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Title: Diagnostic Tool for Out-of-Sample Model Evaluation Abstract: Assessment of model fitness is an important step in many problems. Models are typically fitted to training data by minimizing a loss function, such as the squared-error or negative log-likelihood, and it is natural to desire low losses on future data. ... |
Title: Multi-task twin support vector machine with Universum data Abstract: Multi-task learning (MTL) has emerged as a promising topic of machine learning in recent years, aiming to enhance the performance of numerous related learning tasks by exploiting beneficial information. During the training phase, most of the ex... |
Title: Defect Prediction Using Stylistic Metrics Abstract: Defect prediction is one of the most popular research topics due to its potential to minimize software quality assurance efforts. Existing approaches have examined defect prediction from various perspectives such as complexity and developer metrics. However, no... |
Title: POGEMA: Partially Observable Grid Environment for Multiple Agents Abstract: We introduce POGEMA (https://github.com/AIRI-Institute/pogema) a sandbox for challenging partially observable multi-agent pathfinding (PO-MAPF) problems . This is a grid-based environment that was specifically designed to be a flexible, ... |
Title: List-Decodable Covariance Estimation Abstract: We give the first polynomial time algorithm for \emph{list-decodable covariance estimation}. For any $\alpha > 0$, our algorithm takes input a sample $Y \subseteq \mathbb{R}^d$ of size $n\geq d^{\mathsf{poly}(1/\alpha)}$ obtained by adversarially corrupting an $(1-\... |
Title: Information Geometry of Dropout Training Abstract: Dropout is one of the most popular regularization techniques in neural network training. Because of its power and simplicity of idea, dropout has been analyzed extensively and many variants have been proposed. In this paper, several properties of dropout are dis... |
Title: A Study on the Evaluation of Generative Models Abstract: Implicit generative models, which do not return likelihood values, such as generative adversarial networks and diffusion models, have become prevalent in recent years. While it is true that these models have shown remarkable results, evaluating their perfo... |
Title: Optimally Weighted Ensembles of Regression Models: Exact Weight Optimization and Applications Abstract: Automated model selection is often proposed to users to choose which machine learning model (or method) to apply to a given regression task. In this paper, we show that combining different regression models ca... |
Title: FairGrad: Fairness Aware Gradient Descent Abstract: We tackle the problem of group fairness in classification, where the objective is to learn models that do not unjustly discriminate against subgroups of the population. Most existing approaches are limited to simple binary tasks or involve difficult to implemen... |
Title: AI-based software for lung nodule detection in chest X-rays -- Time for a second reader approach? Abstract: Objectives: To compare artificial intelligence (AI) as a second reader in detecting lung nodules on chest X-rays (CXR) versus radiologists of two binational institutions, and to evaluate AI performance whe... |
Title: Influence of uncertainty estimation techniques on false-positive reduction in liver lesion detection Abstract: Deep learning techniques show success in detecting objects in medical images, but still suffer from false-positive predictions that may hinder accurate diagnosis. The estimated uncertainty of the neural... |
Title: SpA-Former: Transformer image shadow detection and removal via spatial attention Abstract: In this paper, we propose an end-to-end SpA-Former to recover a shadow-free image from a single shaded image. Unlike traditional methods that require two steps for shadow detection and then shadow removal, the SpA-Former u... |
Title: S2TNet: Spatio-Temporal Transformer Networks for Trajectory Prediction in Autonomous Driving Abstract: To safely and rationally participate in dense and heterogeneous traffic, autonomous vehicles require to sufficiently analyze the motion patterns of surrounding traffic-agents and accurately predict their future... |
Title: How to Combine Variational Bayesian Networks in Federated Learning Abstract: Federated Learning enables multiple data centers to train a central model collaboratively without exposing any confidential data. Even though deterministic models are capable of performing high prediction accuracy, their lack of calibra... |
Title: Optical Flow Regularization of Implicit Neural Representations for Video Frame Interpolation Abstract: Recent works have shown the ability of Implicit Neural Representations (INR) to carry meaningful representations of signal derivatives. In this work, we leverage this property to perform Video Frame Interpolati... |
Title: KiloNeuS: Implicit Neural Representations with Real-Time Global Illumination Abstract: The latest trends in inverse rendering techniques for reconstruction use neural networks to learn 3D representations as neural fields. NeRF-based techniques fit multi-layer perceptrons (MLPs) to a set of training images to est... |
