query release, based on making measurements using a residual query basis that admits efficient pseudoinversion, which is an important primitive used in reconstruction. An extension GReM-LNN (Gaussian Residuals-to-Marginals with Local Non-negativity) reconstructs marginals under Gaussian noise satisfying consistency and non-negativity, which often reduces error on reconstructed answers. We demonstrate the utility of ReM and GReM-LNN by applying them to improve existing private query answering mechanisms.

Augmenting a smooth cost function with an $\ell_1$ penalty allows analysts to efficiently conduct estimation and variable selection simultaneously in sophisticated models and can be efficiently implemented using proximal gradient methods. However, one drawback of the $\ell_1$ penalty is bias: nonzero parameters are underestimated in magnitude, motivating techniques such as the Adaptive Lasso which endow each parameter with its own penalty coefficient. But it's not clear how these parameter-specific penalties should be set in complex models. In this article, we study the approach of treating the penalty coefficients as additional decision variables to be learned in a \textit{Maximum a Posteriori} manner, developing a proximal gradient approach to joint optimization of these together with the parameters of any differentiable cost function. Beyond reducing bias in estimates, this procedure can also encourage arbitrary sparsity structure via a prior on the penalty coefficients. We compare our method to implementations of specific sparsity structures for non-Gaussian regression on synthetic and real datasets, finding our more general method to be competitive in terms of both speed and accuracy. We then consider nonlinear models for two case studies: COVID-19 vaccination behavior and international refugee movement, highlighting the applicability of this approach to complex problems and intricate sparsity structures.

Inspired by the two-way fixed effects regression model widely used in the panel data literature, we propose a two-way unmeasured confounding assumption to model the system dynamics in causal reinforcement learning and develop a two-way deconfounder algorithm that devises a neural tensor network to simultaneously learn both the unmeasured confounders and the system dynamics, based on which a model-based estimator can be constructed for consistent policy value estimation. We illustrate the effectiveness of the proposed estimator through theoretical results and numerical experiments.

As natural disasters become increasingly frequent, the need for efficient and equitable evacuation planning has become more critical. This paper proposes a data-driven, reinforcement learning-based framework to optimize bus-based evacuations with an emphasis on improving both efficiency and equity. We model the evacuation problem as a Markov Decision Process solved by reinforcement learning, using real-time transit data from General Transit Feed Specification and transportation networks extracted from OpenStreetMap. The reinforcement learning agent dynamically reroutes buses from their scheduled location to minimize total passengers' evacuation time while prioritizing equity-priority communities. Simulations on the San Francisco Bay Area transportation network indicate that the proposed framework achieves significant improvements in both evacuation efficiency and equitable service distribution compared to traditional rule-based and random strategies. These results highlight the potential of reinforcement learning to enhance system performance and urban resilience during emergency evacuations, offering a scalable solution for real-world applications in intelligent transportation systems.

This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement learning and sequential decision making, covering value-based RL, policy-gradient methods, model-based methods, and various other topics (including a very brief discussion of RL+LLMs).