In this brief paper, we present a naive aggregation algorithm for a typical learning problem with expert advice setting, in which the task of improving generalization, i.e., model validation, is embedded in the learning process as a sequential decision-making problem. In particular, we consider a class of learning problem of point estimations for modeling high-dimensional nonlinear functions, where a group of experts update their parameter estimates using the discrete-time version of gradient systems, with small additive noise term, guided by the corresponding subsample datasets obtained from the original dataset. Here, our main objective is to provide conditions under which such an algorithm will sequentially determine a set of mixing distribution strategies used for aggregating the experts' estimates that ultimately leading to an optimal parameter estimate, i.e., as a consensus solution for all experts, which is better than any individual expert's estimate in terms of improved generalization or learning performances. Finally, as part of this work, we present some numerical results for a typical case of nonlinear regression problem.

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Origins With Cush Jumbo Widely available, episodes weekly Cush Jumbo is always good fun when doing press interviews for her work (The Good Wife, Criminal Record, Hamlet) – and she's just as great now the tables are turned in her first podcast. She speaks to stars about their origin stories, including Kate Nash, Harlan Coben, David Schwimmer and, in episode one, Anna Wintour, who says she hates people who waffle and recalls getting fired from Harper's Bazaar because she couldn't pin a dress. Rebel Spirit Widely available, episodes weekly Comedian Akilah Hughes gives her serious mission a light touch as she returns to her Kentucky home town to try to change her high school's racist mascot from a Confederate general to a biscuit. Can she drag the school into the modern age – and what will the change mean to her and other pupils? Sara & Cariad's Weirdos Book Club Widely available, episodes weekly Sara Pascoe and Cariad Lloyd go beyond the usual selections with season two of their book club for people who don't want to discuss reading over cheese and wine.

The development of Large Language Models (LLMs) has revolutionized Q&A across various industries, including the database domain. However, there is still a lack of a comprehensive benchmark to evaluate the capabilities of different LLMs and their modular components in database Q&A. To this end, we introduce DQA, the first comprehensive database Q&A benchmark. DQA features an innovative LLM-based method for automating the generation, cleaning, and rewriting of database Q&A, resulting in over 240,000 Q&A pairs in English and Chinese. These Q&A pairs cover nearly all aspects of database knowledge, including database manuals, database blogs, and database tools. This inclusion allows for additional assessment of LLMs' Retrieval-Augmented Generation (RAG) and Tool Invocation Generation (TIG) capabilities in the database Q&A task. Furthermore, we propose a comprehensive LLM-based database Q&A testbed on DQA. This testbed is highly modular and scalable, with both basic and advanced components like Question Classification Routing (QCR), RAG, TIG, and Prompt Template Engineering (PTE). Besides, DQA provides a complete evaluation pipeline, featuring diverse metrics and a standardized evaluation process to ensure comprehensiveness, accuracy, and fairness. We use DQA to evaluate the database Q&A capabilities under the proposed testbed comprehensively. The evaluation reveals findings like (i) the strengths and limitations of nine different LLM-based Q&A bots and (ii) the performance impact and potential improvements of various service components (e.g., QCR, RAG, TIG). We hope our benchmark and findings will better guide the future development of LLM-based database Q&A research.

Diffusion models (DMs) have demonstrated exceptional generative capabilities across various areas, while they are hindered by slow inference speeds and high computational demands during deployment. The most common way to accelerate DMs involves reducing the number of denoising steps during generation, achieved through faster sampling solvers or knowledge distillation (KD). In contrast to prior approaches, we propose a novel method that transfers the capability of large pretrained DMs to faster architectures. Specifically, we employ KD in a distinct manner to compress DMs by distilling their generative ability into more rapid variants. Furthermore, considering that the source data is either unaccessible or too enormous to store for current generative models, we introduce a new paradigm for their distillation without source data, termed Data-Free Knowledge Distillation for Diffusion Models (DKDM). Generally, our established DKDM framework comprises two main components: 1) a DKDM objective that uses synthetic denoising data produced by pretrained DMs to optimize faster DMs without source data, and 2) a dynamic iterative distillation method that flexibly organizes the synthesis of denoising data, preventing it from slowing down the optimization process as the generation is slow. To our knowledge, this is the first attempt at using KD to distill DMs into any architecture in a data-free manner. Importantly, our DKDM is orthogonal to most existing acceleration methods, such as denoising step reduction, quantization and pruning. Experiments show that our DKDM is capable of deriving 2x faster DMs with performance remaining on par with the baseline. Notably, our DKDM enables pretrained DMs to function as "datasets" for training new DMs.

