However, image data is difficult to manually divide into the discrete domains required by adaptation algorithms, and the standard practice of equating datasets with domains is a weak proxy for all the real conditions that alter the statistics in complex ways (lighting, pose, background, resolution, etc.) We propose an approach to automatically discover latent domains in image or video datasets. Our formulation imposes two key properties on domains: maximum distinctiveness and maximum learnability. By maximum distinctiveness, we require the underlying distributions of the identified domains to be different from each other to the maximum extent; by maximum learnability, we ensure that a strong discriminative model can be learned from the domain. We devise a nonparametric formulation and efficient optimization procedure that can successfully discover domains among both training and test data. We extensively evaluate our approach on object recognition and human activity recognition tasks.

Most current planners assume complete domain models and focus on generating correct plans. Unfortunately, domain modeling is a laborious and error-prone task, thus real world agents have to plan with incomplete domain models. While domain experts cannot guarantee completeness, often they are able to circumscribe the incompleteness of the model by providing annotations as to which parts of the domain model may be incomplete. In such cases, the goal should be to synthesize plans that are robust with respect to any known incompleteness of the domain. In this paper, we first introduce annotations expressing the knowledge of the domain incompleteness and formalize the notion of plan robustness with respect to an incomplete domain model. We then show an approach to compiling the problem of finding robust plans to the conformant probabilistic planning problem, and present experimental results with Probabilistic-FF planner.

AI-generated deepfake pictures, video or audio of existing people, places or events must be labeled as artificially manipulated. There's extra scrutiny for the biggest and most powerful AI models that pose "systemic risks," which include OpenAI's GPT4 -- its most advanced system -- and Google's Gemini. The EU says it's worried that these powerful AI systems could "cause serious accidents or be misused for far-reaching cyberattacks." They also fear generative AI could spread "harmful biases" across many applications, affecting many people. Companies that provide these systems will have to assess and mitigate the risks; report any serious incidents, such as malfunctions that cause someone's death or serious harm to health or property; put cybersecurity measures in place; and disclose how much energy their models use. Brussels first suggested AI regulations in 2019, taking a familiar global role in ratcheting up scrutiny of emerging industries, while other governments scramble to keep up. In the U.S., President Joe Biden signed a sweeping executive order on AI in October that's expected to be backed up by legislation and global agreements. In the meantime, lawmakers in at least seven U.S. states are working on their own AI legislation.

In recent years, distributed representations of inputs have led to performance gains in many applications by allowing statistical information to be shared across inputs. However, the predicted outputs (labels, and more generally structures) are still treated as discrete objects even though outputs are often not discrete units of meaning. In this paper, we present a new formulation for structured prediction where we represent individual labels in a structure as dense vectors and allow semantically similar labels to share parameters. We extend this representation to larger structures by defining compositionality using tensor products to give a natural generalization of standard structured prediction approaches. We define a learning objective for jointly learning the model parameters and the label vectors and propose an alternating minimization algorithm for learning. We show that our formulation outperforms structural SVM baselines in two tasks: multiclass document classification and part-of-speech tagging.

The European Parliament has given final approval to wide-ranging rules to govern artificial intelligence. The far-reaching regulation – the Artificial Intelligence Act – was passed by lawmakers on Wednesday. Senior European Union officials said the rules, first proposed in 2021, will protect citizens from the possible risks of a technology developing at breakneck speed while also fostering innovation. Brussels has sprinted to pass the new law since Microsoft-backed OpenAI's ChatGPT arrived on the scene in late 2022, unleashing a global AI race. Just 46 lawmakers in the European Parliament in Strasbourg voted against the proposal.

We propose a semi-parametric and dynamic rank factor model for topic modeling, capable of (i) discovering topic prevalence over time, and (ii) learning contemporary multi-scale dependence structures, providing topic and word correlations as a byproduct. The high-dimensional and time-evolving ordinal/rank observations (such as word counts), after an arbitrary monotone transformation, are well accommodated through an underlying dynamic sparse factor model. The framework naturally admits heavy-tailed innovations, capable of inferring abrupt temporal jumps in the importance of topics. Posterior inference is performed through straightforward Gibbs sampling, based on the forward-filtering backwardsampling algorithm. Moreover, an efficient data subsampling scheme is leveraged to speed up inference on massive datasets. The modeling framework is illustrated on two real datasets: the US State of the Union Address and the JSTOR collection from Science.

We formulate the problem of metric learning for k nearest neighbor classification as a large margin structured prediction problem, with a latent variable representing the choice of neighbors and the task loss directly corresponding to classification error. We describe an efficient algorithm for exact loss augmented inference, and a fast gradient descent algorithm for learning in this model. The objective drives the metric to establish neighborhood boundaries that benefit the true class labels for the training points. Our approach, reminiscent of gerrymandering (redrawing of political boundaries to provide advantage to certain parties), is more direct in its handling of optimizing classification accuracy than those previously proposed. In experiments on a variety of data sets our method is shown to achieve excellent results compared to current state of the art in metric learning.

Fox News White House correspondent Jacqui Heinrich has the latest on concerns over the president's mental and physical fitness on'Special Report.' Don't be surprised if your doctors start writing you overly friendly messages. They could be getting some help from artificial intelligence. New AI tools are helping doctors communicate with their patients, some by answering messages and others by taking notes during exams. Already thousands of doctors are using similar products based on large language models.

Sparse high dimensional graphical model selection is a popular topic in contemporary machine learning.

The success of machine learning, particularly in supervised settings, has led to numerous attempts to apply it in adversarial settings such as spam and malware detection. The core challenge in this class of applications is that adversaries are not static data generators, but make a deliberate effort to evade the classifiers deployed to detect them. We investigate both the problem of modeling the objectives of such adversaries, as well as the algorithmic problem of accounting for rational, objective-driven adversaries. In particular, we demonstrate severe shortcomings of feature reduction in adversarial settings using several natural adversarial objective functions, an observation that is particularly pronounced when the adversary is able to substitute across similar features (for example, replace words with synonyms or replace letters in words). We offer a simple heuristic method for making learning more robust to feature cross-substitution attacks. We then present a more general approach based on mixed-integer linear programming with constraint generation, which implicitly trades off overfitting and feature selection in an adversarial setting using a sparse regularizer along with an evasion model. Our approach is the first method for combining an adversarial classification algorithm with a very general class of models of adversarial classifier evasion. We show that our algorithmic approach significantly outperforms state-of-the-art alternatives.