In this paper we provide an algorithmic framework based on Langevin diffusion (LD) and its corresponding discretizations that allow us to simultaneously obtain: i) An algorithm for sampling from the exponential mechanism, whose privacy analysis does not depend on convexity and which can be stopped at anytime without compromising privacy, and ii) tight uniform stability guarantees for the exponential mechanism. As a direct consequence, we obtain optimal excess empirical and population risk guarantees for (strongly) convex losses under both pure and approximate differential privacy (DP). The framework allows us to design a DP uniform sampler from the Rashomon set. Rashomon sets are widely used in interpretable and robust machine learning, understanding variable importance, and characterizing fairness.
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Hallucination plagues even frontier LLMs--but how bad is it really for summarizing academic papers? We evaluate Factored Verification, a simple automated method for detecting hallucinations in abstractive summaries. This method sets a new SotA on hallucination detection in the summarization task of the HaluEval benchmark, achieving 76.2% accuracy. We then use this method to estimate how often language models hallucinate when summarizing across multiple academic papers and find 0.62 hallucinations in the average ChatGPT (16k) summary, 0.84 for GPT-4, and 1.55 for Claude 2. We ask models to self-correct using Factored Critiques and find that this lowers the number of hallucinations to 0.49 for ChatGPT, 0.46 for GPT-4, and 0.95 for Claude 2. The hallucinations we find are often subtle, so we advise caution when using models to synthesize academic papers.
Predicting pedestrian behavior is one of the main challenges for intelligent driving systems. In this paper, we present a new paradigm for evaluating egocentric pedestrian trajectory prediction algorithms. Based on various contextual information, we extract driving scenarios for a meaningful and systematic approach to identifying challenges for prediction models. In this regard, we also propose a new metric for more effective ranking within the scenario-based evaluation. We conduct extensive empirical studies of existing models on these scenarios to expose shortcomings and strengths of different approaches. The scenario-based analysis highlights the importance of using multimodal sources of information and challenges caused by inadequate modeling of ego-motion and scale of pedestrians. To this end, we propose a novel egocentric trajectory prediction model that benefits from multimodal sources of data fused in an effective and efficient step-wise hierarchical fashion and two auxiliary tasks designed to learn more robust representation of scene dynamics. We show that our approach achieves significant improvement by up to 40% in challenging scenarios compared to the past arts via empirical evaluation on common benchmark datasets.
This paper investigates the transferability of debiasing techniques across different languages within multilingual models. We examine the applicability of these techniques in English, French, German, and Dutch. Using multilingual BERT (mBERT), we demonstrate that cross-lingual transfer of debiasing techniques is not only feasible but also yields promising results. Surprisingly, our findings reveal no performance disadvantages when applying these techniques to non-English languages. Using translations of the CrowS-Pairs dataset, our analysis identifies SentenceDebias as the best technique across different languages, reducing bias in mBERT by an average of 13%. We also find that debiasing techniques with additional pretraining exhibit enhanced cross-lingual effectiveness for the languages included in the analyses, particularly in lower-resource languages. These novel insights contribute to a deeper understanding of bias mitigation in multilingual language models and provide practical guidance for debiasing techniques in different language contexts.
In this paper, we delve into the problem of simplicial representation learning utilizing the 1-Wasserstein distance on a tree structure (a.k.a., Tree-Wasserstein distance (TWD)), where TWD is defined as the L1 distance between two tree-embedded vectors. Specifically, we consider a framework for simplicial representation estimation employing a self-supervised learning approach based on SimCLR with a negative TWD as a similarity measure. In SimCLR, the cosine similarity with real-vector embeddings is often utilized; however, it has not been well studied utilizing L1-based measures with simplicial embeddings. A key challenge is that training the L1 distance is numerically challenging and often yields unsatisfactory outcomes, and there are numerous choices for probability models. Thus, this study empirically investigates a strategy for optimizing self-supervised learning with TWD and find a stable training procedure. More specifically, we evaluate the combination of two types of TWD (total variation and ClusterTree) and several simplicial models including the softmax function, the ArcFace probability model, and simplicial embedding. Moreover, we propose a simple yet effective Jeffrey divergence-based regularization method to stabilize the optimization. Through empirical experiments on STL10, CIFAR10, CIFAR100, and SVHN, we first found that the simple combination of softmax function and TWD can obtain significantly lower results than the standard SimCLR (non-simplicial model and cosine similarity). We found that the model performance depends on the combination of TWD and the simplicial model, and the Jeffrey divergence regularization usually helps model training. Finally, we inferred that the appropriate choice of combination of TWD and simplicial models outperformed cosine similarity based representation learning.