Title: Guided Diffusion Model for Adversarial Purification from Random Noise Abstract: In this paper, we propose a novel guided diffusion purification approach to provide a strong defense against adversarial attacks. Our model achieves 89.62% robust accuracy under PGD-L_inf attack (eps = 8/255) on the CIFAR-10 dataset.... |
Title: Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks Abstract: Bilevel optimization have gained growing interests, with numerous applications found in meta learning, minimax games, reinforcement learning, and nested composition optimization. This paper studies the problem of dis... |
Title: Bregman Power k-Means for Clustering Exponential Family Data Abstract: Recent progress in center-based clustering algorithms combats poor local minima by implicit annealing, using a family of generalized means. These methods are variations of Lloyd's celebrated $k$-means algorithm, and are most appropriate for s... |
Title: Robust Universal Adversarial Perturbations Abstract: Universal Adversarial Perturbations (UAPs) are imperceptible, image-agnostic vectors that cause deep neural networks (DNNs) to misclassify inputs from a data distribution with high probability. Existing methods do not create UAPs robust to transformations, the... |
Title: Play It Cool: Dynamic Shifting Prevents Thermal Throttling Abstract: Machine learning (ML) has entered the mobile era where an enormous number of ML models are deployed on edge devices. However, running common ML models on edge devices continuously may generate excessive heat from the computation, forcing the de... |
Title: DaisyRec 2.0: Benchmarking Recommendation for Rigorous Evaluation Abstract: Recently, one critical issue looms large in the field of recommender systems -- there are no effective benchmarks for rigorous evaluation -- which consequently leads to unreproducible evaluation and unfair comparison. We, therefore, cond... |
Title: Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices Abstract: Federated Learning (FL) is a machine learning paradigm to distributively learn machine learning models from decentralized data that remains on-device. Despite the success of standard Federated optimization methods, ... |
Title: Learning Debiased Classifier with Biased Committee Abstract: Neural networks are prone to be biased towards spurious correlations between classes and latent attributes exhibited in a major portion of training data, which ruins their generalization capability. This paper proposes a new method for training debiase... |
Title: Learning Distribution Grid Topologies: A Tutorial Abstract: Unveiling feeder topologies from data is of paramount importance to advance situational awareness and proper utilization of smart resources in power distribution grids. This tutorial summarizes, contrasts, and establishes useful links between recent wor... |
Title: Robust Bayesian Recourse Abstract: Algorithmic recourse aims to recommend an informative feedback to overturn an unfavorable machine learning decision. We introduce in this paper the Bayesian recourse, a model-agnostic recourse that minimizes the posterior probability odds ratio. Further, we present its min-max ... |
Title: Few-shot Long-Tailed Bird Audio Recognition Abstract: It is easier to hear birds than see them. However, they still play an essential role in nature and are excellent indicators of deteriorating environmental quality and pollution. Recent advances in Machine Learning and Convolutional Neural Networks allow us to... |
Title: Efficient Interdependent Systems Recovery Modeling with DeepONets Abstract: Modeling the recovery of interdependent critical infrastructure is a key component of quantifying and optimizing societal resilience to disruptive events. However, simulating the recovery of large-scale interdependent systems under rando... |
Title: Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming Abstract: Across applications spanning supervised classification and sequential control, deep learning has been reported to find "shortcut" solutions that fail catastrophically under minor changes in the data distribution. In this paper, we show emp... |
Title: $\texttt{FedBC}$: Calibrating Global and Local Models via Federated Learning Beyond Consensus Abstract: In federated learning (FL), the objective of collaboratively learning a global model through aggregation of model updates across devices tends to oppose the goal of personalization via local information. In th... |
Title: Jointist: Joint Learning for Multi-instrument Transcription and Its Applications Abstract: In this paper, we introduce Jointist, an instrument-aware multi-instrument framework that is capable of transcribing, recognizing, and separating multiple musical instruments from an audio clip. Jointist consists of the in... |
Title: Automated Cancer Subtyping via Vector Quantization Mutual Information Maximization Abstract: Cancer subtyping is crucial for understanding the nature of tumors and providing suitable therapy. However, existing labelling methods are medically controversial, and have driven the process of subtyping away from teach... |
Title: Imitation Learning for Generalizable Self-driving Policy with Sim-to-real Transfer Abstract: Imitation Learning uses the demonstrations of an expert to uncover the optimal policy and it is suitable for real-world robotics tasks as well. In this case, however, the training of the agent is carried out in a simulat... |
