The dame-flame Python package is the first major implementation of two algorithms, the dynamic almost matching exactly (DAME) algorithm (Dieng, Liu, Roy, Rudin, and Volfovsky 2019, published in AISTATS'19), and the fast, large-scale almost matching exactly (FLAME) algorithm (Wang, Morucci, Awan, Liu, Roy, Rudin, and Volfovsky 2019, published in JMLR'21), which provide almost exact matching of treatment and control units in discrete observational data for causal analysis. As discussed in Dieng et al. (2019), and Wang et al. (2019), the two algorithms produce high-quality interpretable matched groups, by using machine learning on a holdout training set to learn distance metrics. DAME solves an optimization problem that matches units on as many covariates as possible, prioritizing matches on important covariates. FLAME approximates the solution found by DAME via a much faster backward feature selection procedure. The DAME and FLAME algorithms are discussed in the remainder of this section. We also provide testing and installation details. In Section 2, we discuss the class structure in the dame-flame package, detail special features of dame-flame, and compare dame-flame to other matching packages. In Section 3, we offer examples and a user guide.

The widespread public deployment of large language models (LLMs) in recent months has prompted a wave of new attention and engagement from advocates, policymakers, and scholars from many fields. This attention is a timely response to the many urgent questions that this technology raises, but it can sometimes miss important considerations. This paper surveys the evidence for eight potentially surprising such points: 1. LLMs predictably get more capable with increasing investment, even without targeted innovation. 2. Many important LLM behaviors emerge unpredictably as a byproduct of increasing investment. 3. LLMs often appear to learn and use representations of the outside world. 4. There are no reliable techniques for steering the behavior of LLMs. 5. Experts are not yet able to interpret the inner workings of LLMs. 6. Human performance on a task isn't an upper bound on LLM performance. 7. LLMs need not express the values of their creators nor the values encoded in web text. 8. Brief interactions with LLMs are often misleading.

The engineering community has recently witnessed the emergence of chatbot technology with the release of OpenAI ChatGPT-4 and Google Bard. While these chatbots have been reported to perform well and even pass various standardized tests, including medical and law exams, this forum paper explores whether these chatbots can also pass the Fundamentals of Engineering (FE) and Principles and Practice of Engineering (PE) exams. A diverse range of civil and environmental engineering questions and scenarios are used to evaluate the chatbots' performance, as commonly present in the FE and PE exams. The chatbots' responses were analyzed based on their relevance, accuracy, and clarity and then compared against the recommendations of the National Council of Examiners for Engineering and Surveying (NCEES). Our report shows that ChatGPT-4 and Bard, respectively scored 70.9% and 39.2% in the FE exam and 46.2% and 41% in the PE exam. It is evident that the current version of ChatGPT-4 could potentially pass the FE exam. While future editions are much more likely to pass both exams, this study also highlights the potential of using chatbots as teaching assistants and guiding engineers.

Safety is a critical component of autonomous systems and remains a challenge for learning-based policies to be utilized in the real world. In particular, policies learned using reinforcement learning often fail to generalize to novel environments due to unsafe behavior. In this paper, we propose Sim-to-Lab-to-Real to bridge the reality gap with a probabilistically guaranteed safety-aware policy distribution. To improve safety, we apply a dual policy setup where a performance policy is trained using the cumulative task reward and a backup (safety) policy is trained by solving the Safety Bellman Equation based on Hamilton-Jacobi (HJ) reachability analysis. In Sim-to-Lab transfer, we apply a supervisory control scheme to shield unsafe actions during exploration; in Lab-to-Real transfer, we leverage the Probably Approximately Correct (PAC)-Bayes framework to provide lower bounds on the expected performance and safety of policies in unseen environments. Additionally, inheriting from the HJ reachability analysis, the bound accounts for the expectation over the worst-case safety in each environment. We empirically study the proposed framework for ego-vision navigation in two types of indoor environments with varying degrees of photorealism. We also demonstrate strong generalization performance through hardware experiments in real indoor spaces with a quadrupedal robot. See https://sites.google.com/princeton.edu/sim-to-lab-to-real for supplementary material.

In computing, the aim of personalization is to train a model that caters to a specific individual or group of people by optimizing one or more performance metrics and adhering to specific constraints. In this paper, we discuss the need for personalization in affective and personality computing (hereinafter referred to as affective computing). We present a survey of state-of-the-art approaches for personalization in affective computing. Our review spans training techniques and objectives towards the personalization of affective computing models. We group existing approaches into seven categories: (1) Target-specific Models, (2) Group-specific Models, (3) Weighting-based Approaches, (4) Fine-tuning Approaches, (5) Multitask Learning, (6) Generative-based Models, and (7) Feature Augmentation. Additionally, we provide a statistical meta-analysis of the surveyed literature, analyzing the prevalence of different affective computing tasks, interaction modes, interaction contexts, and the level of personalization among the surveyed works. Based on that, we provide a road-map for those who are interested in exploring this direction.

