pwc-archive/papers-with-abstracts huggingface dataset:
Anti-Concentrated Confidence Bonuses for Scalable Exploration
Authors: Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham Kakade
Date: 2021-10-21 00:00:00
Proceeding: ICLR 2022 4
Tasks: Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Intrinsic rewards play a central role in handling the exploration-exploitation trade-off when designing sequential decision-making algorithms, in both foundational theory and state-of-the-art deep reinforcement learning. The LinUCB algorithm, a centerpiece of the stochastic linear bandits literature, prescribes an elliptical bonus which addresses the challenge of leveraging shared information in large action spaces. This bonus scheme cannot be directly transferred to high-dimensional exploration problems, however, due to the computational cost of maintaining the inverse covariance matrix of action features. We introduce \emph{anti-concentrated confidence bounds} for efficiently approximating the elliptical bonus, using an ensemble of regressors trained to predict random noise from policy network-derived features. Using this approximation, we obtain stochastic linear bandit algorithms which obtain $\tilde O(d \sqrt{T})$ regret bounds for $\mathrm{poly}(d)$ fixed actions. We develop a practical variant for deep reinforcement learning that is competitive with contemporary intrinsic reward heuristics on Atari benchmarks.
DeLag: Using Multi-Objective Optimization to Enhance the Detection of Latency Degradation Patterns in Service-based Systems
Authors: Luca Traini, Vittorio Cortellessa
Date: 2021-10-21 00:00:00

Performance debugging in production is a fundamental activity in modern service-based systems. The diagnosis of performance issues is often time-consuming, since it requires thorough inspection of large volumes of traces and performance indices. In this paper we present DeLag, a novel automated search-based approach for diagnosing performance issues in service-based systems. DeLag identifies subsets of requests that show, in the combination of their Remote Procedure Call execution times, symptoms of potentially relevant performance issues. We call such symptoms Latency Degradation Patterns. DeLag simultaneously searches for multiple latency degradation patterns while optimizing precision, recall and latency dissimilarity. Experimentation on 700 datasets of requests generated from two microservice-based systems shows that our approach provides better and more stable effectiveness than three state-of-the-art approaches and general purpose machine learning clustering algorithms. DeLag is more effective than all baseline techniques in at least one case study (with p $\leq$ 0.05 and non-negligible effect size). Moreover, DeLag outperforms in terms of efficiency the second and the third most effective baseline techniques on the largest datasets used in our evaluation (up to 22%).
Watermarking Graph Neural Networks based on Backdoor Attacks
Authors: Jing Xu, Stefanos Koffas, Oguzhan Ersoy, Stjepan Picek
Date: 2021-10-21 00:00:00
Tasks: Classification, Graph Classification, Model extraction, Node Classification, Task 2

Graph Neural Networks (GNNs) have achieved promising performance in various real-world applications. Building a powerful GNN model is not a trivial task, as it requires a large amount of training data, powerful computing resources, and human expertise in fine-tuning the model. Moreover, with the development of adversarial attacks, e.g., model stealing attacks, GNNs raise challenges to model authentication. To avoid copyright infringement on GNNs, verifying the ownership of the GNN models is necessary. This paper presents a watermarking framework for GNNs for both graph and node classification tasks. We 1) design two strategies to generate watermarked data for the graph classification task and one for the node classification task, 2) embed the watermark into the host model through training to obtain the watermarked GNN model, and 3) verify the ownership of the suspicious model in a black-box setting. The experiments show that our framework can verify the ownership of GNN models with a very high probability (up to $99\%$) for both tasks. Finally, we experimentally show that our watermarking approach is robust against a state-of-the-art model extraction technique and four state-of-the-art defenses against backdoor attacks.
Learning Time-Varying Graphs from Online Data
Authors: Alberto Natali, Elvin Isufi, Mario Coutino, Geert Leus
Date: 2021-10-21 00:00:00
Tasks: Graph Learning