Title: Multi-Resolution, Multi-Horizon Distributed Solar PV Power Forecasting with Forecast Combinations Abstract: Distributed, small-scale solar photovoltaic (PV) systems are being installed at a rapidly increasing rate. This can cause major impacts on distribution networks and energy markets. As a result, there is a ... |
Title: Scaling Autoregressive Models for Content-Rich Text-to-Image Generation Abstract: We present the Pathways Autoregressive Text-to-Image (Parti) model, which generates high-fidelity photorealistic images and supports content-rich synthesis involving complex compositions and world knowledge. Parti treats text-to-im... |
Title: Generative Pretraining for Black-Box Optimization Abstract: Many problems in science and engineering involve optimizing an expensive black-box function over a high-dimensional space. For such black-box optimization (BBO) problems, we typically assume a small budget for online function evaluations, but also often... |
Title: Federated Latent Class Regression for Hierarchical Data Abstract: Federated Learning (FL) allows a number of agents to participate in training a global machine learning model without disclosing locally stored data. Compared to traditional distributed learning, the heterogeneity (non-IID) of the agents slows down... |
Title: Efficient and effective training of language and graph neural network models Abstract: Can we combine heterogenous graph structure with text to learn high-quality semantic and behavioural representations? Graph neural networks (GNN)s encode numerical node attributes and graph structure to achieve impressive perf... |
Title: On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL Abstract: We study reward-free reinforcement learning (RL) under general non-linear function approximation, and establish sample efficiency and hardness results under various standard structural assumptions. On the positive side, we propos... |
Title: On the Limitations of Elo: Real-World Games, are Transitive, not Additive Abstract: Real-world competitive games, such as chess, go, or StarCraft II, rely on Elo models to measure the strength of their players. Since these games are not fully transitive, using Elo implicitly assumes they have a strong transitive... |
Title: BiometricBlender: Ultra-high dimensional, multi-class synthetic data generator to imitate biometric feature space Abstract: The lack of freely available (real-life or synthetic) high or ultra-high dimensional, multi-class datasets may hamper the rapidly growing research on feature screening, especially in the fi... |
Title: Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning Abstract: Neural operators have gained significant attention recently due to their ability to approximate high-dimensional parametric maps between function spaces. At present, only parametric function ... |
Title: Quantum-Enhanced Selection Operators for Evolutionary Algorithms Abstract: Genetic algorithms have unique properties which are useful when applied to black box optimization. Using selection, crossover, and mutation operators, candidate solutions may be obtained without the need to calculate a gradient. In this w... |
Title: Imitate then Transcend: Multi-Agent Optimal Execution with Dual-Window Denoise PPO Abstract: A novel framework for solving the optimal execution and placement problems using reinforcement learning (RL) with imitation was proposed. The RL agents trained from the proposed framework consistently outperformed the in... |
Title: Sharp Constants in Uniformity Testing via the Huber Statistic Abstract: Uniformity testing is one of the most well-studied problems in property testing, with many known test statistics, including ones based on counting collisions, singletons, and the empirical TV distance. It is known that the optimal sample com... |
Title: Predicting Team Performance with Spatial Temporal Graph Convolutional Networks Abstract: This paper presents a new approach for predicting team performance from the behavioral traces of a set of agents. This spatiotemporal forecasting problem is very relevant to sports analytics challenges such as coaching and o... |
Title: Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management Abstract: Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection. Managing the pressures by controlling injecti... |
Title: Meta Reinforcement Learning with Finite Training Tasks -- a Density Estimation Approach Abstract: In meta reinforcement learning (meta RL), an agent learns from a set of training tasks how to quickly solve a new task, drawn from the same task distribution. The optimal meta RL policy, a.k.a. the Bayes-optimal beh... |
Title: Beyond Uniform Lipschitz Condition in Differentially Private Optimization Abstract: Most prior convergence results on differentially private stochastic gradient descent (DP-SGD) are derived under the simplistic assumption of uniform Lipschitzness, i.e., the per-sample gradients are uniformly bounded. This assump... |
Title: TraSE: Towards Tackling Authorial Style from a Cognitive Science Perspective Abstract: Stylistic analysis of text is a key task in research areas ranging from authorship attribution to forensic analysis and personality profiling. The existing approaches for stylistic analysis are plagued by issues like topic inf... |
