Human-in-the-loop methods leverage human feedback to enhance machine learning and AI. Manual review of outputs can correct errors, identify model weaknesses, or expand labels to broaden model capabilities. Feedback collection methods range from simple flagging of outputs as correct or incorrect to more complex feature-level adjustments or natural language interpretations. This article presents a user study evaluating changes in user performance over time and explores the tradeoff between feedback quality and human effort. We compare four interactive input methods for reviewing and correcting outcomes in object detection and activity recognition in videos. Our findings indicate that while some complex input methods, such as free-text, require more time, the quality and impact of their feedback on model accuracy often surpass those of simpler methods that require less effort. However, more effort does not always lead to better-quality feedback, especially when aiming to improve the model. Our VLM experiments show that the most accurate models were trained using detailed natural language feedback or precise word-level corrections, while simple yes/no judgments also led to solid performance at a much lower annotation cost.

Our paper builds on the recent trend of using neural networks trained with self-supervised or supervised learning to solve the Most Probable Explanation (MPE) task in discrete graphical models. At inference time, these networks take an evidence assignment as input and generate the most likely assignment for the remaining variables via a single forward pass. We address two key limitations of existing approaches: (1) the inability to fully exploit the graphical model’s structure and parameters, and (2) the suboptimal discretization of continuous neural network outputs. Our approach embeds model structure and parameters into a more expressive feature representation, significantly improving performance. Existing methods rely on standard thresholding, which often yields suboptimal results due to the non-convexity of the loss function. We introduce two methods to overcome discretization challenges: (1) an external oracle-based approach that infers uncertain variables using additional evidence from confidently predicted ones, and (2) a technique that identifies and selects the highest-scoring discrete solutions near the continuous output. Experimental results on various probabilistic models demonstrate the effectiveness and scalability of our approach, highlighting its practical impact.

Influence Maximization aims to select a subset of elements in a social network to maximize information spread under a diffusion model. While existing work primarily focuses on selecting influential nodes, these approaches assume unrestricted message propagation-an assumption that fails in closed social networks, where content visibility is constrained and node-level activations may be infeasible. Motivated by the growing adoption of privacy-focused platforms such as Signal, Discord, Instagram, and Slack, our work addresses the following fundamental question: How can we learn effective edge activation strategies for influence maximization in closed networks? To answer this question we introduce Reinforcement Learning for Link Activation (RELINK), the first DRL framework for edge-level influence maximization in privacy-constrained networks. It models edge selection as a Markov Decision Process, where the agent learns to activate edges under budget constraints. Unlike prior node-based DRL methods, RELINK uses an edge-centric Q-learning approach that accounts for structural constraints and constrained information propagation. Our framework combines a rich node embedding pipeline with an edge-aware aggregation module. The agent is trained using an n-step Double DQN objective, guided by dense reward signals that capture marginal gains in influence spread. Extensive experiments on real-world networks show that RELINK consistently outperforms existing edge-based methods, achieving up to 15% higher influence spread and improved scalability across diverse settings.

We introduce learning to condition (L2C), a scalable, data-driven framework for accelerating Most Probable Explanation (MPE) inference in Probabilistic Graphical Models (PGMs)—a fundamentally intractable problem. L2C trains a neural network to score variable-value assignments based on their utility for conditioning, given observed evidence. To facilitate supervised learning, we develop a scalable data generation pipeline that extracts training signals from the search traces of existing MPE solvers. The trained network serves as a heuristic that integrates with search algorithms, acting as a conditioning strategy prior to exact inference or as a branching and node selection policy within branch-and-bound solvers. We evaluate L2C on challenging MPE queries involving high-treewidth PGMs. Experiments show that our learned heuristic significantly reduces the search space while maintaining or improving solution quality over state-of-the-art methods.

Probabilistic circuits (PCs) such as sum-product networks efficiently represent large multi-variate probability distributions. They are preferred in practice over other probabilistic representations, such as Bayesian and Markov networks, because PCs can solve marginal inference (MAR) tasks in time that scales linearly in the size of the network. Unfortunately, the most probable explanation (MPE) task and its generalization, the marginal maximum-a-posteriori (MMAP) inference task remain NP-hard in these models. Inspired by the recent work on using neural networks for generating near-optimal solutions to optimization problems such as integer linear programming, we propose an approach that uses neural networks to approximate MMAP inference in PCs. The key idea in our approach is to approximate the cost of an assignment to the query variables using a continuous multilinear function and then use the latter as a loss function. The two main benefits of our new method are that it is self-supervised, and after the neural network is learned, it requires only linear time to output a solution. We evaluate our new approach on several benchmark datasets and show that it outperforms three competing linear time approximations: max-product inference, max-marginal inference, and sequential estimation, which are used in practice to solve MMAP tasks in PCs.

