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Title: cMelGAN: An Efficient Conditional Generative Model Based on Mel Spectrograms Abstract: Analysing music in the field of machine learning is a very difficult problem with numerous constraints to consider. The nature of audio data, with its very high dimensionality and widely varying scales of structure, is one of ... |
Title: Developing patient-driven artificial intelligence based on personal rankings of care decision making steps Abstract: We propose and experimentally motivate a new methodology to support decision-making processes in healthcare with artificial intelligence based on personal rankings of care decision making steps th... |
Title: Parameter Adaptation for Joint Distribution Shifts Abstract: While different methods exist to tackle distinct types of distribution shift, such as label shift (in the form of adversarial attacks) or domain shift, tackling the joint shift setting is still an open problem. Through the study of a joint distribution... |
Title: Generalization Bounds on Multi-Kernel Learning with Mixed Datasets Abstract: This paper presents novel generalization bounds for the multi-kernel learning problem. Motivated by applications in sensor networks, we assume that the dataset is mixed where each sample is taken from a finite pool of Markov chains. Our... |
Title: COIN: Communication-Aware In-Memory Acceleration for Graph Convolutional Networks Abstract: Graph convolutional networks (GCNs) have shown remarkable learning capabilities when processing graph-structured data found inherently in many application areas. GCNs distribute the outputs of neural networks embedded in ... |
Title: 3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design Abstract: Deep learning has achieved tremendous success in designing novel chemical compounds with desirable pharmaceutical properties. In this work, we focus on a new type of drug design problem -- generating a small "linker" to p... |
Title: Finding Global Homophily in Graph Neural Networks When Meeting Heterophily Abstract: We investigate graph neural networks on graphs with heterophily. Some existing methods amplify a node's neighborhood with multi-hop neighbors to include more nodes with homophily. However, it is a significant challenge to set pe... |
Title: Optimization of Decision Tree Evaluation Using SIMD Instructions Abstract: Decision forest (decision tree ensemble) is one of the most popular machine learning algorithms. To use large models on big data, like document scoring with learning-to-rank models, we need to evaluate these models efficiently. In this pa... |
Title: Posterior Probability Matters: Doubly-Adaptive Calibration for Neural Predictions in Online Advertising Abstract: Predicting user response probabilities is vital for ad ranking and bidding. We hope that predictive models can produce accurate probabilistic predictions that reflect true likelihoods. Calibration te... |
Title: A Computational Framework of Cortical Microcircuits Approximates Sign-concordant Random Backpropagation Abstract: Several recent studies attempt to address the biological implausibility of the well-known backpropagation (BP) method. While promising methods such as feedback alignment, direct feedback alignment, a... |
Title: A Note on the Chernoff Bound for Random Variables in the Unit Interval Abstract: The Chernoff bound is a well-known tool for obtaining a high probability bound on the expectation of a Bernoulli random variable in terms of its sample average. This bound is commonly used in statistical learning theory to upper bou... |
Title: Exploiting the Relationship Between Kendall's Rank Correlation and Cosine Similarity for Attribution Protection Abstract: Model attributions are important in deep neural networks as they aid practitioners in understanding the models, but recent studies reveal that attributions can be easily perturbed by adding i... |
Title: Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations Abstract: As post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to ensure that the quality of the resulting explanations is consistent... |
Title: Classifiers are Better Experts for Controllable Text Generation Abstract: This paper proposes a simple method for controllable text generation based on weighting logits produced, namely CAIF sampling. Using an arbitrary third-party text classifier, we adjust a small part of a language model's logits and guide te... |
Title: Supervised Learning and Model Analysis with Compositional Data Abstract: The compositionality and sparsity of high-throughput sequencing data poses a challenge for regression and classification. However, in microbiome research in particular, conditional modeling is an essential tool to investigate relationships ... |
Title: Textual Explanations and Critiques in Recommendation Systems Abstract: Artificial intelligence and machine learning algorithms have become ubiquitous. Although they offer a wide range of benefits, their adoption in decision-critical fields is limited by their lack of interpretability, particularly with textual d... |
Title: Discovering the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions Abstract: Most graph neural networks (GNNs) rely on the message passing paradigm to propagate node features and build interactions. Recent works point out that different graph learning tasks require different ranges ... |
