We establish new generalisation bounds for multiclass classification by abstracting to a more general setting of discretised error types. Extending the PAC-Bayes theory, we are hence able to provide fine-grained bounds on performance for multiclass classification, as well as applications to other learning problems including discretisation of regression losses. Tractable training objectives are derived from the bounds. The bounds are uniform over all weightings of the discretised error types and thus can be used to bound weightings not foreseen at training, including the full confusion matrix in the multiclass classification case.

Existing work (Ji and Grishman, we consider the attention weight between 2008; McClosky et al., 2011; Li et al., 2013; two event mentions as a learned similarity, and we Chen et al., 2015; Du and Cardie, 2020; Li et al., ensure that the attention mechanism learns to align 2021a) traditionally uses a predefined list of event similar events using a semi-supervised contrastive types and their respective annotations to learn an loss. By doing this, we are able to leverage the event extraction model. However, these annotations large variety of semantic information in pretrained are both expensive and time-consuming to language models for clustering unseen types by using create. This problem is amplified when considering a trained attention head. Unlike (Huang and specialization-intensive domains such as scientific Ji, 2020), we are able to separate clustering from literature, which requires years of specialized experience learning, allowing specific task-suited clustering to understand even a specific niche. For algorithms to be selected.

Sim2real transfer is primarily concerned with transferring policies trained in simulation to potentially noisy real world environments. A common problem associated with sim2real transfer is estimating the real-world environmental parameters to ground the simulated environment to. Although existing methods such as Domain Randomisation (DR) can produce robust policies by sampling from a distribution of parameters during training, there is no established method for identifying the parameters of the corresponding distribution for a given real-world setting. In this work, we propose Uncertainty-aware policy search (UncAPS), where we use Universal Policy Network (UPN) to store simulation-trained task-specific policies across the full range of environmental parameters and then subsequently employ robust Bayesian optimisation to craft robust policies for the given environment by combining relevant UPN policies in a DR like fashion. Such policy-driven grounding is expected to be more efficient as it estimates only task-relevant sets of parameters. Further, we also account for the estimation uncertainties in the search process to produce policies that are robust against both aleatoric and epistemic uncertainties. We empirically evaluate our approach in a range of noisy, continuous control environments, and show its improved performance compared to competing baselines.

In this paper, we investigate inductive inference with system W from conditional belief bases with respect to syntax splitting. The concept of syntax splitting for inductive inference states that inferences about independent parts of the signature should not affect each other. This was captured in work by Kern-Isberner, Beierle, and Brewka in the form of postulates for inductive inference operators expressing syntax splitting as a combination of relevance and independence; it was also shown that c-inference fulfils syntax splitting, while system P inference and system Z both fail to satisfy it. System W is a recently introduced inference system for nonmonotonic reasoning that captures and properly extends system Z as well as c-inference. We show that system W fulfils the syntax splitting postulates for inductive inference operators by showing that it satisfies the required properties of relevance and independence. This makes system W another inference operator besides c-inference that fully complies with syntax splitting, while in contrast to c-inference, also extending rational closure.

We study the benign overfitting theory in the prediction of the conditional average treatment effect (CATE), with linear regression models. As the development of machine learning for causal inference, a wide range of large-scale models for causality are gaining attention. One problem is that suspicions have been raised that the large-scale models are prone to overfitting to observations with sample selection, hence the large models may not be suitable for causal prediction. In this study, to resolve the suspicious, we investigate on the validity of causal inference methods for overparameterized models, by applying the recent theory of benign overfitting (Bartlett et al., 2020). Specifically, we consider samples whose distribution switches depending on an assignment rule, and study the prediction of CATE with linear models whose dimension diverges to infinity. We focus on two methods: the T-learner, which based on a difference between separately constructed estimators with each treatment group, and the inverse probability weight (IPW)-learner, which solves another regression problem approximated by a propensity score. In both methods, the estimator consists of interpolators that fit the samples perfectly. As a result, we show that the T-learner fails to achieve the consistency except the random assignment, while the IPW-learner converges the risk to zero if the propensity score is known. This difference stems from that the T-learner is unable to preserve eigenspaces of the covariances, which is necessary for benign overfitting in the overparameterized setting. Our result provides new insights into the usage of causal inference methods in the overparameterizated setting, in particular, doubly robust estimators.

Fluence announced an agreement with The AES Corporation, a Fortune 500 global energy company, to implement the AI-powered Fluence IQ Bidding Application to maximize the value of a 1.1GW portfolio of solar and energy storage projects in the Western US. In October of last year, Fluence acquired AMS' software and digital intelligence platform for renewables and energy storage. The platform is designed to improve revenue of energy storage assets in wholesale markets. AMS' technology uses artificial intelligence, advanced price forecasting, portfolio optimization and market bidding to ensure energy storage and flexible generation assets are responding optimally to price signals sent by the market. The way the Fluence IQ Bidding Application works is it recommends bids into daily and hourly auctions for energy and grid services, anticipating opportunities using advanced forecasting to take advantage of favorable pricing, while minimizing exposure to unfavorable pricing.

Smart farming using AI and IoT is no longer a distant dream, smart farms are here to stay thanks to amazing advancements in AI and IoT devices. Over the past decades, the agriculture sector has undergone significant changes. Today, it's possible to grow plants even in the most hostile climatic regions. Crops are more resistant to insects, weeds, and climate change than ever before. Lastly, it's possible to breed high-yielding farm animals. But despite all these advancements, a large population of the world is still undernourished.

Machine learning global investment is expected to grow at a rate of 44.1% CAGR from 2016 to 2022 and increased up to 8.81 billion. Altogether, Machine learning has become a vital tactic for organizations in order to drive customer engagement, increase ROI, and gain competitive advantages. Currently, ML has received adoption worldwide and is thriving as a major technological advancement in data generation & analysis. The major driving factors are improved customer data insight, trend analysis & forecasting, and enhanced business operations. In the endeavor to infuse this technology, machine learning companies are supporting both large and small enterprises. That is boosting its significance in the current marketplace. Netflix, Upwork, Google, YouTube, and many other giant brands are using AI and machine learning algorithms. If you are thinking about infusing machine learning and AI algorithms within your business platforms, then it is essential to go along with an innovative and value-driven approach.

Fleur had the pleasure of speaking with Ian Kuali'i, a multi-disciplinary self-taught artist of Hawaiian/Apache ancestry working in the forms of murals, large-scale hand cut paper and site-specific installations. From a single sheet of paper using only an xacto blade as his tool, Ian's portraits, journal entries and scenes are masterfully rendered in hand cut paper with a blend of loose urban contemporary techniques and collaged found materials. Ian describes his creative process as "The meditative process of destroying to create." Fleur: Can you please introduce yourself? My name is Ian Joseph Kekoa Hardwick-Kuali'i or just simply Ian Kuali'i.

UNSW is also taking a leading role in pushing forward the codification of the use of AI in the law9 and in understanding and highlighting the ethical boundaries where AI techniques can and should really be used for the benefit of society. All up, UNSW has over 300 researchers involved in pushing forward the power of AI related techniques for the good of society. These researchers participate in over a dozen research centres, labs and facilities across all faculties at UNSW, including at ADFA@UNSW Canberra providing Australia's defence forces with world leading teaching and research. This unique capability has developed organically over time but currently lacks a cohesive means to bring them all together. UNSW.ai is designed to harness the power of this capability to accelerate our development of new and more powerful AI techniques while ensuring their integrity, security and appropriate use.