We propose a self-supervised learning approach for solving the following constrained optimization task in log-linear models or Markov networks. Let $f$ and $g$ be two log-linear models defined over the sets $X$ and $Y$ of random variables. Given an assignment $x$ to all variables in $X$ (evidence or observations) and a real number $q$, the constrained most-probable explanation (CMPE) task seeks to find an assignment $y$ to all variables in $Y$ such that $f(x, y)$ is maximized and $g(x, y) \leq q$. In our proposed self-supervised approach, given assignments $x$ to $X$ (data), we train a deep neural network that learns to output near-optimal solutions to the CMPE problem without requiring access to any pre-computed solutions. The key idea in our approach is to use first principles and approximate inference methods for CMPE to derive novel loss functions that seek to push infeasible solutions towards feasible ones and feasible solutions towards optimal ones. We analyze the properties of our proposed method and experimentally demonstrate its efficacy on several benchmark problems.

We present a unified framework called deep dependency networks (DDNs) that combines dependency networks and deep learning architectures for multi-label classification, with a particular emphasis on image and video data. The primary advantage of dependency networks is their ease of training, in contrast to other probabilistic graphical models like Markov networks. In particular, when combined with deep learning architectures, they provide an intuitive, easy-to-use loss function for multi-label classification. A drawback of DDNs compared to Markov networks is their lack of advanced inference schemes, necessitating the use of Gibbs sampling. To address this challenge, we propose novel inference schemes based on local search and integer linear programming for computing the most likely assignment to the labels given observations. We evaluate our novel methods on three video datasets (Charades, TACoS, Wetlab) and three image datasets (MS-COCO, PASCAL VOC, NUS-WIDE), comparing their performance with (a) basic neural architectures and (b) neural architectures combined with Markov networks equipped with advanced inference and learning techniques. Our results demonstrate the superiority of our new DDN methods over the two competing approaches.

We propose a novel neural networks based approach to efficiently answer arbitrary Most Probable Explanation (MPE) queries—a well-known NP-hard task—in large probabilistic models such as Bayesian and Markov networks, probabilistic circuits, and neural auto-regressive models. By arbitrary MPE queries, we mean that there is no predefined partition of variables into evidence and non-evidence variables. The key idea is to distill all MPE queries over a given probabilistic model into a neural network and then use the latter for answering queries, eliminating the need for time-consuming inference algorithms that operate directly on the probabilistic model. We improve upon this idea by incorporating inference-time optimization with self-supervised loss to iteratively improve the solutions and employ a teacher-student framework that provides a better initial network, which in turn, helps reduce the number of inference-time optimization steps. The teacher network utilizes a self-supervised loss function optimized for getting the exact MPE solution, while the student network learns from the teacher's near-optimal outputs through supervised loss. We demonstrate the efficacy and scalability of our approach on various datasets and a broad class of probabilistic models, showcasing its practical effectiveness.

Following step-by-step procedures is an essential component of various activities carried out by individuals in their daily lives. These procedures serve as a guiding framework that helps to achieve goals efficiently, whether it is assembling furniture or preparing a recipe. However, the complexity and duration of procedural activities inherently increase the likelihood of making errors. Understanding such procedural activities from a sequence of frames is a challenging task that demands an accurate interpretation of visual information and the ability to reason about the structure of the activity. To this end, we collect a new egocentric 4D dataset, CaptainCook4D, comprising 384 recordings (94.5 hours) of people performing recipes in real kitchen environments. This dataset consists of two distinct types of activity: one in which participants adhere to the provided recipe instructions and another in which they deviate and induce errors. We provide 5.3K step annotations and 10K fine-grained action annotations and benchmark the dataset for the following tasks: error recognition, multistep localization and procedure learning.

We consider the following video activity recognition (VAR) task: given a video, infer the set of activities being performed in the video and assign each frame to an activity. Although VAR can be solved accurately using existing deep learning techniques, deep networks are neither interpretable nor explainable and as a result their use is problematic in high stakes decision-making applications (in healthcare, experimental Biology, aviation, law, etc.). In such applications, failure may lead to disastrous consequences and therefore it is necessary that the user is able to either understand the inner workings of the model or probe it to understand its reasoning patterns for a given decision. We address these limitations of deep networks by proposing a new approach that feeds the output of a deep model into a tractable, interpretable probabilistic model called a dynamic conditional cutset network that is defined over the explanatory and output variables and then performing joint inference over the combined model. The two key benefits of using cutset networks are: (a) they explicitly model the relationship between the output and explanatory variables and as a result, the combined model is likely to be more accurate than the vanilla deep model and (b) they can answer reasoning queries in polynomial time and as a result, they can derive meaningful explanations by efficiently answering explanation queries. We demonstrate the efficacy of our approach on two datasets, Textually Annotated Cooking Scenes (TACoS), and wet lab, using conventional evaluation measures such as the Jaccard Index and Hamming Loss, as well as a human-subjects study.

In this paper, a kernelized version of the random vector functional link network is proposed for multi-label classification. This classifier uses pseudoinverse to find output weights of the network. As pseudoinverse is non-iterative in nature, it requires less fine-tuning to train the network. Kernelization of RVFL makes it robust and stable as no need to tune the number of neuron in the enhancement layer. A threshold function is used with a kernelized random vector functional link network to make it suitable for multi-label learning problems. Experiments performed on three benchmark multi-label datasets bibtex, emotions, and scene shows that proposed classifier outperforms various the existing multi-label classifiers.