Summary: This work considers learning user preferences using a bandit model. The reward is not only based on the judgement of the user, but also whether the user examined the arm. That is feedback examination * judgement In particular, if a user does not examine an arm, lack of feedback does not necessarily indicate that the user does not "like" the arm. This work uses a latent model for the (unobserved) examination of arms, and posits that the probability of positive feedback (binary) can be expressed as a product of the probability of examination (logistic) and positive feedback (logistic). The work proposes a VI approach to estimating the parameters, and then use a Thompson Sampling approach from the approximate posterior as policy. This allows them to use machinery from Russo and Van Roy to obtain regret bounds.

The paper proposes a simple technique for improved feature learning in convolutional neural networks. The technique consists of adding a "negative" virtual class to CNN training on classification tasks with the softmax loss function. The authors evaluate their approach on a range of computer vision datasets, (CIFAR10/100/100, LFW, SLLFW, CUB200, ImageNet32) and find that it outperforms simple baselines on all of them, and outperforms more complicated state-of-the-art techniques on most of them. The authors also present an analysis from a few different standpoints as to why their method is effective. Strengths: - The technique proposed by the authors is extremely simple to implement (just a one line change in existing code would suffice, as far as I can tell).

SUMMARY While I am not heavily familiar with the literature on adaptive online learning, this paper seems to be a breakthrough, offering in the form of Theorem 1 a very strong result that can be leveraged to obtain adaptive (in the model complexity sense) online learning bounds in a number of settings. The efficiency, at least in the polytime sense, of the algorithms for the various settings makes these results all the more interesting. I was very surprised by the aside'' on the 1-mixability of logistic loss and the argument for circumventing the lower bound of Hazan, Koren, and Levy in the supervised learning setting. I wish that the authors could give more detail to this observation and the consequences, as the implications are so interesting that I would be (almost) sold on acceptance from this fact alone. I found the results of this paper to be very interesting, technically strong, and important, so I would strongly recommend acceptance.

This paper describes an algorithm for optizimization of Hawkes process parameters in on-line settings, where non-parametric form of a kernel is learnt. The paper reports a gradient approach to optimization, with theoretical analysis thereof. In particular, the authors provide: a regret bound, justification for simplification steps (discretization of time and truncation of time over which previous posts influence a new post), an approach to a tractable projection of the solution (a step in the algorithm), time complexity analysis. The paper is very well written, which is very helpful given it is mathematically involved. I found it tackling an important problem (on-line learning is important for large scale datasets, and non-parametricity is a very reasonable setting when it is hard to specify a reasonable kernel form a priori).

This paper introduces a pioneering approach to artificial intelligence research, embodied in our O1 Replication Journey. In response to the announcement of OpenAI's groundbreaking O1 model, we embark on a transparent, real-time exploration to replicate its capabilities while reimagining the process of conducting and communicating AI research. Our methodology addresses critical challenges in modern AI research, including the insularity of prolonged team-based projects, delayed information sharing, and the lack of recognition for diverse contributions. By providing comprehensive, real-time documentation of our replication efforts, including both successes and failures, we aim to foster open science, accelerate collective advancement, and lay the groundwork for AI-driven scientific discovery. Our research progress report diverges significantly from traditional research papers, offering continuous updates, full process transparency, and active community engagement throughout the research journey. Technologically, we proposed the journey learning paradigm, which encourages models to learn not just shortcuts, but the complete exploration process, including trial and error, reflection, and backtracking. With only 327 training samples and without any additional tricks, journey learning outperformed conventional supervised learning by over 8\% on the MATH dataset, demonstrating its extremely powerful potential. We believe this to be the most crucial component of O1 technology that we have successfully decoded. We share valuable resources including technical hypotheses and insights, cognitive exploration maps, custom-developed tools, etc at https://github.com/GAIR-NLP/O1-Journey.

In this paper, we have gone through different AI-Based frameworks used for various educational tasks like digital customized assignment allotment and performance monitoring, identifying slow-learners and fast-learners, etc. application describes a novel invention, digital screen-integrated tables, designed specifically for educational settings. The tables feature integrated digital screens controlled by a central processing unit (CPU), enabling synchronized display of educational content such as textbooks, presentations, exam questions, and interactive learning materials. Additionally, the invention facilitates the collection of student performance data during classroom activities and assessments. The gathered data is utilized for analysis using machine learning models to identify patterns and trends in student learning behaviours. By leveraging machine learning algorithms, educators can ascertain whether a student is a fast learner or a slow learner, based on which, the teacher can allocate more resources to the slow learners. This innovative approach aims to address the evolving needs of modern classrooms by providing a dynamic and data-driven learning environment. The unique integration of digital screens into traditional classroom furniture represents a significant advancement in educational technology. This patent filing encompasses the design, functionality, and method of operation of the digital screen-integrated tables, emphasizing their innovative features and applications in educational institutions.