In this work, we address the challenging problem of long-horizon goal-reaching policy learning from non-expert, action-free observation data. Unlike fully labeled expert data, our data is more accessible and avoids the costly process of action labeling. Additionally, compared to online learning, which often involves aimless exploration, our data provides useful guidance for more efficient exploration. To achieve our goal, we propose a novel subgoal guidance learning strategy. The motivation behind this strategy is that long-horizon goals offer limited guidance for efficient exploration and accurate state transition. We develop a diffusion strategy-based high-level policy to generate reasonable subgoals as waypoints, preferring states that more easily lead to the final goal. Additionally, we learn state-goal value functions to encourage efficient subgoal reaching. These two components naturally integrate into the off-policy actor-critic framework, enabling efficient goal attainment through informative exploration. We evaluate our method on complex robotic navigation and manipulation tasks, demonstrating a significant performance advantage over existing methods. Our ablation study further shows that our method is robust to observation data with various corruptions.

We introduce a general iterative procedure called risk-based calibration (RC) designed to minimize the empirical risk under the 0-1 loss (empirical error) for probabilistic classifiers. These classifiers are based on modeling probability distributions, including those constructed from the joint distribution (generative) and those based on the class conditional distribution (conditional). RC can be particularized to any probabilistic classifier provided a specific learning algorithm that computes the classifier's parameters in closed form using data statistics. RC reinforces the statistics aligned with the true class while penalizing those associated with other classes, guided by the 0-1 loss. The proposed method has been empirically tested on 30 datasets using na\"ive Bayes, quadratic discriminant analysis, and logistic regression classifiers. RC improves the empirical error of the original closed-form learning algorithms and, more notably, consistently outperforms the gradient descent approach with the three classifiers.

We propose an agent architecture that automates parts of the common reinforcement learning experiment workflow, to enable automated mastery of control domains for embodied agents. To do so, it leverages a VLM to perform some of the capabilities normally required of a human experimenter, including the monitoring and analysis of experiment progress, the proposition of new tasks based on past successes and failures of the agent, decomposing tasks into a sequence of subtasks (skills), and retrieval of the skill to execute - enabling our system to build automated curricula for learning. We believe this is one of the first proposals for a system that leverages a VLM throughout the full experiment cycle of reinforcement learning. We provide a first prototype of this system, and examine the feasibility of current models and techniques for the desired level of automation. For this, we use a standard Gemini model, without additional fine-tuning, to provide a curriculum of skills to a language-conditioned Actor-Critic algorithm, in order to steer data collection so as to aid learning new skills. Data collected in this way is shown to be useful for learning and iteratively improving control policies in a robotics domain. Additional examination of the ability of the system to build a growing library of skills, and to judge the progress of the training of those skills, also shows promising results, suggesting that the proposed architecture provides a potential recipe for fully automated mastery of tasks and domains for embodied agents.

In this work we explore parsimonious manifold learning techniques, specifically for Diffusion-maps. We demonstrate an algorithm and it's implementation with computational complexity (in both time and memory) of $\sim N$, with $N$ representing the number-of-samples. These techniques are essential for large-scale unsupervised learning tasks without any prior assumptions, due to sampling theorem limitations.

Logit-based knowledge distillation (KD) for classification is cost-efficient compared to feature-based KD but often subject to inferior performance. Recently, it was shown that the performance of logit-based KD can be improved by effectively delivering the probability distribution for the non-target classes from the teacher model, which is known as `implicit (dark) knowledge', to the student model. Through gradient analysis, we first show that this actually has an effect of adaptively controlling the learning of implicit knowledge. Then, we propose a new loss that enables the student to learn explicit knowledge (i.e., the teacher's confidence about the target class) along with implicit knowledge in an adaptive manner. Furthermore, we propose to separate the classification and distillation tasks for effective distillation and inter-class relationship modeling. Experimental results demonstrate that the proposed method, called adaptive explicit knowledge transfer (AEKT) method, achieves improved performance compared to the state-of-the-art KD methods on the CIFAR-100 and ImageNet datasets.