This work proposes an algorithmic framework to learn time-varying graphs from online data. The generality offered by the framework renders it model-independent, i.e., it can be theoretically analyzed in its abstract formulation and then instantiated under a variety of model-dependent graph learning problems. This is possible by phrasing (time-varying) graph learning as a composite optimization problem, where different functions regulate different desiderata, e.g., data fidelity, sparsity or smoothness. Instrumental for the findings is recognizing that the dependence of the majority (if not all) data-driven graph learning algorithms on the data is exerted through the empirical covariance matrix, representing a sufficient statistic for the estimation problem. Its user-defined recursive update enables the framework to work in non-stationary environments, while iterative algorithms building on novel time-varying optimization tools explicitly take into account the temporal dynamics, speeding up convergence and implicitly including a temporal-regularization of the solution. We specialize the framework to three well-known graph learning models, namely, the Gaussian graphical model (GGM), the structural equation model (SEM), and the smoothness-based model (SBM), where we also introduce ad-hoc vectorization schemes for structured matrices (symmetric, hollows, etc.) which are crucial to perform correct gradient computations, other than enabling to work in low-dimensional vector spaces and hence easing storage requirements. After discussing the theoretical guarantees of the proposed framework, we corroborate it with extensive numerical tests in synthetic and real data.
A Utility Maximization Model of Pedestrian and Driver Interactions
Authors: Yi-Shin Lin, Aravinda Ramakrishnan Srinivasan, Matteo Leonetti, Jac Billington, Gustav Markkula
Date: 2021-10-21 00:00:00

Many models account for the traffic flow of road users but few take the details of local interactions into consideration and how they could deteriorate into safety-critical situations. Building on the concept of sensorimotor control, we develop a modeling framework applying the principles of utility maximization, motor primitives, and intermittent action decisions to account for the details of interactive behaviors among road users. The framework connects these principles to the decision theory and is applied to determine whether such an approach can reproduce the following phenomena: When two pedestrians travel on crossing paths, (a) their interaction is sensitive to initial asymmetries, and (b) based on which, they rapidly resolve collision conflict by adapting their behaviors. When a pedestrian crosses the road while facing an approaching car, (c) either road user yields to the other to resolve their conflict, akin to the pedestrian interaction, and (d) the outcome reveals a specific situational kinematics, associated with the nature of vehicle acceleration. We show that these phenomena emerge naturally from our modeling framework when the model can evolve its parameters as a consequence of the situations. We believe that the modeling framework and phenomenon-centered analysis offer promising tools to understand road user interactions. We conclude with a discussion on how the model can be instrumental in studying the safety-critical situations when including other variables in road-user interactions.
Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments
Authors: Joost Verbraeken, Martijn de Vos, Johan Pouwelse
Date: 2021-10-21 00:00:00
Tasks: Federated Learning, Transfer Learning

Federated learning (FL) is a privacy-friendly type of machine learning where devices locally train a model on their private data and typically communicate model updates with a server. In decentralized FL (DFL), peers communicate model updates with each other instead. However, DFL is challenging since (1) the training data possessed by different peers is often non-i.i.d. (i.e., distributed differently between the peers) and (2) malicious, or Byzantine, attackers can share arbitrary model updates with other peers to subvert the training process. We address these two challenges and present Bristle, middleware between the learning application and the decentralized network layer. Bristle leverages transfer learning to predetermine and freeze the non-output layers of a neural network, significantly speeding up model training and lowering communication costs. To securely update the output layer with model updates from other peers, we design a fast distance-based prioritizer and a novel performance-based integrator. Their combined effect results in high resilience to Byzantine attackers and the ability to handle non-i.i.d. classes. We empirically show that Bristle converges to a consistent 95% accuracy in Byzantine environments, outperforming all evaluated baselines. In non-Byzantine environments, Bristle requires 83% fewer iterations to achieve 90% accuracy compared to state-of-the-art methods. We show that when the training classes are non-i.i.d., Bristle significantly outperforms the accuracy of the most Byzantine-resilient baselines by 2.3x while reducing communication costs by 90%.
Interpretable Machine Learning for Resource Allocation with Application to Ventilator Triage
Authors: Julien Grand-Clément, You Hui Goh, Carri Chan, Vineet Goyal, Elizabeth Chuang
Date: 2021-10-21 00:00:00
Tasks: BIG-bench Machine Learning, Interpretable Machine Learning

