pwc-archive/papers-with-abstracts huggingface dataset:
Learning finitely correlated states: stability of the spectral reconstruction
Authors: Marco Fanizza, Niklas Galke, Josep Lumbreras, Cambyse Rouzé, Andreas Winter
Date: 2023-12-12 00:00:00
Tasks: Spectral Reconstruction, Translation

Matrix product operators allow efficient descriptions (or realizations) of states on a 1D lattice. We consider the task of learning a realization of minimal dimension from copies of an unknown state, such that the resulting operator is close to the density matrix in trace norm. For finitely correlated translation-invariant states on an infinite chain, a realization of minimal dimension can be exactly reconstructed via linear algebra operations from the marginals of a size depending on the representation dimension. We establish a bound on the trace norm error for an algorithm that estimates a candidate realization from estimates of these marginals and outputs a matrix product operator, estimating the state of a chain of arbitrary length $t$. This bound allows us to establish an $O(t^2)$ upper bound on the sample complexity of the learning task, with an explicit dependence on the site dimension, realization dimension and spectral properties of a certain map constructed from the state. A refined error bound can be proven for $C^*$-finitely correlated states, which have an operational interpretation in terms of sequential quantum channels applied to the memory system. We can also obtain an analogous error bound for a class of matrix product density operators on a finite chain reconstructible by local marginals. In this case, a linear number of marginals must be estimated, obtaining a sample complexity of $\tilde{O}(t^3)$. The learning algorithm also works for states that are sufficiently close to a finitely correlated state, with the potential of providing competitive algorithms for other interesting families of states.
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems
Authors: Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi, Victor Schmidt, Santiago Miret, Fragkiskos D. Malliaros, Taco Cohen, Pietro Liò, Yoshua Bengio, Michael Bronstein
Date: 2023-12-12 00:00:00
Tasks: Protein Structure Prediction, Specificity

Recent advances in computational modelling of atomic systems, spanning molecules, proteins, and materials, represent them as geometric graphs with atoms embedded as nodes in 3D Euclidean space. In these graphs, the geometric attributes transform according to the inherent physical symmetries of 3D atomic systems, including rotations and translations in Euclidean space, as well as node permutations. In recent years, Geometric Graph Neural Networks have emerged as the preferred machine learning architecture powering applications ranging from protein structure prediction to molecular simulations and material generation. Their specificity lies in the inductive biases they leverage - such as physical symmetries and chemical properties - to learn informative representations of these geometric graphs. In this opinionated paper, we provide a comprehensive and self-contained overview of the field of Geometric GNNs for 3D atomic systems. We cover fundamental background material and introduce a pedagogical taxonomy of Geometric GNN architectures: (1) invariant networks, (2) equivariant networks in Cartesian basis, (3) equivariant networks in spherical basis, and (4) unconstrained networks. Additionally, we outline key datasets and application areas and suggest future research directions. The objective of this work is to present a structured perspective on the field, making it accessible to newcomers and aiding practitioners in gaining an intuition for its mathematical abstractions.
BIRB: A Generalization Benchmark for Information Retrieval in Bioacoustics
Authors: Jenny Hamer, Eleni Triantafillou, Bart van Merriënboer, Stefan Kahl, Holger Klinck, Tom Denton, Vincent Dumoulin
Date: 2023-12-12 00:00:00
Tasks: Information Retrieval, Representation Learning, Retrieval

The ability for a machine learning model to cope with differences in training and deployment conditions--e.g. in the presence of distribution shift or the generalization to new classes altogether--is crucial for real-world use cases. However, most empirical work in this area has focused on the image domain with artificial benchmarks constructed to measure individual aspects of generalization. We present BIRB, a complex benchmark centered on the retrieval of bird vocalizations from passively-recorded datasets given focal recordings from a large citizen science corpus available for training. We propose a baseline system for this collection of tasks using representation learning and a nearest-centroid search. Our thorough empirical evaluation and analysis surfaces open research directions, suggesting that BIRB fills the need for a more realistic and complex benchmark to drive progress on robustness to distribution shifts and generalization of ML models.
PIMoG: An Effective Screen-shooting Noise-Layer Simulation for Deep-Learning-Based Watermarking Network
Authors: Han Fang
Date: 2022-10-10 00:00:00
Proceeding: MM '22: Proceedings of the 30th ACM International Conference on Multimedia 2022 10