Title: Topic Modelling on Consumer Financial Protection Bureau Data: An Approach Using BERT Based Embeddings Abstract: Customers' reviews and comments are important for businesses to understand users' sentiment about the products and services. However, this data needs to be analyzed to assess the sentiment associated w... |
Title: Not to Overfit or Underfit? A Study of Domain Generalization in Question Answering Abstract: Machine learning models are prone to overfitting their source (training) distributions, which is commonly believed to be why they falter in novel target domains. Here we examine the contrasting view that multi-source dom... |
Title: Evaluating Independence and Conditional Independence Measures Abstract: Independence and Conditional Independence (CI) are two fundamental concepts in probability and statistics, which can be applied to solve many central problems of statistical inference. There are many existing independence and CI measures def... |
Title: Reliable Offline Model-based Optimization for Industrial Process Control Abstract: In the research area of offline model-based optimization, novel and promising methods are frequently developed. However, implementing such methods in real-world industrial systems such as production lines for process control is of... |
Title: Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets Abstract: Deep generative models have achieved tremendous success in designing novel drug molecules in recent years. A new thread of works have shown the great potential in advancing the specificity and success rate of in silico drug design by ... |
Title: Learning Shared Kernel Models: the Shared Kernel EM algorithm Abstract: Expectation maximisation (EM) is an unsupervised learning method for estimating the parameters of a finite mixture distribution. It works by introducing "hidden" or "latent" variables via Baum's auxiliary function $Q$ that allow the joint da... |
Title: FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning Abstract: Pseudo labeling and consistency regularization approaches based on confidence thresholding have made great progress in semi-supervised learning (SSL). However, we argue that existing methods might fail to adopt suitable thresholds since... |
Title: Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review Abstract: This review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes t... |
Title: Clinical outcome prediction under hypothetical interventions -- a representation learning framework for counterfactual reasoning Abstract: Most machine learning (ML) models are developed for prediction only; offering no option for causal interpretation of their predictions or parameters/properties. This can hamp... |
Title: RoMFAC: A Robust Mean-Field Actor-Critic Reinforcement Learning against Adversarial Perturbations on States Abstract: Deep reinforcement learning methods for multi-agent systems make optimal decisions dependent on states observed by agents, but a little uncertainty on the observations can possibly mislead agents... |
Title: Sample-Efficient Learning of Correlated Equilibria in Extensive-Form Games Abstract: Imperfect-Information Extensive-Form Games (IIEFGs) is a prevalent model for real-world games involving imperfect information and sequential plays. The Extensive-Form Correlated Equilibrium (EFCE) has been proposed as a natural ... |
Title: Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback Abstract: Motivated by applications to online learning in sparse estimation and Bayesian optimization, we consider the problem of online unconstrained nonsubmodular minimization with delayed costs in both full informat... |
Title: Federated learning for LEO constellations via inter-HAP links Abstract: Low Earth Obit (LEO) satellite constellations have seen a sharp increase of deployment in recent years, due to their distinctive capabilities of providing broadband Internet access and enabling global data acquisition as well as large-scale ... |
Title: Towards a Comprehensive Solution for a Vision-based Digitized Neurological Examination Abstract: The ability to use digitally recorded and quantified neurological exam information is important to help healthcare systems deliver better care, in-person and via telehealth, as they compensate for a growing shortage ... |
Title: A comparison of PINN approaches for drift-diffusion equations on metric graphs Abstract: In this paper we focus on comparing machine learning approaches for quantum graphs, which are metric graphs, i.e., graphs with dedicated edge lengths, and an associated differential operator. In our case the differential equ... |
Title: Fair Bayes-Optimal Classifiers Under Predictive Parity Abstract: Increasing concerns about disparate effects of AI have motivated a great deal of work on fair machine learning. Existing works mainly focus on independence- and separation-based measures (e.g., demographic parity, equality of opportunity, equalized... |
Title: Sparsity-Aware Robust Normalized Subband Adaptive Filtering algorithms based on Alternating Optimization Abstract: This paper proposes a unified sparsity-aware robust normalized subband adaptive filtering (SA-RNSAF) algorithm for identification of sparse systems under impulsive noise. The proposed SA-RNSAF algor... |