Rationing of healthcare resources is a challenging decision that policy makers and providers may be forced to make during a pandemic, natural disaster, or mass casualty event. Well-defined guidelines to triage scarce life-saving resources must be designed to promote transparency, trust, and consistency. To facilitate buy-in and use during high-stress situations, these guidelines need to be interpretable and operational. We propose a novel data-driven model to compute interpretable triage guidelines based on policies for Markov Decision Process that can be represented as simple sequences of decision trees ("tree policies"). In particular, we characterize the properties of optimal tree policies and present an algorithm based on dynamic programming recursions to compute good tree policies. We utilize this methodology to obtain simple, novel triage guidelines for ventilator allocations for COVID-19 patients, based on real patient data from Montefiore hospitals. We also compare the performance of our guidelines to the official New York State guidelines that were developed in 2015 (well before the COVID-19 pandemic). Our empirical study shows that the number of excess deaths associated with ventilator shortages could be reduced significantly using our policy. Our work highlights the limitations of the existing official triage guidelines, which need to be adapted specifically to COVID-19 before being successfully deployed.
Learning OFDM Waveforms with PAPR and ACLR Constraints
Authors: Mathieu Goutay, Fayçal Ait Aoudia, Jakob Hoydis, Jean-Marie Gorce
Date: 2021-10-21 00:00:00

An attractive research direction for future communication systems is the design of new waveforms that can both support high throughputs and present advantageous signal characteristics. Although most modern systems use orthogonal frequency-division multiplexing (OFDM) for its efficient equalization, this waveform suffers from multiple limitations such as a high adjacent channel leakage ratio (ACLR) and high peak-to-average power ratio (PAPR). In this paper, we propose a learning-based method to design OFDM-based waveforms that satisfy selected constraints while maximizing an achievable information rate. To that aim, we model the transmitter and the receiver as convolutional neural networks (CNNs) that respectively implement a high-dimensional modulation scheme and perform the detection of the transmitted bits. This leads to an optimization problem that is solved using the augmented Lagrangian method. Evaluation results show that the end-to-end system is able to satisfy target PAPR and ACLR constraints and allows significant throughput gains compared to a tone reservation (TR) baseline. An additional advantage is that no dedicated pilots are needed.
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness
Authors: Simon Geisler, Johanna Sommer, Jan Schuchardt, Aleksandar Bojchevski, Stephan Günnemann
Date: 2021-10-21 00:00:00
Proceeding: ICLR 2022 4
Tasks: Adversarial Robustness, Combinatorial Optimization

