The rise of powerful generative models has sparked concerns over data authenticity. While detection methods have been extensively developed for images and text, the case of tabular data, despite its ubiquity, has been largely overlooked. Yet, detecting synthetic tabular data is especially challenging due to its heterogeneous structure and unseen formats at test time. We address the underexplored task of detecting synthetic tabular data "in the wild", i.e. when the detector is deployed on tables with variable and previously unseen schemas. We introduce a novel datum-wise transformer architecture that significantly outperforms the only previously published baseline, improving both AUC and accuracy by 7 points. By incorporating a table-adaptation component, our model gains an additional 7 accuracy points, demonstrating enhanced robustness. This work provides the first strong evidence that detecting synthetic tabular data in real-world conditions is feasible, and demonstrates substantial improvements over previous approaches. The code will be made available in the extended version.

Task-oriented dialogue systems are designed to achieve specific goals while conversing with humans.In practice, they may have to handle simultaneously several domains and tasks.The dialogue manager must therefore be able to take into account domain changes and plan over different domains/tasks in order to deal with multidomain dialogues.However, learning with reinforcement in such context becomes difficult because the state-action dimension is larger while the reward signal remains scarce.Our experimental results suggest that structured policies based on graph neural networks combined with different degrees of imitation learning can effectively handle multi-domain dialogues.The reported experiments underline the benefit of structured policies over standard policies.

We study a search-based paraphrase generation scheme where candidate paraphrases are generated by iterated transformations from the original sentence and evaluated in terms of syntax quality, semantic distance, and lexical distance.The semantic distance is derived from BERT, and the lexical quality is based on GPT2 perplexity.To solve this multi-objective search problem, we propose two algorithms: Monte-Carlo Tree Search For Paraphrase Generation (MCPG) and Pareto Tree Search (PTS).We provide an extensive set of experiments on 5 datasets with a rigorous reproduction and validation for several state-of-the-art paraphrase generation algorithms.These experiments show that, although being non explicitly supervised, our algorithms perform well against these baselines.

A Budgeted Markov Decision Process (BMDP) is an extension of a Markov Decision Process to critical applications requiring safety constraints. It relies on a notion of risk implemented in the shape of an upper bound on a constrains violation signal that -- importantly -- can be modified in real-time. So far, BMDPs could only be solved in the case of finite state spaces with known dynamics. This work extends the state-of-the-art to continuous spaces environments and unknown dynamics. We show that the solution to a BMDP is the fixed point of a novel Budgeted Bellman Optimality operator. This observation allows us to introduce natural extensions of Deep Reinforcement Learning algorithms to address large-scale BMDPs. We validate our approach on two simulated applications: spoken dialogue and autonomous driving.

We study a variant of the stochastic multi-armed bandit (MAB) problem in which the rewards are corrupted. In this framework, motivated by privacy preservation in online recommender systems, the goal is to maximize the sum of the (unobserved) rewards, based on the observation of transformation of these rewards through a stochastic corruption process with known parameters. We provide a lower bound on the expected regret of any bandit algorithm in this corrupted setting. We devise a frequentist algorithm, KLUCB-CF, and a Bayesian algorithm, TS-CF and give upper bounds on their regret. We also provide the appropriate corruption parameters to guarantee a desired level of local privacy and analyze how this impacts the regret. Finally, we present some experimental results that confirm our analysis.

To address the contextual bandit problem, we propose an online random forest algorithm. The analysis of the proposed algorithm is based on the sample complexity needed to find the optimal decision stump. Then, the decision stumps are recursively stacked in a random collection of decision trees, BANDIT FOREST. We show that the proposed algorithm is optimal up to logarithmic factors. The dependence of the sample complexity upon the number of contextual variables is logarithmic. The computational cost of the proposed algorithm with respect to the time horizon is linear. These analytical results allow the proposed algorithm to be efficient in real applications , where the number of events to process is huge, and where we expect that some contextual variables, chosen from a large set, have potentially non-linear dependencies with the rewards. In the experiments done to illustrate the theoretical analysis, BANDIT FOREST obtain promising results in comparison with state-of-the-art algorithms.

We study the K-armed dueling bandit problem which is a variation of the classical Multi-Armed Bandit (MAB) problem in which the learner receives only relative feedback about the selected pairs of arms. We propose a new algorithm called Relative Exponential-weight algorithm for Exploration and Exploitation (REX3) to handle the adversarial utility-based formulation of this problem. This algorithm is a non-trivial extension of the Exponential-weight algorithm for Exploration and Exploitation (EXP3) algorithm. We prove a finite time expected regret upper bound of order O(sqrt(K ln(K)T)) for this algorithm and a general lower bound of order omega(sqrt(KT)). At the end, we provide experimental results using real data from information retrieval applications.

We study a stochastic online learning scheme with partial feedback where the utility of decisions is only observable through an estimation of the environment parameters. We propose a generic pure-exploration algorithm, able to cope with various utility functions from multi-armed bandits settings to dueling bandits. The primary application of this setting is to offer a natural generalization of dueling bandits for situations where the environment parameters reflect the idiosyncratic preferences of a mixed crowd.

Automatically generated content is ubiquitous in the web: dynamic sites built using the three-tier paradigm are good examples (e.g., commercial sites, blogs and other sites edited using web authoring software), as well as less legitimate spamdexing attempts (e.g., link farms, faked directories). Those pages built using the same generating method (template or script) share a common “look and feel” that is not easily detected by common text classification methods, but is more related to stylometry. In this work we study and compare several HTML style similarity measures based on both textual and extra-textual features in HTML source code. We also propose a flexible algorithm to cluster a large collection of documents according to these measures. Since the proposed algorithm is based on locality sensitive hashing (LSH), we first review this technique. We then describe how to use the HTML style similarity clusters to pinpoint dubious pages and enhance the quality of spam classifiers. We present an evaluation of our algorithm on the WEBSPAM-UK2006 dataset.