With the omnipresence of camera phone and digital display, capturing digitally displayed image with camera phone are getting widely practiced. In the context of watermarking, this brings forth the issue of screen-shooting robustness. The key to acquiring screenshooting robustness is designing a good noise layer that could represent screen-shooting distortions in a deep-learning-based watermarking framework. However, it is very difficult to quantitatively formulate the screen-shooting distortion since the screen-shooting process is too complex. In order to design an effective noise layer for screen-shooting robustness, we propose new insight in this paper, that is, it is not necessary to quantitatively simulate the overall procedure in the screen-shooting noise layer, only including the most influenced distortions is enough to generate an effective noise layer with strong robustness. To verify this insight, we propose a screen-shooting noise layer dubbed PIMoG. Specifically, we summarize the most influenced distortions of screen-shooting process into three parts (perspective distortion, illumination distortion and moiré distortion) and further simulate them in a differentiable way.For the rest distortion, we utilize the Gaussian noise to approximate the main part of them. As a result, the whole network can be trained end-to-end with such noise layer. Extensive experiments illustrate the superior performance of the proposed PIMoG noise layer. In addition to the noise layer design, we also propose a gradient maskguided image loss and an edge mask-guided image loss to further improve the robustness and invisibility of the whole network respectively. Based on the proposed loss and PIMoG noise layer, the whole framework outperforms the SOTA watermarking method with at least 5% in extraction accuracy and achieves more than 97% accuracy in different screen-shooting conditions.
Multi-Modal Conformal Prediction Regions with Simple Structures by Optimizing Convex Shape Templates
Authors: Renukanandan Tumu, Matthew Cleaveland, Rahul Mangharam, George J. Pappas, Lars Lindemann
Date: 2023-12-12 00:00:00
Tasks: Autonomous Vehicles, Conformal Prediction, Prediction, Time Series Prediction, valid

Conformal prediction is a statistical tool for producing prediction regions for machine learning models that are valid with high probability. A key component of conformal prediction algorithms is a \emph{non-conformity score function} that quantifies how different a model's prediction is from the unknown ground truth value. Essentially, these functions determine the shape and the size of the conformal prediction regions. While prior work has gone into creating score functions that produce multi-model prediction regions, such regions are generally too complex for use in downstream planning and control problems. We propose a method that optimizes parameterized \emph{shape template functions} over calibration data, which results in non-conformity score functions that produce prediction regions with minimum volume. Our approach results in prediction regions that are \emph{multi-modal}, so they can properly capture residuals of distributions that have multiple modes, and \emph{practical}, so each region is convex and can be easily incorporated into downstream tasks, such as a motion planner using conformal prediction regions. Our method applies to general supervised learning tasks, while we illustrate its use in time-series prediction. We provide a toolbox and present illustrative case studies of F16 fighter jets and autonomous vehicles, showing an up to $68\%$ reduction in prediction region area compared to a circular baseline region.
FairSISA: Ensemble Post-Processing to Improve Fairness of Unlearning in LLMs
Authors: Swanand Ravindra Kadhe, Anisa Halimi, Ambrish Rawat, Nathalie Baracaldo
Date: 2023-12-12 00:00:00
Tasks: Fairness, Unsupervised Pre-training