Title: Multi-Omic Data Integration and Feature Selection for Survival-based Patient Stratification via Supervised Concrete Autoencoders Abstract: Cancer is a complex disease with significant social and economic impact. Advancements in high-throughput molecular assays and the reduced cost for performing high-quality mul... |
Title: TiCo: Transformation Invariance and Covariance Contrast for Self-Supervised Visual Representation Learning Abstract: We present Transformation Invariance and Covariance Contrast (TiCo) for self-supervised visual representation learning. Similar to other recent self-supervised learning methods, our method is base... |
Title: Performance Prediction Under Dataset Shift Abstract: ML models deployed in production often have to face unknown domain changes, fundamentally different from their training settings. Performance prediction models carry out the crucial task of measuring the impact of these changes on model performance. We study t... |
Title: Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics Abstract: Infectious diseases remain among the top contributors to human illness and death worldwide, among which many diseases produce epidemic waves of infection. The unavailability of specific drugs and ready-to-use vaccines to p... |
Title: A consistent and flexible framework for deep matrix factorizations Abstract: Deep matrix factorizations (deep MFs) are recent unsupervised data mining techniques inspired by constrained low-rank approximations. They aim to extract complex hierarchies of features within high-dimensional datasets. Most of the loss... |
Title: Multi-level Domain Adaptation for Lane Detection Abstract: We focus on bridging domain discrepancy in lane detection among different scenarios to greatly reduce extra annotation and re-training costs for autonomous driving. Critical factors hinder the performance improvement of cross-domain lane detection that c... |
Title: Towards OOD Detection in Graph Classification from Uncertainty Estimation Perspective Abstract: The problem of out-of-distribution detection for graph classification is far from being solved. The existing models tend to be overconfident about OOD examples or completely ignore the detection task. In this work, we... |
Title: Learning Continuous Rotation Canonicalization with Radial Beam Sampling Abstract: Nearly all state of the art vision models are sensitive to image rotations. Existing methods often compensate for missing inductive biases by using augmented training data to learn pseudo-invariances. Alongside the resource demandi... |
Title: Differentially Private Maximal Information Coefficients Abstract: The Maximal Information Coefficient (MIC) is a powerful statistic to identify dependencies between variables. However, it may be applied to sensitive data, and publishing it could leak private information. As a solution, we present algorithms to a... |
Title: Learning Neuro-Symbolic Skills for Bilevel Planning Abstract: Decision-making is challenging in robotics environments with continuous object-centric states, continuous actions, long horizons, and sparse feedback. Hierarchical approaches, such as task and motion planning (TAMP), address these challenges by decomp... |
Title: ConTraNet: A single end-to-end hybrid network for EEG-based and EMG-based human machine interfaces Abstract: Objective: Electroencephalography (EEG) and electromyography (EMG) are two non-invasive bio-signals, which are widely used in human machine interface (HMI) technologies (EEG-HMI and EMG-HMI paradigm) for ... |
Title: On the Maximum Hessian Eigenvalue and Generalization Abstract: The mechanisms by which certain training interventions, such as increasing learning rates and applying batch normalization, improve the generalization of deep networks remains a mystery. Prior works have speculated that "flatter" solutions generalize... |
Title: Sparse Kernel Gaussian Processes through Iterative Charted Refinement (ICR) Abstract: Gaussian Processes (GPs) are highly expressive, probabilistic models. A major limitation is their computational complexity. Naively, exact GP inference requires $\mathcal{O}(N^3)$ computations with $N$ denoting the number of mo... |
Title: Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning Abstract: The combination of Monte Carlo methods and deep learning has recently led to efficient algorithms for solving partial differential equations (PDEs) in high dimensions. Related learning problems are often stated as varia... |
Title: D-CIPHER: Discovery of Closed-form PDEs Abstract: Closed-form differential equations, including partial differential equations and higher-order ordinary differential equations, are one of the most important tools used by scientists to model and better understand natural phenomena. Discovering these equations dir... |
Title: Nimble GNN Embedding with Tensor-Train Decomposition Abstract: This paper describes a new method for representing embedding tables of graph neural networks (GNNs) more compactly via tensor-train (TT) decomposition. We consider the scenario where (a) the graph data that lack node features, thereby requiring the l... |
Title: Gradient-Enhanced Physics-Informed Neural Networks for Power Systems Operational Support Abstract: The application of deep learning methods to speed up the resolution of challenging power flow problems has recently shown very encouraging results. However, power system dynamics are not snap-shot, steady-state ope... |