Title: Proxyless Neural Architecture Adaptation for Supervised Learning and Self-Supervised Learning Abstract: Recently, Neural Architecture Search (NAS) methods have been introduced and show impressive performance on many benchmarks. Among those NAS studies, Neural Architecture Transformer (NAT) aims to adapt the give... |
Title: Evaluating Generalizability of Fine-Tuned Models for Fake News Detection Abstract: The Covid-19 pandemic has caused a dramatic and parallel rise in dangerous misinformation, denoted an `infodemic' by the CDC and WHO. Misinformation tied to the Covid-19 infodemic changes continuously; this can lead to performance... |
Title: Interpretable Stochastic Model Predictive Control using Distributional Reinforced Estimation for Quadrotor Tracking Systems Abstract: This paper presents a novel trajectory tracker for autonomous quadrotor navigation in dynamic and complex environments. The proposed framework integrates a distributional Reinforc... |
Title: Trajectory Inference via Mean-field Langevin in Path Space Abstract: Trajectory inference aims at recovering the dynamics of a population from snapshots of its temporal marginals. To solve this task, a min-entropy estimator relative to the Wiener measure in path space was introduced by Lavenant et al. arXiv:2102... |
Title: BackLink: Supervised Local Training with Backward Links Abstract: Empowered by the backpropagation (BP) algorithm, deep neural networks have dominated the race in solving various cognitive tasks. The restricted training pattern in the standard BP requires end-to-end error propagation, causing large memory cost a... |
Title: Breaking with Fixed Set Pathology Recognition through Report-Guided Contrastive Training Abstract: When reading images, radiologists generate text reports describing the findings therein. Current state-of-the-art computer-aided diagnosis tools utilize a fixed set of predefined categories automatically extracted ... |
Title: Classification of Astronomical Bodies by Efficient Layer Fine-Tuning of Deep Neural Networks Abstract: The SDSS-IV dataset contains information about various astronomical bodies such as Galaxies, Stars, and Quasars captured by observatories. Inspired by our work on deep multimodal learning, which utilized transf... |
Title: Revisiting Facial Key Point Detection: An Efficient Approach Using Deep Neural Networks Abstract: Facial landmark detection is a widely researched field of deep learning as this has a wide range of applications in many fields. These key points are distinguishing characteristic points on the face, such as the eye... |
Title: Efficient Deep Learning Methods for Identification of Defective Casting Products Abstract: Quality inspection has become crucial in any large-scale manufacturing industry recently. In order to reduce human error, it has become imperative to use efficient and low computational AI algorithms to identify such defec... |
Title: Practical Insights of Repairing Model Problems on Image Classification Abstract: Additional training of a deep learning model can cause negative effects on the results, turning an initially positive sample into a negative one (degradation). Such degradation is possible in real-world use cases due to the diversit... |
Title: SystemMatch: optimizing preclinical drug models to human clinical outcomes via generative latent-space matching Abstract: Translating the relevance of preclinical models ($\textit{in vitro}$, animal models, or organoids) to their relevance in humans presents an important challenge during drug development. The ri... |
Title: Unsupervised Abnormal Traffic Detection through Topological Flow Analysis Abstract: Cyberthreats are a permanent concern in our modern technological world. In the recent years, sophisticated traffic analysis techniques and anomaly detection (AD) algorithms have been employed to face the more and more subversive ... |
Title: Robust Regularized Low-Rank Matrix Models for Regression and Classification Abstract: While matrix variate regression models have been studied in many existing works, classical statistical and computational methods for the analysis of the regression coefficient estimation are highly affected by high dimensional ... |
Title: A Comprehensive Survey on Model Quantization for Deep Neural Networks Abstract: Recent advances in machine learning by deep neural networks are significant. But using these networks has been accompanied by a huge number of parameters for storage and computations that leads to an increase in the hardware cost and... |
Title: Spiking Approximations of the MaxPooling Operation in Deep SNNs Abstract: Spiking Neural Networks (SNNs) are an emerging domain of biologically inspired neural networks that have shown promise for low-power AI. A number of methods exist for building deep SNNs, with Artificial Neural Network (ANN)-to-SNN conversi... |
Title: A Learning Approach for Joint Design of Event-triggered Control and Power-Efficient Resource Allocation Abstract: In emerging Industrial Cyber-Physical Systems (ICPSs), the joint design of communication and control sub-systems is essential, as these sub-systems are interconnected. In this paper, we study the joi... |