Training large language models (LLMs) is a costly endeavour in terms of time and computational resources. The large amount of training data used during the unsupervised pre-training phase makes it difficult to verify all data and, unfortunately, undesirable data may be ingested during training. Re-training from scratch is impractical and has led to the creation of the 'unlearning' discipline where models are modified to "unlearn" undesirable information without retraining. However, any modification can alter the behaviour of LLMs, especially on key dimensions such as fairness. This is the first work that examines this interplay between unlearning and fairness for LLMs. In particular, we focus on a popular unlearning framework known as SISA [Bourtoule et al., 2021], which creates an ensemble of models trained on disjoint shards. We evaluate the performance-fairness trade-off for SISA, and empirically demsontrate that SISA can indeed reduce fairness in LLMs. To remedy this, we propose post-processing bias mitigation techniques for ensemble models produced by SISA. We adapt the post-processing fairness improvement technique from [Hardt et al., 2016] to design three methods that can handle model ensembles, and prove that one of the methods is an optimal fair predictor for ensemble of models. Through experimental results, we demonstrate the efficacy of our post-processing framework called 'FairSISA'.
QSMVM: QoS-aware and social-aware multimetric routing protocol for video-streaming services over MANETs
Authors: Efraín Palacios Jara, Ahmad Mohamad Mezhe, Mónica Aguilar Igartua, Rebeca P. Díaz Redondo, Ana Fernández Vilas
Date: 2023-12-12 00:00:00

A mobile ad hoc network (MANET) is a set of autonomous mobile devices connected by wireless links in a distributed manner and without a fixed infrastructure. Real-time multimedia services, such as video-streaming over MANETs, offers very promising applications, e.g. two members of a group of tourists who want to share a video transmitted through the MANET they form; a video-streaming service deployed over a MANET where users watch a film; among other examples. On the other hand, social web technologies, where people actively interact online with others through social networks, are leading to a socialization of networks. Information of interaction among users is being used to provide socially-enhanced software. To achieve this, we need to know the strength of the relationship between a given user and each user they interact with. This strength of the relationship can be measured through a concept called tie strength (TS), first introduced by Mark Granovetter in 1973. In this article, we modify our previous proposal named multipath multimedia dynamic source routing (MMDSR) protocol to include a social metric TS in the decisions taken by the forwarding algorithm. We find a trade-off between the quality of service (QoS) and the trust level between users who form the forwarding path in the MANET. Our goal is to increase the trust metric while the QoS is not affected significantly.
ICL Markup: Structuring In-Context Learning using Soft-Token Tags
Authors: Marc-Etienne Brunet, Ashton Anderson, Richard Zemel
Date: 2023-12-12 00:00:00
Tasks: In-Context Learning, Intent Detection, Meta-Learning, parameter-efficient fine-tuning, text-classification, Text Classification

Large pretrained language models (LLMs) can be rapidly adapted to a wide variety of tasks via a text-to-text approach, where the instruction and input are fed to the model in natural language. Combined with in-context learning (ICL), this paradigm is impressively flexible and powerful. However, it also burdens users with an overwhelming number of choices, many of them arbitrary. Inspired by markup languages like HTML, we contribute a method of using soft-token tags to compose prompt templates. This approach reduces arbitrary decisions and streamlines the application of ICL. Our method is a form of meta-learning for ICL; it learns these tags in advance during a parameter-efficient fine-tuning ``warm-up'' process. The tags can subsequently be used in templates for ICL on new, unseen tasks without any additional fine-tuning. Our experiments with this approach yield promising initial results, improving LLM performance on important enterprise applications such as few-shot and open-world intent detection, as well as text classification in news and legal domains.
Momentum Particle Maximum Likelihood
Authors: Jen Ning Lim, Juan Kuntz, Samuel Power, Adam M. Johansen
Date: 2023-12-12 00:00:00

Maximum likelihood estimation (MLE) of latent variable models is often recast as the minimization of a free energy functional over an extended space of parameters and probability distributions. This perspective was recently combined with insights from optimal transport to obtain novel particle-based algorithms for fitting latent variable models to data. Drawing inspiration from prior works which interpret `momentum-enriched' optimization algorithms as discretizations of ordinary differential equations, we propose an analogous dynamical-systems-inspired approach to minimizing the free energy functional. The result is a dynamical system that blends elements of Nesterov's Accelerated Gradient method, the underdamped Langevin diffusion, and particle methods. Under suitable assumptions, we prove that the continuous-time system minimizes the functional. By discretizing the system, we obtain a practical algorithm for MLE in latent variable models. The algorithm outperforms existing particle methods in numerical experiments and compares favourably with other MLE algorithms.
GateNet: A novel Neural Network Architecture for Automated Flow Cytometry Gating
Authors: Lukas Fisch, Michael O. Heming, Andreas Schulte-Mecklenbeck, Catharina C. Gross, Stefan Zumdick, Carlotta Barkhau, Daniel Emden, Jan Ernsting, Ramona Leenings, Kelvin Sarink, Nils R. Winter, Udo Dannlowski, Heinz Wiendl, Gerd Meyer zu Hörste, Tim Hahn
Date: 2023-12-12 00:00:00
Tasks: GPU