We conducted controlled experimental bias audits for four versions of ChatGPT, which we asked to recommend an opening offer in salary negotiations for a new hire. We submitted 98,800 prompts to each version, systematically varying the employee's gender, university, and major, and tested prompts in voice of each side of the negotiation: the employee versus employer. We find ChatGPT as a multi-model platform is not robust and consistent enough to be trusted for such a task. We observed statistically significant salary offers when varying gender for all four models, although with smaller gaps than for other attributes tested. The largest gaps were different model versions and between the employee- vs employer-voiced prompts. We also observed substantial gaps when varying university and major, but many of the biases were not consistent across model versions. We tested for fictional and fraudulent universities and found wildly inconsistent results across cases and model versions. We make broader contributions to the AI/ML fairness literature. Our scenario and our experimental design differ from mainstream AI/ML auditing efforts in key ways. Bias audits typically test discrimination for protected classes like gender, which we contrast with testing non-protected classes of university and major. Asking for negotiation advice includes how aggressive one ought to be in a negotiation relative to known empirical salary distributions and scales, which is a deeply contextual and personalized task that has no objective ground truth to validate. These results raise concerns for the specific model versions we tested and ChatGPT as a multi-model platform in continuous development. Our epistemology does not permit us to definitively certify these models as either generally biased or unbiased on the attributes we test, but our study raises matters of concern for stakeholders to further investigate.

Human learning thrives on the ability to learn from mistakes, adapt through feedback, and refine understanding-processes often missing in static machine learning models. In this work, we introduce Composite Learning Units (CLUs) designed to transform reasoners, such as Large Language Models (LLMs), into learners capable of generalized, continuous learning without conventional parameter updates while enhancing their reasoning abilities through continual interaction and feedback. CLUs are built on an architecture that allows a reasoning model to maintain and evolve a dynamic knowledge repository: a General Knowledge Space for broad, reusable insights and a Prompt-Specific Knowledge Space for task-specific learning. Through goal-driven interactions, CLUs iteratively refine these knowledge spaces, enabling the system to adapt dynamically to complex tasks, extract nuanced insights, and build upon past experiences autonomously. We demonstrate CLUs' effectiveness through a cryptographic reasoning task, where they continuously evolve their understanding through feedback to uncover hidden transformation rules. While conventional models struggle to grasp underlying logic, CLUs excel by engaging in an iterative, goal-oriented process. Specialized components-handling knowledge retrieval, prompt generation, and feedback analysis-work together within a reinforcing feedback loop. This approach allows CLUs to retain the memory of past failures and successes, adapt autonomously, and apply sophisticated reasoning effectively, continually learning from mistakes while also building on breakthroughs.

In the landscape of autonomous driving, Bird's-Eye-View (BEV) representation has recently garnered substantial academic attention, serving as a transformative framework for the fusion of multi-modal sensor inputs. This BEV paradigm effectively shifts the sensor fusion challenge from a rule-based methodology to a data-centric approach, thereby facilitating more nuanced feature extraction from an array of heterogeneous sensors. Notwithstanding its evident merits, the computational overhead associated with BEV-based techniques often mandates high-capacity hardware infrastructures, thus posing challenges for practical, real-world implementations. To mitigate this limitation, we introduce a novel content-aware multi-modal joint input pruning technique. Our method leverages BEV as a shared anchor to algorithmically identify and eliminate non-essential sensor regions prior to their introduction into the perception model's backbone. We validatethe efficacy of our approach through extensive experiments on the NuScenes dataset, demonstrating substantial computational efficiency without sacrificing perception accuracy. To the best of our knowledge, this work represents the first attempt to alleviate the computational burden from the input pruning point.

Recent advancements of large language models (LLMs) have led to claims of AI surpassing humans in natural language processing (NLP) tasks such as textual understanding and reasoning. This work investigates these assertions by introducing CAIMIRA, a novel framework rooted in item response theory (IRT) that enables quantitative assessment and comparison of problem-solving abilities of question-answering (QA) agents: humans and AI systems. Through analysis of over 300,000 responses from ~70 AI systems and 155 humans across thousands of quiz questions, CAIMIRA uncovers distinct proficiency patterns in knowledge domains and reasoning skills. Humans outperform AI systems in knowledge-grounded abductive and conceptual reasoning, while state-of-the-art LLMs like GPT-4 and LLaMA show superior performance on targeted information retrieval and fact-based reasoning, particularly when information gaps are well-defined and addressable through pattern matching or data retrieval. These findings highlight the need for future QA tasks to focus on questions that challenge not only higher-order reasoning and scientific thinking, but also demand nuanced linguistic interpretation and cross-contextual knowledge application, helping advance AI developments that better emulate or complement human cognitive abilities in real-world problem-solving.