End-to-end (geometric) deep learning has seen first successes in approximating the solution of combinatorial optimization problems. However, generating data in the realm of NP-hard/-complete tasks brings practical and theoretical challenges, resulting in evaluation protocols that are too optimistic. Specifically, most datasets only capture a simpler subproblem and likely suffer from spurious features. We investigate these effects by studying adversarial robustness - a local generalization property - to reveal hard, model-specific instances and spurious features. For this purpose, we derive perturbation models for SAT and TSP. Unlike in other applications, where perturbation models are designed around subjective notions of imperceptibility, our perturbation models are efficient and sound, allowing us to determine the true label of perturbed samples without a solver. Surprisingly, with such perturbations, a sufficiently expressive neural solver does not suffer from the limitations of the accuracy-robustness trade-off common in supervised learning. Although such robust solvers exist, we show empirically that the assessed neural solvers do not generalize well w.r.t. small perturbations of the problem instance.
Reinforcement learning enabled cooperative spectrum sensing in cognitive radio networks
Authors: Wenli Ning, Xiaoyan Huang, Kun Yang, Fan Wu, and Supeng Leng
Date: 2020-03-19 00:00:00
Proceeding: JOURNAL OF COMMUNICATIONS AND NETWORKS 2020 3
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In cognitive radio (CR) networks, fast and accurate spectrum sensing plays a fundamental role in achieving high spectral
efficiency. In this paper, a reinforcement learning (RL) enabled cooperative spectrum sensing scheme is proposed for the secondary
users (SUs) to determine the scanning order of channels and select the partner for cooperative spectrum sensing. By applying Qlearning approach, each SU learns the occupancy pattern of the
primary channels thus forming a dynamic scanning preference list,
so as to reduce the scanning overhead and access delay. To improve
the detection efficiency in dynamic environment, a discounted upper confidence bound (D-UCB) based cooperation partner selection
algorithm is devised wherein each SU learns the time varying detection probability of its neighbors, and selects the one with the potentially highest detection probability as the cooperation partner.
Simulation results demonstrate that the proposed cooperative spectrum sensing scheme achieves significant performance gain over
various reference algorithms in terms of scanning overhead, access
delay, and detection efficiency.
Evolutionary Foundation for Heterogeneity in Risk Aversion
Authors: Yuval Heller, Ilan Nehama
Date: 2021-10-21 00:00:00

We examine the evolutionary basis for risk aversion with respect to aggregate risk. We study populations in which agents face choices between alternatives with different levels of aggregate risk. We show that the choices that maximize the long-run growth rate are induced by a heterogeneous population in which the least and most risk-averse agents are indifferent between facing an aggregate risk and obtaining its linear and harmonic mean for sure, respectively. Moreover, approximately optimal behavior can be induced by a simple distribution according to which all agents have constant relative risk aversion, and the coefficient of relative risk aversion is uniformly distributed between zero and two.
Improving Channel Charting using a Split Triplet Loss and an Inertial Regularizer
Authors: Brian Rappaport, Emre Gönültaş, Jakob Hoydis, Maximilian Arnold, Pavan Koteshwar Srinath, Christoph Studer
Date: 2021-10-21 00:00:00
Tasks: Dimensionality Reduction, Triplet

Channel charting is an emerging technology that enables self-supervised pseudo-localization of user equipments by performing dimensionality reduction on large channel-state information (CSI) databases that are passively collected at infrastructure base stations or access points. In this paper, we introduce a new dimensionality reduction method specifically designed for channel charting using a novel split triplet loss, which utilizes physical information available during the CSI acquisition process. In addition, we propose a novel regularizer that exploits the physical concept of inertia, which significantly improves the quality of the learned channel charts. We provide an experimental verification of our methods using synthetic and real-world measured CSI datasets, and we demonstrate that our methods are able to outperform the state-of-the-art in channel charting based on the triplet loss.
Ambiguities in Direction-of-Arrival Estimation with Linear Arrays
Authors: Frederic Matter, Tobias Fischer, Marius Pesavento, Marc E. Pfetsch
Date: 2021-10-20 00:00:00
Tasks: Direction of Arrival Estimation, parameter estimation

In this paper, we present a novel approach to compute ambiguities in thinned uniform linear arrays, i.e., sparse non-uniform linear arrays, via a mixed-integer program. Ambiguities arise when there exists a set of distinct directions-of-arrival, for which the corresponding steering matrix is rank-deficient and are associated with nonunique parameter estimation. Our approach uses Young tableaux for which a submatrix of the steering matrix has a vanishing determinant, which can be expressed through vanishing sums of unit roots. Each of these vanishing sums then corresponds to an ambiguous set of directions-of-arrival. We derive a method to enumerate such ambiguous sets using a mixed-integer program and present results on several examples.
Distributed Wasserstein Barycenters via Displacement Interpolation
Authors: Pedro Cisneros-Velarde, Francesco Bullo
Date: 2020-12-15 00:00:00
Tasks: Sociology