Flow cytometry is widely used to identify cell populations in patient-derived fluids such as peripheral blood (PB) or cerebrospinal fluid (CSF). While ubiquitous in research and clinical practice, flow cytometry requires gating, i.e. cell type identification which requires labor-intensive and error-prone manual adjustments. To facilitate this process, we designed GateNet, the first neural network architecture enabling full end-to-end automated gating without the need to correct for batch effects. We train GateNet with over 8,000,000 events based on N=127 PB and CSF samples which were manually labeled independently by four experts. We show that for novel, unseen samples, GateNet achieves human-level performance (F1 score ranging from 0.910 to 0.997). In addition we apply GateNet to a publicly available dataset confirming generalization with an F1 score of 0.936. As our implementation utilizes graphics processing units (GPU), gating only needs 15 microseconds per event. Importantly, we also show that GateNet only requires ~10 samples to reach human-level performance, rendering it widely applicable in all domains of flow cytometry.
Complex Recurrent Spectral Network
Authors: Lorenzo Chicchi, Lorenzo Giambagli, Lorenzo Buffoni, Raffaele Marino, Duccio Fanelli
Date: 2023-12-12 00:00:00

This paper presents a novel approach to advancing artificial intelligence (AI) through the development of the Complex Recurrent Spectral Network ($\mathbb{C}$-RSN), an innovative variant of the Recurrent Spectral Network (RSN) model. The $\mathbb{C}$-RSN is designed to address a critical limitation in existing neural network models: their inability to emulate the complex processes of biological neural networks dynamically and accurately. By integrating key concepts from dynamical systems theory and leveraging principles from statistical mechanics, the $\mathbb{C}$-RSN model introduces localized non-linearity, complex fixed eigenvalues, and a distinct separation of memory and input processing functionalities. These features collectively enable the $\mathbb{C}$-RSN evolving towards a dynamic, oscillating final state that more closely mirrors biological cognition. Central to this work is the exploration of how the $\mathbb{C}$-RSN manages to capture the rhythmic, oscillatory dynamics intrinsic to biological systems, thanks to its complex eigenvalue structure and the innovative segregation of its linear and non-linear components. The model's ability to classify data through a time-dependent function, and the localization of information processing, is demonstrated with an empirical evaluation using the MNIST dataset. Remarkably, distinct items supplied as a sequential input yield patterns in time which bear the indirect imprint of the insertion order (and of the time of separation between contiguous insertions).
Forced Exploration in Bandit Problems
Authors: Han Qi, Fei Guo, Li Zhu
Date: 2023-12-12 00:00:00

The multi-armed bandit(MAB) is a classical sequential decision problem. Most work requires assumptions about the reward distribution (e.g., bounded), while practitioners may have difficulty obtaining information about these distributions to design models for their problems, especially in non-stationary MAB problems. This paper aims to design a multi-armed bandit algorithm that can be implemented without using information about the reward distribution while still achieving substantial regret upper bounds. To this end, we propose a novel algorithm alternating between greedy rule and forced exploration. Our method can be applied to Gaussian, Bernoulli and other subgaussian distributions, and its implementation does not require additional information. We employ a unified analysis method for different forced exploration strategies and provide problem-dependent regret upper bounds for stationary and piecewise-stationary settings. Furthermore, we compare our algorithm with popular bandit algorithms on different reward distributions.
Class Probability Matching Using Kernel Methods for Label Shift Adaptation
Authors: Hongwei Wen, Annika Betken, Hanyuan Hang
Date: 2023-12-12 00:00:00
Tasks: Domain Adaptation