Title: Solving Constrained Variational Inequalities via an Interior Point Method Abstract: We develop an interior-point approach to solve constrained variational inequality (cVI) problems. Inspired by the efficacy of the alternating direction method of multipliers (ADMM) method in the single-objective context, we gener... |
Title: Controllability of Coarsely Measured Networked Linear Dynamical Systems (Extended Version) Abstract: We consider the controllability of large-scale linear networked dynamical systems when complete knowledge of network structure is unavailable and knowledge is limited to coarse summaries. We provide conditions un... |
Title: Ensembling over Classifiers: a Bias-Variance Perspective Abstract: Ensembles are a straightforward, remarkably effective method for improving the accuracy,calibration, and robustness of models on classification tasks; yet, the reasons that underlie their success remain an active area of research. We build upon t... |
Title: sqSGD: Locally Private and Communication Efficient Federated Learning Abstract: Federated learning (FL) is a technique that trains machine learning models from decentralized data sources. We study FL under local notions of privacy constraints, which provides strong protection against sensitive data disclosures v... |
Title: EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine Abstract: There has been significant progress in developing reinforcement learning (RL) training systems. Past works such as IMPALA, Apex, Seed RL, Sample Factory, and others aim to improve the system's overall throughput. In this pap... |
Title: Scaling up Kernels in 3D CNNs Abstract: Recent advances in 2D CNNs and vision transformers (ViTs) reveal that large kernels are essential for enough receptive fields and high performance. Inspired by this literature, we examine the feasibility and challenges of 3D large-kernel designs. We demonstrate that applyi... |
Title: Uncertainty Quantification for Competency Assessment of Autonomous Agents Abstract: For safe and reliable deployment in the real world, autonomous agents must elicit appropriate levels of trust from human users. One method to build trust is to have agents assess and communicate their own competencies for perform... |
Title: On the effectiveness of persistent homology Abstract: Persistent homology (PH) is one of the most popular methods in Topological Data Analysis. While PH has been used in many different types of applications, the reasons behind its success remain elusive. In particular, it is not known for which classes of proble... |
Title: (Certified!!) Adversarial Robustness for Free! Abstract: In this paper we show how to achieve state-of-the-art certified adversarial robustness to 2-norm bounded perturbations by relying exclusively on off-the-shelf pretrained models. To do so, we instantiate the denoised smoothing approach of Salman et al. by c... |
Title: Faster Diffusion Cardiac MRI with Deep Learning-based breath hold reduction Abstract: Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) enables us to probe the microstructural arrangement of cardiomyocytes within the myocardium in vivo and non-invasively, which no other imaging modality allows. This innovativ... |
Title: Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery Abstract: This paper revisits datasets and evaluation criteria for Symbolic Regression, a task of expressing given data using mathematical equations, specifically focused on its potential for scientific discovery. Focused on a set of... |
Title: EpiGRAF: Rethinking training of 3D GANs Abstract: A very recent trend in generative modeling is building 3D-aware generators from 2D image collections. To induce the 3D bias, such models typically rely on volumetric rendering, which is expensive to employ at high resolutions. During the past months, there appear... |
Title: Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control Abstract: Learned models and policies can generalize effectively when evaluated within the distribution of the training data, but can produce unpredictable and erroneous outputs on out-of-distribution inputs. In order to avoid dis... |
Title: Learning to Estimate and Refine Fluid Motion with Physical Dynamics Abstract: Extracting information on fluid motion directly from images is challenging. Fluid flow represents a complex dynamic system governed by the Navier-Stokes equations. General optical flow methods are typically designed for rigid body moti... |
Title: Policy learning with asymmetric utilities Abstract: Data-driven decision making plays an important role even in high stakes settings like medicine and public policy. Learning optimal policies from observed data requires a careful formulation of the utility function whose expected value is maximized across a popu... |
Title: Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee Abstract: Kernel survival analysis models estimate individual survival distributions with the help of a kernel function, which measures the similarity between any two data points. Such a kernel function can be l... |
Title: The Privacy Onion Effect: Memorization is Relative Abstract: Machine learning models trained on private datasets have been shown to leak their private data. While recent work has found that the average data point is rarely leaked, the outlier samples are frequently subject to memorization and, consequently, priv... |