The maximum likelihood estimation is widely used for statistical inferences. In this study, we reformulate the h-likelihood proposed by Lee and Nelder in 1996, whose maximization yields maximum likelihood estimators for fixed parameters and asymptotically best unbiased predictors for random parameters. We establish the statistical foundations for h-likelihood theories, which extend classical likelihood theories to embrace broad classes of statistical models with random parameters. The maximum h-likelihood estimators asymptotically achieve the generalized Cramer-Rao lower bound. Furthermore, we explore asymptotic theory when the consistency of either fixed parameter estimation or random parameter prediction is violated. The introduction of this new h-likelihood framework enables likelihood theories to cover inferences for a much broader class of models, while also providing computationally efficient fitting algorithms to give asymptotically optimal estimators for fixed parameters and predictors for random parameters.
In this paper, we investigate a general class of stochastic gradient descent (SGD) algorithms, called Conditioned SGD, based on a preconditioning of the gradient direction. Using a discrete-time approach with martingale tools, we establish under mild assumptions the weak convergence of the rescaled sequence of iterates for a broad class of conditioning matrices including stochastic first-order and second-order methods. Almost sure convergence results, which may be of independent interest, are also presented. Interestingly, the asymptotic normality result consists in a stochastic equicontinuity property so when the conditioning matrix is an estimate of the inverse Hessian, the algorithm is asymptotically optimal.
In this paper, we show a textual analysis of past ICALEPCS and IPAC conference proceedings to gain insights into the research trends and topics discussed in the field. We use natural language processing techniques to extract meaningful information from the abstracts and papers of past conference proceedings. We extract topics to visualize and identify trends, analyze their evolution to identify emerging research directions, and highlight interesting publications based solely on their content with an analysis of their network. Additionally, we will provide an advanced search tool to better search the existing papers to prevent duplication and easier reference findings. Our analysis provides a comprehensive overview of the research landscape in the field and helps researchers and practitioners to better understand the state-of-the-art and identify areas for future research.
In this paper, with the goal of quantifying the qualitative image outputs of a Vision-based Tactile Sensor (VTS), we present the design, fabrication, and characterization of a novel Quantitative Surface Tactile Sensor (called QS-TS). QS-TS directly estimates the sensor's gel layer deformation in real-time enabling safe and autonomous tactile manipulation and servoing of delicate objects using robotic manipulators. The core of the proposed sensor is the utilization of miniature 1.5 mm x 1.5 mm synthetic square markers with inner binary patterns and a broad black border, called ArUco Markers. Each ArUco marker can provide real-time camera pose estimation that, in our design, is used as a quantitative measure for obtaining deformation of the QS-TS gel layer. Moreover, thanks to the use of ArUco markers, we propose a unique fabrication procedure that mitigates various challenges associated with the fabrication of the existing marker-based VTSs and offers an intuitive and less-arduous method for the construction of the VTS. Remarkably, the proposed fabrication facilitates the integration and adherence of markers with the gel layer to robustly and reliably obtain a quantitative measure of deformation in real-time regardless of the orientation of ArUco Markers. The performance and efficacy of the proposed QS-TS in estimating the deformation of the sensor's gel layer were experimentally evaluated and verified. Results demonstrate the phenomenal performance of the QS-TS in estimating the deformation of the gel layer with a relative error of <5%.
In this paper, we investigate the practical relevance of explainable artificial intelligence (XAI) with a special focus on the producing industries and relate them to the current state of academic XAI research. Our findings are based on an extensive series of interviews regarding the role and applicability of XAI along the Machine Learning (ML) lifecycle in current industrial practice and its expected relevance in the future. The interviews were conducted among a great variety of roles and key stakeholders from different industry sectors. On top of that, we outline the state of XAI research by providing a concise review of the relevant literature. This enables us to provide an encompassing overview covering the opinions of the surveyed persons as well as the current state of academic research. By comparing our interview results with the current research approaches we reveal several discrepancies. While a multitude of different XAI approaches exists, most of them are centered around the model evaluation phase and data scientists. Their versatile capabilities for other stages are currently either not sufficiently explored or not popular among practitioners. In line with existing work, our findings also confirm that more efforts are needed to enable also non-expert users' interpretation and understanding of opaque AI models with existing methods and frameworks.
Salient object detection is a problem that has been considered in detail and many solutions proposed. In this paper, we argue that work to date has addressed a problem that is relatively ill-posed. Specifically, there is not universal agreement about what constitutes a salient object when multiple observers are queried. This implies that some objects are more likely to be judged salient than others, and implies a relative rank exists on salient objects. The solution presented in this paper solves this more general problem that considers relative rank, and we propose data and metrics suitable to measuring success in a relative objects saliency landscape. A novel deep learning solution is proposed based on a hierarchical representation of relative saliency and stage-wise refinement. We also show that the problem of salient object subitizing can be addressed with the same network, and our approach exceeds performance of any prior work across all metrics considered (both traditional and newly proposed).
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