In domain adaptation, covariate shift and label shift problems are two distinct and complementary tasks. In covariate shift adaptation where the differences in data distribution arise from variations in feature probabilities, existing approaches naturally address this problem based on \textit{feature probability matching} (\textit{FPM}). However, for label shift adaptation where the differences in data distribution stem solely from variations in class probability, current methods still use FPM on the $d$-dimensional feature space to estimate the class probability ratio on the one-dimensional label space. To address label shift adaptation more naturally and effectively, inspired by a new representation of the source domain's class probability, we propose a new framework called \textit{class probability matching} (\textit{CPM}) which matches two class probability functions on the one-dimensional label space to estimate the class probability ratio, fundamentally different from FPM operating on the $d$-dimensional feature space. Furthermore, by incorporating the kernel logistic regression into the CPM framework to estimate the conditional probability, we propose an algorithm called \textit{class probability matching using kernel methods} (\textit{CPMKM}) for label shift adaptation. From the theoretical perspective, we establish the optimal convergence rates of CPMKM with respect to the cross-entropy loss for multi-class label shift adaptation. From the experimental perspective, comparisons on real datasets demonstrate that CPMKM outperforms existing FPM-based and maximum-likelihood-based algorithms.
Towards Safe Multi-Task Bayesian Optimization
Authors: Jannis O. Lübsen, Christian Hespe, Annika Eichler
Date: 2023-12-12 00:00:00
Tasks: Bayesian Optimization

Bayesian optimization has emerged as a highly effective tool for the safe online optimization of systems, due to its high sample efficiency and noise robustness. To further enhance its efficiency, reduced physical models of the system can be incorporated into the optimization process, accelerating it. These models are able to offer an approximation of the actual system, and evaluating them is significantly cheaper. The similarity between the model and reality is represented by additional hyperparameters, which are learned within the optimization process. Safety is a crucial criterion for online optimization methods such as Bayesian optimization, which has been addressed by recent works that provide safety guarantees under the assumption of known hyperparameters. In practice, however, this does not apply. Therefore, we extend the robust Gaussian process uniform error bounds to meet the multi-task setting, which involves the calculation of a confidence region from the hyperparameter posterior distribution utilizing Markov chain Monte Carlo methods. Subsequently, the robust safety bounds are employed to facilitate the safe optimization of the system, while incorporating measurements of the models. Simulation results indicate that the optimization can be significantly accelerated for expensive to evaluate functions in comparison to other state-of-the-art safe Bayesian optimization methods, contingent on the fidelity of the models.
Identifying Drivers of Predictive Aleatoric Uncertainty
Authors: Pascal Iversen, Simon Witzke, Katharina Baum, Bernhard Y. Renard
Date: 2023-12-12 00:00:00
Tasks: Explainable Artificial Intelligence (XAI), Uncertainty Quantification

Explainability and uncertainty quantification are key to trustable artificial intelligence. However, the reasoning behind uncertainty estimates is generally left unexplained. Identifying the drivers of uncertainty complements explanations of point predictions in recognizing model limitations and enhancing transparent decision-making. So far, explanations of uncertainties have been rarely studied. The few exceptions rely on Bayesian neural networks or technically intricate approaches, such as auxiliary generative models, thereby hindering their broad adoption. We propose a straightforward approach to explain predictive aleatoric uncertainties. We estimate uncertainty in regression as predictive variance by adapting a neural network with a Gaussian output distribution. Subsequently, we apply out-of-the-box explainers to the model's variance output. This approach can explain uncertainty influences more reliably than complex published approaches, which we demonstrate in a synthetic setting with a known data-generating process. We substantiate our findings with a nuanced, quantitative benchmark including synthetic and real, tabular and image datasets. For this, we adapt metrics from conventional XAI research to uncertainty explanations. Overall, the proposed method explains uncertainty estimates with little modifications to the model architecture and outperforms more intricate methods in most settings.