Feature selection is of great importance in Machine Learning, where it can be used to reduce the dimensionality of classification, ranking and prediction problems. The removal of redundant and noisy features can improve both the accuracy and scalability of the trained models. However, feature selection is a computationally expensive task with a solution space that grows combinatorically. In this work, we consider in particular a quadratic feature selection problem that can be tackled with the Quantum Approximate Optimization Algorithm (QAOA), already employed in combinatorial optimization. First we represent the feature selection problem with the QUBO formulation, which is then mapped to an Ising spin Hamiltonian. Then we apply QAOA with the goal of finding the ground state of this Hamiltonian, which corresponds to the optimal selection of features. In our experiments, we consider seven different real-world datasets with dimensionality up to 21 and run QAOA on both a quantum simulator and, for small datasets, the 7-qubit IBM (ibm-perth) quantum computer. We use the set of selected features to train a classification model and evaluate its accuracy. Our analysis shows that it is possible to tackle the feature selection problem with QAOA and that currently available quantum devices can be used effectively. Future studies could test a wider range of classification models as well as improve the effectiveness of QAOA by exploring better performing optimizers for its classical step.

Fact verification has attracted a lot of research attention recently, e.g., in journalism, marketing, and policymaking, as misinformation and disinformation online can sway one's opinion and affect one's actions. While fact-checking is a hard task in general, in many cases, false statements can be easily debunked based on analytics over tables with reliable information. Hence, table-based fact verification has recently emerged as an important and growing research area. Yet, progress has been limited due to the lack of datasets that can be used to pre-train language models (LMs) to be aware of common table operations, such as aggregating a column or comparing tuples. To bridge this gap, in this paper we introduce PASTA, a novel state-of-the-art framework for table-based fact verification via pre-training with synthesized sentence-table cloze questions. In particular, we design six types of common sentence-table cloze tasks, including Filter, Aggregation, Superlative, Comparative, Ordinal, and Unique, based on which we synthesize a large corpus consisting of 1.2 million sentence-table pairs from WikiTables. PASTA uses a recent pre-trained LM, DeBERTaV3, and further pretrains it on our corpus. Our experimental results show that PASTA achieves new state-of-the-art performance on two table-based fact verification benchmarks: TabFact and SEM-TAB-FACTS. In particular, on the complex set of TabFact, which contains multiple operations, PASTA largely outperforms the previous state of the art by 4.7 points (85.6% vs. 80.9%), and the gap between PASTA and human performance on the small TabFact test set is narrowed to just 1.5 points (90.6% vs. 92.1%).

Recent works on Lottery Ticket Hypothesis have shown that pre-trained language models (PLMs) contain smaller matching subnetworks(winning tickets) which are capable of reaching accuracy comparable to the original models. However, these tickets are proved to be notrobust to adversarial examples, and even worse than their PLM counterparts. To address this problem, we propose a novel method based on learning binary weight masks to identify robust tickets hidden in the original PLMs. Since the loss is not differentiable for the binary mask, we assign the hard concrete distribution to the masks and encourage their sparsity using a smoothing approximation of L0 regularization.Furthermore, we design an adversarial loss objective to guide the search for robust tickets and ensure that the tickets perform well bothin accuracy and robustness. Experimental results show the significant improvement of the proposed method over previous work on adversarial robustness evaluation.

Recent studies on adversarial images have shown that they tend to leave the underlying low-dimensional data manifold, making them significantly more challenging for current models to make correct predictions. This so-called off-manifold conjecture has inspired a novel line of defenses against adversarial attacks on images. In this study, we find a similar phenomenon occurs in the contextualized embedding space induced by pretrained language models, in which adversarial texts tend to have their embeddings diverge from the manifold of natural ones. Based on this finding, we propose Textual Manifold-based Defense (TMD), a defense mechanism that projects text embeddings onto an approximated embedding manifold before classification. It reduces the complexity of potential adversarial examples, which ultimately enhances the robustness of the protected model. Through extensive experiments, our method consistently and significantly outperforms previous defenses under various attack settings without trading off clean accuracy. To the best of our knowledge, this is the first NLP defense that leverages the manifold structure against adversarial attacks. Our code is available at \url{https://github.com/dangne/tmd}.