Consider a multi-agent system whereby each agent has an initial probability measure. In this paper, we propose a distributed algorithm based upon stochastic, asynchronous and pairwise exchange of information and displacement interpolation in the Wasserstein space. We characterize the evolution of this algorithm and prove it computes the Wasserstein barycenter of the initial measures under various conditions. One version of the algorithm computes a standard Wasserstein barycenter, i.e., a barycenter based upon equal weights; and the other version computes a randomized Wasserstein barycenter, i.e., a barycenter based upon random weights for the initial measures. Finally, we specialize our algorithm to Gaussian distributions and draw a connection with the modeling of opinion dynamics in mathematical sociology.
RCT: Random Consistency Training for Semi-supervised Sound Event Detection
Authors: Nian Shao, Erfan Loweimi, Xiaofei Li
Date: 2021-10-21 00:00:00
Tasks: Data Augmentation, Event Detection, Sound Event Detection

Sound event detection (SED), as a core module of acoustic environmental analysis, suffers from the problem of data deficiency. The integration of semi-supervised learning (SSL) largely mitigates such problem while bringing no extra annotation budget. This paper researches on several core modules of SSL, and introduces a random consistency training (RCT) strategy. First, a self-consistency loss is proposed to fuse with the teacher-student model to stabilize the training. Second, a hard mixup data augmentation is proposed to account for the additive property of sounds. Third, a random augmentation scheme is applied to flexibly combine different types of data augmentations. Experiments show that the proposed strategy outperform other widely-used strategies.
One model to enhance them all: array geometry agnostic multi-channel personalized speech enhancement
Authors: Hassan Taherian, Sefik Emre Eskimez, Takuya Yoshioka, Huaming Wang, Zhuo Chen, Xuedong Huang
Date: 2021-10-20 00:00:00
Tasks: All, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition

With the recent surge of video conferencing tools usage, providing high-quality speech signals and accurate captions have become essential to conduct day-to-day business or connect with friends and families. Single-channel personalized speech enhancement (PSE) methods show promising results compared with the unconditional speech enhancement (SE) methods in these scenarios due to their ability to remove interfering speech in addition to the environmental noise. In this work, we leverage spatial information afforded by microphone arrays to improve such systems' performance further. We investigate the relative importance of speaker embeddings and spatial features. Moreover, we propose a new causal array-geometry-agnostic multi-channel PSE model, which can generate a high-quality enhanced signal from arbitrary microphone geometry. Experimental results show that the proposed geometry agnostic model outperforms the model trained on a specific microphone array geometry in both speech quality and automatic speech recognition accuracy. We also demonstrate the effectiveness of the proposed approach for unseen array geometries.
Disentanglement of Emotional Style and Speaker Identity for Expressive Voice Conversion
Authors: Zongyang Du, Berrak Sisman, Kun Zhou, Haizhou Li
Date: 2021-10-20 00:00:00
Tasks: Disentanglement, Voice Conversion

Expressive voice conversion performs identity conversion for emotional speakers by jointly converting speaker identity and emotional style. Due to the hierarchical structure of speech emotion, it is challenging to disentangle the emotional style for different speakers. Inspired by the recent success of speaker disentanglement with variational autoencoder (VAE), we propose an any-to-any expressive voice conversion framework, that is called StyleVC. StyleVC is designed to disentangle linguistic content, speaker identity, pitch, and emotional style information. We study the use of style encoder to model emotional style explicitly. At run-time, StyleVC converts both speaker identity and emotional style for arbitrary speakers. Experiments validate the effectiveness of our proposed framework in both objective and subjective evaluations.