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Title: Optimal Algorithms for Mean Estimation under Local Differential Privacy Abstract: We study the problem of mean estimation of $\ell_2$-bounded vectors under the constraint of local differential privacy. While the literature has a variety of algorithms that achieve the asymptotically optimal rates for this problem... |
Title: KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning Abstract: While hyper-parameters (HPs) are important for knowledge graph (KG) learning, existing methods fail to search them efficiently. To solve this problem, we first analyze the properties of different HPs and measure the transfer ability... |
Title: Soft and Hard Constrained Parametric Generative Schemes for Encoding and Synthesizing Airfoils Abstract: Traditional airfoil parametric technique has significant limitation in modern aerodynamic optimization design.There is a strong demand for developing a parametric method with good intuitiveness, flexibility a... |
Title: COGMEN: COntextualized GNN based Multimodal Emotion recognitioN Abstract: Emotions are an inherent part of human interactions, and consequently, it is imperative to develop AI systems that understand and recognize human emotions. During a conversation involving various people, a person's emotions are influenced ... |
Title: Assistive Recipe Editing through Critiquing Abstract: There has recently been growing interest in the automatic generation of cooking recipes that satisfy some form of dietary restrictions, thanks in part to the availability of online recipe data. Prior studies have used pre-trained language models, or relied on... |
Title: Learning to Solve Vehicle Routing Problems: A Survey Abstract: This paper provides a systematic overview of machine learning methods applied to solve NP-hard Vehicle Routing Problems (VRPs). Recently, there has been a great interest from both machine learning and operations research communities to solve VRPs eit... |
Title: Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning Abstract: Dynamic mechanism design has garnered significant attention from both computer scientists and economists in recent years. By allowing agents to interact with the seller over multiple rounds, where agents' reward f... |
Title: A Deep Learning Approach to Dst Index Prediction Abstract: The disturbance storm time (Dst) index is an important and useful measurement in space weather research. It has been used to characterize the size and intensity of a geomagnetic storm. A negative Dst value means that the Earth's magnetic field is weakene... |
Title: Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services Abstract: Recently, industrial recommendation services have been boosted by the continual upgrade of deep learning methods. However, they still face de-biasing challenges such as exposure bias and cold-start problem, where circu... |
Title: DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data Abstract: Accurate forecasting of extreme values in time series is critical due to the significant impact of extreme events on human and natural systems. This paper presents DeepExtrema, a novel framework that combines a deep ... |
Title: Uncertainty Minimization for Personalized Federated Semi-Supervised Learning Abstract: Since federated learning (FL) has been introduced as a decentralized learning technique with privacy preservation, statistical heterogeneity of distributed data stays the main obstacle to achieve robust performance and stable ... |
Title: Demystifying the Data Need of ML-surrogates for CFD Simulations Abstract: Computational fluid dynamics (CFD) simulations, a critical tool in various engineering applications, often require significant time and compute power to predict flow properties. The high computational cost associated with CFD simulations s... |
Title: FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs Abstract: Few-shot graph classification aims at predicting classes for graphs, given limited labeled graphs for each class. To tackle the bottleneck of label scarcity, recent works propose to incorporate few-shot learning frameworks for fast adap... |
Title: Generative Adversarial Network Based Synthetic Learning and a Novel Domain Relevant Loss Term for Spine Radiographs Abstract: Problem: There is a lack of big data for the training of deep learning models in medicine, characterized by the time cost of data collection and privacy concerns. Generative adversarial n... |
Title: Understanding Transfer Learning for Chest Radiograph Clinical Report Generation with Modified Transformer Architectures Abstract: The image captioning task is increasingly prevalent in artificial intelligence applications for medicine. One important application is clinical report generation from chest radiograph... |
Title: Subverting Fair Image Search with Generative Adversarial Perturbations Abstract: In this work we explore the intersection fairness and robustness in the context of ranking: when a ranking model has been calibrated to achieve some definition of fairness, is it possible for an external adversary to make the rankin... |
Title: Sequential Importance Sampling for Hybrid Model Bayesian Inference to Support Bioprocess Mechanism Learning and Robust Control Abstract: Driven by the critical needs of biomanufacturing 4.0, we introduce a probabilistic knowledge graph hybrid model characterizing the risk- and science-based understanding of biop... |
Title: Dangling-Aware Entity Alignment with Mixed High-Order Proximities Abstract: We study dangling-aware entity alignment in knowledge graphs (KGs), which is an underexplored but important problem. As different KGs are naturally constructed by different sets of entities, a KG commonly contains some dangling entities ... |
Title: Spot-adaptive Knowledge Distillation Abstract: Knowledge distillation (KD) has become a well established paradigm for compressing deep neural networks. The typical way of conducting knowledge distillation is to train the student network under the supervision of the teacher network to harness the knowledge at one... |
Title: Compressive Ptychography using Deep Image and Generative Priors Abstract: Ptychography is a well-established coherent diffraction imaging technique that enables non-invasive imaging of samples at a nanometer scale. It has been extensively used in various areas such as the defense industry or materials science. O... |