Title: MIND: Maximum Mutual Information Based Neural Decoder Abstract: We are assisting at a growing interest in the development of learning architectures with application to digital communication systems. Herein, we consider the detection/decoding problem. We aim at developing an optimal neural architecture for such a... |
Title: GAN-Aimbots: Using Machine Learning for Cheating in First Person Shooters Abstract: Playing games with cheaters is not fun, and in a multi-billion-dollar video game industry with hundreds of millions of players, game developers aim to improve the security and, consequently, the user experience of their games by ... |
Title: Generalization error bounds for DECONET: a deep unfolding network for analysis Compressive Sensing Abstract: In this paper, we propose a new deep unfolding neural network -- based on a state-of-the-art optimization algorithm -- for analysis Compressed Sensing. The proposed network called Decoding Network (DECONE... |
Title: Fake News Quick Detection on Dynamic Heterogeneous Information Networks Abstract: The spread of fake news has caused great harm to society in recent years. So the quick detection of fake news has become an important task. Some current detection methods often model news articles and other related components as a ... |
Title: High Performance of Gradient Boosting in Binding Affinity Prediction Abstract: Prediction of protein-ligand (PL) binding affinity remains the key to drug discovery. Popular approaches in recent years involve graph neural networks (GNNs), which are used to learn the topology and geometry of PL complexes. However,... |
Title: Cliff Diving: Exploring Reward Surfaces in Reinforcement Learning Environments Abstract: Visualizing optimization landscapes has led to many fundamental insights in numeric optimization, and novel improvements to optimization techniques. However, visualizations of the objective that reinforcement learning optimi... |
Title: SaiNet: Stereo aware inpainting behind objects with generative networks Abstract: In this work, we present an end-to-end network for stereo-consistent image inpainting with the objective of inpainting large missing regions behind objects. The proposed model consists of an edge-guided UNet-like network using Part... |
Title: Integration of Text and Graph-based Features for Detecting Mental Health Disorders from Voice Abstract: With the availability of voice-enabled devices such as smart phones, mental health disorders could be detected and treated earlier, particularly post-pandemic. The current methods involve extracting features d... |
Title: PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning Abstract: In this work, we present a reinforcement learning (RL) based approach to designing parallel prefix circuits such as adders or priority encoders that are fundamental to high-performance digital design. Unlike prior meth... |
Title: Verifying Neural Networks Against Backdoor Attacks Abstract: Neural networks have achieved state-of-the-art performance in solving many problems, including many applications in safety/security-critical systems. Researchers also discovered multiple security issues associated with neural networks. One of them is b... |
Title: RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL Abstract: Relational structures such as schema linking and schema encoding have been validated as a key component to qualitatively translating natural language into SQL queries. However, introducing these structural relations ... |
Title: QHD: A brain-inspired hyperdimensional reinforcement learning algorithm Abstract: Reinforcement Learning (RL) has opened up new opportunities to solve a wide range of complex decision-making tasks. However, modern RL algorithms, e.g., Deep Q-Learning, are based on deep neural networks, putting high computational... |
Title: RiCS: A 2D Self-Occlusion Map for Harmonizing Volumetric Objects Abstract: There have been remarkable successes in computer vision with deep learning. While such breakthroughs show robust performance, there have still been many challenges in learning in-depth knowledge, like occlusion or predicting physical inte... |
Title: Mask CycleGAN: Unpaired Multi-modal Domain Translation with Interpretable Latent Variable Abstract: We propose Mask CycleGAN, a novel architecture for unpaired image domain translation built based on CycleGAN, with an aim to address two issues: 1) unimodality in image translation and 2) lack of interpretability ... |
Title: No-regret learning for repeated non-cooperative games with lossy bandits Abstract: This paper considers no-regret learning for repeated continuous-kernel games with lossy bandit feedback. Since it is difficult to give the explicit model of the utility functions in dynamic environments, the players' action can on... |
Title: Improved Consistency Training for Semi-Supervised Sequence-to-Sequence ASR via Speech Chain Reconstruction and Self-Transcribing Abstract: Consistency regularization has recently been applied to semi-supervised sequence-to-sequence (S2S) automatic speech recognition (ASR). This principle encourages an ASR model ... |
Title: Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data Abstract: Physics-informed extreme learning machine (PIELM) has recently received significant attention as a rapid version of physics-informed neural network (PINN) for solving partial differential equations (... |