Title: The Digital Twin Landscape at the Crossroads of Predictive Maintenance, Machine Learning and Physics Based Modeling Abstract: The concept of a digital twin has exploded in popularity over the past decade, yet confusion around its plurality of definitions, its novelty as a new technology, and its practical applic... |
Title: Shifted Compression Framework: Generalizations and Improvements Abstract: Communication is one of the key bottlenecks in the distributed training of large-scale machine learning models, and lossy compression of exchanged information, such as stochastic gradients or models, is one of the most effective instrument... |
Title: Winning the Lottery Ahead of Time: Efficient Early Network Pruning Abstract: Pruning, the task of sparsifying deep neural networks, received increasing attention recently. Although state-of-the-art pruning methods extract highly sparse models, they neglect two main challenges: (1) the process of finding these sp... |
Title: Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive Learning Abstract: We study offline meta-reinforcement learning, a practical reinforcement learning paradigm that learns from offline data to adapt to new tasks. The distribution of offline data is determined jointly by the behav... |
Title: Model Joins: Enabling Analytics Over Joins of Absent Big Tables Abstract: This work is motivated by two key facts. First, it is highly desirable to be able to learn and perform knowledge discovery and analytics (LKD) tasks without the need to access raw-data tables. This may be due to organizations finding it in... |
Title: Plug and Play Counterfactual Text Generation for Model Robustness Abstract: Generating counterfactual test-cases is an important backbone for testing NLP models and making them as robust and reliable as traditional software. In generating the test-cases, a desired property is the ability to control the test-case... |
Title: A Single-Timescale Analysis For Stochastic Approximation With Multiple Coupled Sequences Abstract: Stochastic approximation (SA) with multiple coupled sequences has found broad applications in machine learning such as bilevel learning and reinforcement learning (RL). In this paper, we study the finite-time conve... |
Title: CoCoPIE XGen: A Full-Stack AI-Oriented Optimizing Framework Abstract: There is a growing demand for shifting the delivery of AI capability from data centers on the cloud to edge or end devices, exemplified by the fast emerging real-time AI-based apps running on smartphones, AR/VR devices, autonomous vehicles, an... |
Title: WrapperFL: A Model Agnostic Plug-in for Industrial Federated Learning Abstract: Federated learning, as a privacy-preserving collaborative machine learning paradigm, has been gaining more and more attention in the industry. With the huge rise in demand, there have been many federated learning platforms that allow... |
Title: Neural Moving Horizon Estimation for Robust Flight Control Abstract: Estimating and reacting to external disturbances is crucial for robust flight control of quadrotors. Existing estimators typically require significant tuning for a specific flight scenario or training with extensive real-world data to achieve s... |
Title: TabText: a Systematic Approach to Aggregate Knowledge Across Tabular Data Structures Abstract: Processing and analyzing tabular data in a productive and efficient way is essential for building successful applications of machine learning in fields such as healthcare. However, the lack of a unified framework for r... |
Title: An Energy and Carbon Footprint Analysis of Distributed and Federated Learning Abstract: Classical and centralized Artificial Intelligence (AI) methods require moving data from producers (sensors, machines) to energy hungry data centers, raising environmental concerns due to computational and communication resour... |
Title: Supervised learning of random quantum circuits via scalable neural networks Abstract: Predicting the output of quantum circuits is a hard computational task that plays a pivotal role in the development of universal quantum computers. Here we investigate the supervised learning of output expectation values of ran... |
Title: Machine Learning Prescriptive Canvas for Optimizing Business Outcomes Abstract: Data science has the potential to improve business in a variety of verticals. While the lion's share of data science projects uses a predictive approach, to drive improvements these predictions should become decisions. However, such ... |
Title: Marginal Tail-Adaptive Normalizing Flows Abstract: Learning the tail behavior of a distribution is a notoriously difficult problem. By definition, the number of samples from the tail is small, and deep generative models, such as normalizing flows, tend to concentrate on learning the body of the distribution. In ... |
Title: Dynamic Reserve Price Design for Lazada Sponsored Search Abstract: In ecommerce platform, users will be less likely to use organic search if sponsored search shows them unexpected advertising items, which will be a hidden cost for the platform. In order to incorporate the hidden cost into auction mechanism which... |
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