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Powerful domain-independent planners have been developed to solve various types of planning problems. 
These planners often require a model of the acting agent&#39;s actions, given in some planning domain description language. 
Yet obtaining such an action model is a notoriously hard task. 
This task is even more challenging in mission-critical domains, where a trial-and-error approach to learning how to act is not an option. 
In such domains, the action model used to generate plans must be safe, in the sense that plans generated with it must be applicable and achieve their goals. 
The challenge of learning safe action models for planning has been recently addressed for domains in which states are sufficiently described with Boolean variables. 
In this work, we go beyond this limitation and propose the N-SAM algorithm. 
N-SAM runs in time that is polynomial in the number of observations and, under certain conditions, is guaranteed to return safe action models. 
We analyze its worst-case sample complexity, which may be intractable for benchmark domains. Empirically, however, N-SAM can quickly learn a safe action model that can solve most problems in the domain.

AAAI 2023

Learning Safe Numeric Action Models

mixed discrete/continuous planning

deterministic planning

planning/scheduling and learning

numeric planning

regression

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.


Powerful domain-independent planners have been developed to solve various types of planning problems. 
These planners often require a model of the acting agent's actions, given in some planning domain description language. 
Yet obtaining such an action model is a notoriously hard task. 
This task is even more challenging in mission-critical domains, where a trial-and-error approach to learning how to act is not an option. 
In such domains, the action model used to generate plans must be safe, in the sense that plans generated with it must be applicable and achieve their goals. 
The challenge of learning safe action models for planning has been recently addressed for domains in which states are sufficiently described with Boolean variables. 
In this work, we go beyond this limitation and propose the N-SAM algorithm. 
N-SAM runs in time that is polynomial in the number of observations and, under certain conditions, is guaranteed to return safe action models. 
We analyze its worst-case sample complexity, which may be intractable for benchmark domains. Empirically, however, N-SAM can quickly learn a safe action model that can solve most problems in the domain.

poster

Since Rendle and Krichene argued that commonly used sampling-based evaluation metrics are ``inconsistent'' with respect to the global metrics (even in expectation), there have been a few studies on the sampling-based recommender system evaluation. Existing methods try either mapping the sampling-based metrics to their global counterparts or more generally, learning the empirical rank distribution to estimate the top-$K$ metrics. 
However, despite existing efforts, there is still a lack of rigorous theoretical understanding of the proposed metric estimators, and the basic item sampling also suffers from the ``blind spot'' issue, i.e., estimation accuracy to recover the top-$K$ metrics when $K$ is small can still be rather substantial. 
In this paper, we provide an in-depth investigation into these problems and make two innovative contributions. First, we propose a new item-sampling estimator that explicitly optimizes the error with respect to the ground truth, and theoretically highlight its subtle difference against prior work. Second, we propose a new adaptive sampling method which aims to deal with the ``blind spot'' problem and also demonstrate the
expectation-maximization (EM) algorithm can be generalized for such a setting. 
Our experimental results confirm our statistical analysis and the superiority of the proposed works. 
This study helps lay the theoretical foundation for adopting item sampling metrics for recommendation evaluation, and provides strong evidence towards making item sampling a powerful and reliable tool for recommendation evaluation.

Towards Reliable Item Sampling for Recommendation Evaluation

Steganography is a technique for covert communication between two parties. With the rapid development of deep neural networks (DNN), more and more steganographic networks are proposed recently, which are shown to be promising to achieve good performance. Unlike the traditional handcrafted steganographic tools, a steganographic network is relatively large in size. It raises concerns on how to covertly transmit the steganographic network in public channels, which is a crucial stage in the pipeline of steganography in real world applications. To address such an issue, we propose a novel scheme for steganography of steganographic networks in this paper. Unlike the existing steganographic schemes which focus on the subtle modification of the cover data to accommodate the secrets. We propose to disguise a steganographic network (termed as the secret DNN model) into a stego DNN model which performs an ordinary machine learning task (termed as the stego task). During the model disguising, we select and tune a subset of filters in the secret DNN model to preserve its function on the secret task, where the remaining filters are reactivated according to a partial optimization strategy to disguise the whole secret DNN model into a stego DNN model. The secret DNN model can be recovered from the stego DNN model when needed. Various experiments have been conducted to demonstrate the advantage of our proposed method for covert communication of steganographic networks as well as general DNN models. 

Steganography of Steganographic Networks

Autoregressive language modeling (ALM) have been successfully used in self-supervised pre-training in Natural language processing (NLP). However, this paradigm has not achieved comparable results with other self-supervised approach in computer vision (e.g., contrastive learning, mask image modeling). In this paper, we try to find the reason why autoregressive modeling does not work well on vision tasks. To tackle this problem, we fully analyze the limitation of visual autoregressive methods and proposed a novel stochastic autoregressive image modeling (named SAIM) by the two simple designs. First, we employ stochastic permutation strategy to generate effective and robust image context which is critical for vision tasks. Second, we create a parallel encoder-decoder training process in which the encoder serves a similar role to the standard vision transformer focus on learning the whole contextual information, and meanwhile the decoder predicts the content of the current position, so that the encoder and decoder can reinforce each other. By introducing stochastic prediction and the parallel encoder-decoder, SAIM significantly improve the performance of autoregressive image modeling. Our method achieves the best accuracy (83.9\%) on the vanilla ViT-Base model among methods using only ImageNet-1K data. Transfer performance in downstream tasks also show that our model achieves competitive performance.

Exploring Stochastic Autoregressive Image Modeling for Visual Representation

Nowadays, systems based on machine learning (ML) are widely used in different domains.
Given their popularity, ML models have become targets for various attacks.
As a result, research at the intersection of security/privacy and ML has flourished. Typically such work has focused on individual types of security/privacy concerns and mitigations thereof.

However, in real-life deployments, an ML model will need to be protected against several concerns simultaneously.
A protection mechanism optimal for a specific security or privacy concern may interact negatively with mechanisms intended to address other concerns. Despite its practical relevance, the potential for such conflicts has not been studied adequately.

In this work, we first provide a framework for analyzing such conflicting interactions.
We then focus on systematically analyzing pairwise interactions between protection mechanisms for one concern, model and data ownership verification, with two other classes of ML protection mechanisms: differentially private training, and robustness against model evasion.
We find that several pairwise interactions result in conflicts.

We also explore potential approaches for avoiding such conflicts. First, we study the effect of hyperparameter relaxations, finding that there is no sweet spot balancing the performance of both protection mechanisms.
Second, we explore whether modifying one type of protection mechanism (ownership verification) so as to decouple it from factors that may be impacted by a conflicting mechanism (differentially private training or robustness to model evasion) can avoid conflict.
We show that this approach can indeed avoid the conflict between ownership verification mechanisms when combined with differentially private training, but has no effect on robustness to model evasion. We conclude by identifying the gaps in the landscape of studying interactions between other types of ML protection mechanisms.

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