Title: Unified Distributed Environment Abstract: We propose Unified Distributed Environment (UDE), an environment virtualization toolkit for reinforcement learning research. UDE is designed to integrate environments built on any simulation platform such as Gazebo, Unity, Unreal, and OpenAI Gym. Through environment virt... |
Title: Efficient Learning of Interpretable Classification Rules Abstract: Machine learning has become omnipresent with applications in various safety-critical domains such as medical, law, and transportation. In these domains, high-stake decisions provided by machine learning necessitate researchers to design interpret... |
Title: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps Abstract: In this paper, we present DendroMap, a novel approach to interactively exploring large-scale image datasets for machine learning. Machine learning practitioners often explore image datasets by generating a grid of image... |
Title: Toward a Geometrical Understanding of Self-supervised Contrastive Learning Abstract: Self-supervised learning (SSL) is currently one of the premier techniques to create data representations that are actionable for transfer learning in the absence of human annotations. Despite their success, the underlying geomet... |
Title: A Tale of Two Flows: Cooperative Learning of Langevin Flow and Normalizing Flow Toward Energy-Based Model Abstract: This paper studies the cooperative learning of two generative flow models, in which the two models are iteratively updated based on the jointly synthesized examples. The first flow model is a norma... |
Title: Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits Abstract: Recent years have seen the development of many open-source ML fairness toolkits aimed at helping ML practitioners assess and address unfairness in their systems. However, there has been little research investigating how ML prac... |
Title: Beyond General Purpose Machine Translation: The Need for Context-specific Empirical Research to Design for Appropriate User Trust Abstract: Machine Translation (MT) has the potential to help people overcome language barriers and is widely used in high-stakes scenarios, such as in hospitals. However, in order to ... |
Title: Representation learning with function call graph transformations for malware open set recognition Abstract: Open set recognition (OSR) problem has been a challenge in many machine learning (ML) applications, such as security. As new/unknown malware families occur regularly, it is difficult to exhaust samples tha... |
Title: Formal limitations of sample-wise information-theoretic generalization bounds Abstract: Some of the tightest information-theoretic generalization bounds depend on the average information between the learned hypothesis and a \emph{single} training example. However, these sample-wise bounds were derived only for \... |
Title: Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds Abstract: Executing safe and precise flight maneuvers in dynamic high-speed winds is important for the ongoing commoditization of uninhabited aerial vehicles (UAVs). However, because the relationship between various wind conditions and its effect... |
Title: Multimodal Conversational AI: A Survey of Datasets and Approaches Abstract: As humans, we experience the world with all our senses or modalities (sound, sight, touch, smell, and taste). We use these modalities, particularly sight and touch, to convey and interpret specific meanings. Multimodal expressions are ce... |
Title: Structural Dropout for Model Width Compression Abstract: Existing ML models are known to be highly over-parametrized, and use significantly more resources than required for a given task. Prior work has explored compressing models offline, such as by distilling knowledge from larger models into much smaller ones.... |
Title: Perspectives on Incorporating Expert Feedback into Model Updates Abstract: Machine learning (ML) practitioners are increasingly tasked with developing models that are aligned with non-technical experts' values and goals. However, there has been insufficient consideration on how practitioners should translate dom... |
Title: Developing a Production System for Purpose of Call Detection in Business Phone Conversations Abstract: For agents at a contact centre receiving calls, the most important piece of information is the reason for a given call. An agent cannot provide support on a call if they do not know why a customer is calling. I... |
Title: Universal Post-Training Backdoor Detection Abstract: A Backdoor attack (BA) is an important type of adversarial attack against deep neural network classifiers, wherein test samples from one or more source classes will be (mis)classified to the attacker's target class when a backdoor pattern (BP) is embedded. In ... |
Title: Differentiable programming: Generalization, characterization and limitations of deep learning Abstract: In the past years, deep learning models have been successfully applied in several cognitive tasks. Originally inspired by neuroscience, these models are specific examples of differentiable programs. In this pa... |