The success of today's AI applications requires not only model training (Model-centric) but also data engineering (Data-centric). In data-centric AI, active learning (AL) plays a vital role, but current AL tools 1) require users to manually select AL strategies, and 2) can not perform AL tasks efficiently. To this end, this paper presents an automatic and efficient MLOps system for AL, named ALaaS (Active-Learning-as-a-Service). Specifically, 1) ALaaS implements an AL agent, including a performance predictor and a workflow controller, to decide the most suitable AL strategies given users' datasets and budgets. We call this a predictive-based successive halving early-stop (PSHEA) procedure. 2) ALaaS adopts a server-client architecture to support an AL pipeline and implements stage-level parallelism for high efficiency. Meanwhile, caching and batching techniques are employed to further accelerate the AL process. In addition to efficiency, ALaaS ensures accessibility with the help of the design philosophy of configuration-as-a-service. Extensive experiments show that ALaaS outperforms all other baselines in terms of latency and throughput. Also, guided by the AL agent, ALaaS can automatically select and run AL strategies for non-expert users under different datasets and budgets. Our code is available at \url{https://github.com/MLSysOps/Active-Learning-as-a-Service}.

Fairness has become a trending topic in natural language processing (NLP), which addresses biases targeting certain social groups such as genders and religions. However, regional bias in language models (LMs), a long-standing global discrimination problem, still remains unexplored. This paper bridges the gap by analysing the regional bias learned by the pre-trained language models that are broadly used in NLP tasks. In addition to verifying the existence of regional bias in LMs, we find that the biases on regional groups can be strongly influenced by the geographical clustering of the groups. We accordingly propose a HiErarchical Regional Bias evaluation method (HERB) utilising the information from the sub-region clusters to quantify the bias in pre-trained LMs. Experiments show that our hierarchical metric can effectively evaluate the regional bias with respect to comprehensive topics and measure the potential regional bias that can be propagated to downstream tasks. Our codes are available at https://github.com/Bernard-Yang/HERB.

State abstraction and function approximation are vital components used by reinforcement learning (RL) algorithms to efficiently solve complex control problems when exact computations are intractable due to large state and action spaces. Over the past few decades, state abstraction in RL has evolved from the use of pre-determined and problemspecific features [18, 74, 9, 69, 64, 42, 58] to the use of adaptive basis functions learnt by solving an isolated regression problem [53, 47, 39, 56], and more recently to the use of neural network-based Deep-RL algorithms that embed state abstraction in successive layers of a neural network [5, 7]. Feature abstraction results in information loss, and the resulting state features might not satisfy the controlled Markov property, even if this property is satisfied by the corresponding state [70]. One approach to counteract the loss of the Markov property is to generate the features using the history of state-action pairs, and empirical evidence suggests that using such history-based features are beneficial in practice [52]. However, a theoretical characterisation of history-based Deep-RL algorithms for fully observed Markov Decision Processes (MDPs) is largely absent form the literature.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Chief diplomats from the world's leading democracies rallied together in a joint statement condemning global adversaries like Iran and North Korea and called on Russia and China to remember their security commitments to the United Nations. After two days of meetings, officials from the Group of 7 (G7) released a lengthy statement Friday in an address to its top geopolitical challengers, warning them to adhere to international laws. United States Secretary of States Antony Blinken and Foreign Minister Yoshimasa Hayashi of Japan, right, meet for bilateral talks at the G7 Foreign Ministers' Meeting in Muenster, Germany, Friday, Nov. 4, 2022.

The University of Florida, a proponent for ethics in artificial intelligence, is part of a new global agreement with seven other worldwide universities that are committed to the development of human-centered approaches to artificial intelligence (AI) that will impact people everywhere. During the Global University Summit at Notre Dame University, Joseph Glover, UF provost and senior vice president of academic affairs, signed The Rome Call for AI Ethics on October 27 on behalf of the University of Florida and served as a panelist for the two-day summit attended by 36 universities invited from around the world. The event was held in Notre Dame, IN. The signing indicates a commitment to the principles of the Rome Call for AI Ethics: to ensure artificial intelligence serves the interests of humanity and to support regulations and principles to deliver emerging technologies that are ethically centered. UF joins a network of universities that will share best practices, tools, and educational content, as well as meet regularly to share updates and discuss innovative ideas.

The Metaverse And NFTs: If the Metaverse is the door to the unique realm of experiences, NFTs are the exclusive keys to that door. These keys are increasingly becoming inevitable for exploring the limitless territories of exciting and personalised digital experiences. With the rising popularity of NFTs, their intrinsic nature makes them the DNA certification for our society. The concept of Metaverse is one of the pillars that are leveraged by the presence of NFTs, playing a pivotal role in building the digital twin of our society. Metaverse can be considered to be an inspiration behind the architectural bedrock of decentralised and interoperable space where real, online, and every kind of experience that was once conceived within the bounds of a science fiction.