Translating spoken languages into Sign languages is necessary for open communication between the hearing and hearing-impaired communities. To achieve this goal, we propose the first method for animating a text written in HamNoSys, a lexical Sign language notation, into signed pose sequences. As HamNoSys is universal by design, our proposed method offers a generic solution invariant to the target Sign language. Our method gradually generates pose predictions using transformer encoders that create meaningful representations of the text and poses while considering their spatial and temporal information. We use weak supervision for the training process and show that our method succeeds in learning from partial and inaccurate data. Additionally, we offer a new distance measurement that considers missing keypoints, to measure the distance between pose sequences using DTW-MJE. We validate its correctness using AUTSL, a large-scale Sign language dataset, show that it measures the distance between pose sequences more accurately than existing measurements, and use it to assess the quality of our generated pose sequences. Code for the data pre-processing, the model, and the distance measurement is publicly released for future research.

Abstract--While developments in machine learning led to impressive performance gains on big data, many human subjects data are, in actuality, small and sparsely labeled. Existing methods applied to such data often do not easily generalize to out-of-sample subjects. Instead, models must make predictions on test data that may be drawn from a different distribution, a problem known as zero-shot learning. To address this challenge, we develop an end-to-end framework using a meta-learning approach, which enables the model to rapidly adapt to a new prediction task with limited training data for out-of-sample test data. We use three real-world small-scale human subjects datasets (two randomized control studies and one observational study), for which we predict treatment outcomes for held-out treatment groups. Our model learns the latent treatment effects of each intervention and, by design, can naturally handle multitask predictions. However, these methods have had limited success in I. Though such studies remain the gold standard large amount of labeled data yet have limited capacity for of scientific discovery [1], [3], many are small and sparsely transferring knowledge [14], [15], hindering their ability to labeled due to regulatory challenges, ethical considerations generalize to complex yet small human subjects datasets and [4], data availability (e.g., investigating rare diseases [3]), tasks [16].

Federated Learning (FL) has recently emerged as a popular framework, which allows resource-constrained discrete clients to cooperatively learn the global model under the orchestration of a central server while storing privacy-sensitive data locally. However, due to the difference in equipment and data divergence of heterogeneous clients, there will be parameter deviation between local models, resulting in a slow convergence rate and a reduction of the accuracy of the global model. The current FL algorithms use the static client learning strategy pervasively and can not adapt to the dynamic training parameters of different clients. In this paper, by considering the deviation between different local model parameters, we propose an adaptive learning rate scheme for each client based on entropy theory to alleviate the deviation between heterogeneous clients and achieve fast convergence of the global model. It's difficult to design the optimal dynamic learning rate for each client as the local information of other clients is unknown, especially during the local training epochs without communications between local clients and the central server. To enable a decentralized learning rate design for each client, we first introduce mean-field schemes to estimate the terms related to other clients' local model parameters. Then the decentralized adaptive learning rate for each client is obtained in closed form by constructing the Hamilton equation. Moreover, we prove that there exist fixed point solutions for the mean-field estimators, and an algorithm is proposed to obtain them. Finally, extensive experimental results on real datasets show that our algorithm can effectively eliminate the deviation between local model parameters compared to other recent FL algorithms.

Denoising diffusion models represent a recent emerging topic in computer vision, demonstrating remarkable results in the area of generative modeling. A diffusion model is a deep generative model that is based on two stages, a forward diffusion stage and a reverse diffusion stage. In the forward diffusion stage, the input data is gradually perturbed over several steps by adding Gaussian noise. In the reverse stage, a model is tasked at recovering the original input data by learning to gradually reverse the diffusion process, step by step. Diffusion models are widely appreciated for the quality and diversity of the generated samples, despite their known computational burdens, i.e. low speeds due to the high number of steps involved during sampling. In this survey, we provide a comprehensive review of articles on denoising diffusion models applied in vision, comprising both theoretical and practical contributions in the field. First, we identify and present three generic diffusion modeling frameworks, which are based on denoising diffusion probabilistic models, noise conditioned score networks, and stochastic differential equations. We further discuss the relations between diffusion models and other deep generative models, including variational auto-encoders, generative adversarial networks, energy-based models, autoregressive models and normalizing flows. Then, we introduce a multi-perspective categorization of diffusion models applied in computer vision. Finally, we illustrate the current limitations of diffusion models and envision some interesting directions for future research.

Artificial intelligence (AI) is becoming increasingly ubiquitous in our daily lives, from the recommendations we receive on social media to the autonomous vehicles being tested on our roads. Yet, for many people, AI remains shrouded in mystery, and the thought of interacting with it can be intimidating. However, there are several steps you can take to familiarize yourself with AI and gain a better understanding. First and foremost, it's essential to understand what AI is and how it works. At its core, AI is the use of computer algorithms to perform tasks that typically require humans, such as recognizing patterns, making decisions, and learning from experience.