Title: Robustness of Control Design via Bayesian Learning Abstract: In the realm of supervised learning, Bayesian learning has shown robust predictive capabilities under input and parameter perturbations. Inspired by these findings, we demonstrate the robustness properties of Bayesian learning in the control search tas... |
Title: Large-Scale Sequential Learning for Recommender and Engineering Systems Abstract: In this thesis, we focus on the design of an automatic algorithms that provide personalized ranking by adapting to the current conditions. To demonstrate the empirical efficiency of the proposed approaches we investigate their appl... |
Title: AVCAffe: A Large Scale Audio-Visual Dataset of Cognitive Load and Affect for Remote Work Abstract: We introduce AVCAffe, the first Audio-Visual dataset consisting of Cognitive load and Affect attributes. We record AVCAffe by simulating remote work scenarios over a video-conferencing platform, where subjects coll... |
Title: A Huber loss-based super learner with applications to healthcare expenditures Abstract: Complex distributions of the healthcare expenditure pose challenges to statistical modeling via a single model. Super learning, an ensemble method that combines a range of candidate models, is a promising alternative for cost... |
Title: Sentiment Analysis of Covid-related Reddits Abstract: This paper focuses on Sentiment Analysis of Covid-19 related messages from the r/Canada and r/Unitedkingdom subreddits of Reddit. We apply manual annotation and three Machine Learning algorithms to analyze sentiments conveyed in those messages. We use VADER a... |
Title: From Images to Probabilistic Anatomical Shapes: A Deep Variational Bottleneck Approach Abstract: Statistical shape modeling (SSM) directly from 3D medical images is an underutilized tool for detecting pathology, diagnosing disease, and conducting population-level morphology analysis. Deep learning frameworks hav... |
Title: Physics guided neural networks for modelling of non-linear dynamics Abstract: The success of the current wave of artificial intelligence can be partly attributed to deep neural networks, which have proven to be very effective in learning complex patterns from large datasets with minimal human intervention. Howev... |
Title: Fault Detection for Non-Condensing Boilers using Simulated Building Automation System Sensor Data Abstract: Building performance has been shown to degrade significantly after commissioning, resulting in increased energy consumption and associated greenhouse gas emissions. Continuous Commissioning using existing ... |
Title: Optimal Parameter-free Online Learning with Switching Cost Abstract: Parameter-freeness in online learning refers to the adaptivity of an algorithm with respect to the optimal decision in hindsight. In this paper, we design such algorithms in the presence of switching cost - the latter penalizes the optimistic u... |
Title: Multi-variant COVID-19 model with heterogeneous transmission rates using deep neural networks Abstract: Mutating variants of COVID-19 have been reported across many US states since 2021. In the fight against COVID-19, it has become imperative to study the heterogeneity in the time-varying transmission rates for ... |
Title: Principal-Agent Hypothesis Testing Abstract: Consider the relationship between the FDA (the principal) and a pharmaceutical company (the agent). The pharmaceutical company wishes to sell a product to make a profit, and the FDA wishes to ensure that only efficacious drugs are released to the public. The efficacy ... |
Title: Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions Abstract: We study the linear contextual bandit problem in the presence of adversarial corruption, where the reward at each round is corrupted by an adversary, and the corruption level (i.e., the sum of corruption magnitudes ove... |
Title: Distributed Transmission Control for Wireless Networks using Multi-Agent Reinforcement Learning Abstract: We examine the problem of transmission control, i.e., when to transmit, in distributed wireless communications networks through the lens of multi-agent reinforcement learning. Most other works using reinforc... |
Title: The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, & Mosquitoes Abstract: The ACM Multimedia 2022 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Vocalis... |
Title: Sharp Asymptotics of Kernel Ridge Regression Beyond the Linear Regime Abstract: The generalization performance of kernel ridge regression (KRR) exhibits a multi-phased pattern that crucially depends on the scaling relationship between the sample size $n$ and the underlying dimension $d$. This phenomenon is due t... |
Title: Multiple Domain Causal Networks Abstract: Observational studies are regarded as economic alternatives to randomized trials, often used in their stead to investigate and determine treatment efficacy. Due to lack of sample size, observational studies commonly combine data from multiple sources or different sites/c... |
Title: Machine learning methods for Schlieren imaging of a plasma channel in tenuous atomic vapor Abstract: We investigate the usage of a Schlieren imaging setup to measure the geometrical dimensions of a plasma channel in atomic vapor. Near resonant probe light is used to image the plasma channel in a tenuous vapor an... |
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