Title: Optimising Equal Opportunity Fairness in Model Training Abstract: Real-world datasets often encode stereotypes and societal biases. Such biases can be implicitly captured by trained models, leading to biased predictions and exacerbating existing societal preconceptions. Existing debiasing methods, such as advers... |
Title: Uncertainty-Based Non-Parametric Active Peak Detection Abstract: Active, non-parametric peak detection is considered. As a use case, active source localization is examined and an uncertainty-based sampling scheme algorithm to effectively localize the peak from a few energy measurements is designed. It is shown t... |
Title: Response Component Analysis for Sea State Estimation Using Artificial Neural Networks and Vessel Response Spectral Data Abstract: The use of the `ship as a wave buoy analogy' (SAWB) provides a novel means to estimate sea states, where relationships are established between causal wave properties and vessel motion... |
Title: KenSwQuAD -- A Question Answering Dataset for Swahili Low Resource Language Abstract: This research developed a Kencorpus Swahili Question Answering Dataset KenSwQuAD from raw data of Swahili language, which is a low resource language predominantly spoken in Eastern African and also has speakers in other parts o... |
Title: GitRank: A Framework to Rank GitHub Repositories Abstract: Open-source repositories provide wealth of information and are increasingly being used to build artificial intelligence (AI) based systems to solve problems in software engineering. Open-source repositories could be of varying quality levels, and bad-qua... |
Title: FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillation Abstract: Non-negative matrix factorization (NMF) with missing-value completion is a well-known effective Collaborative Filtering (CF) method used to provide personalized user recommenda... |
Title: Convolutional and Residual Networks Provably Contain Lottery Tickets Abstract: The Lottery Ticket Hypothesis continues to have a profound practical impact on the quest for small scale deep neural networks that solve modern deep learning tasks at competitive performance. These lottery tickets are identified by pr... |
Title: Knowledge Distillation of Russian Language Models with Reduction of Vocabulary Abstract: Today, transformer language models serve as a core component for majority of natural language processing tasks. Industrial application of such models requires minimization of computation time and memory footprint. Knowledge ... |
Title: Equity and Fairness of Bayesian Knowledge Tracing Abstract: We consider the equity and fairness of curricula derived from Knowledge Tracing models. We begin by defining a unifying notion of an equitable tutoring system as a system that achieves maximum possible knowledge in minimal time for each student interact... |
Title: Learning Individual Interactions from Population Dynamics with Discrete-Event Simulation Model Abstract: The abundance of data affords researchers to pursue more powerful computational tools to learn the dynamics of complex system, such as neural networks, engineered systems and social networks. Traditional mach... |
Title: Second-Order Sensitivity Analysis for Bilevel Optimization Abstract: In this work we derive a second-order approach to bilevel optimization, a type of mathematical programming in which the solution to a parameterized optimization problem (the "lower" problem) is itself to be optimized (in the "upper" problem) as... |
Title: Most Activation Functions Can Win the Lottery Without Excessive Depth Abstract: The strong lottery ticket hypothesis has highlighted the potential for training deep neural networks by pruning, which has inspired interesting practical and theoretical insights into how neural networks can represent functions. For ... |
Title: Language Models in the Loop: Incorporating Prompting into Weak Supervision Abstract: We propose a new strategy for applying large pre-trained language models to novel tasks when labeled training data is limited. Rather than apply the model in a typical zero-shot or few-shot fashion, we treat the model as the bas... |
Title: GRU-TV: Time- and velocity-aware GRU for patient representation on multivariate clinical time-series data Abstract: Electronic health records (EHRs) provide a rich repository to track a patient's health status. EHRs seek to fully document the patient's physiological status, and include data that is is high dimen... |
Title: Machine Learning Operations (MLOps): Overview, Definition, and Architecture Abstract: The final goal of all industrial machine learning (ML) projects is to develop ML products and rapidly bring them into production. However, it is highly challenging to automate and operationalize ML products and thus many ML end... |
Title: Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance Abstract: Human-translated text displays distinct features from naturally written text in the same language. This phenomena, known as translationese, has been argued to confound the machine translation (M... |
Title: pyRDF2Vec: A Python Implementation and Extension of RDF2Vec Abstract: This paper introduces pyRDF2Vec, a Python software package that reimplements the well-known RDF2Vec algorithm along with several of its extensions. By making the algorithm available in the most popular data science language, and by bundling al... |
Title: DeepBayes -- an estimator for parameter estimation in stochastic nonlinear dynamical models Abstract: Stochastic nonlinear dynamical systems are ubiquitous in modern, real-world applications. Yet, estimating the unknown parameters of stochastic, nonlinear dynamical models remains a challenging problem. The major... |
Title: Group-Invariant Quantum Machine Learning Abstract: Quantum Machine Learning (QML) models are aimed at learning from data encoded in quantum states. Recently, it has been shown that models with little to no inductive biases (i.e., with no assumptions about the problem embedded in the model) are likely to have tra... |
Title: Multivariate Prediction Intervals for Random Forests Abstract: Accurate uncertainty estimates can significantly improve the performance of iterative design of experiments, as in Sequential and Reinforcement learning. For many such problems in engineering and the physical sciences, the design task depends on mult... |
Title: FEDNEST: Federated Bilevel, Minimax, and Compositional Optimization Abstract: Standard federated optimization methods successfully apply to stochastic problems with single-level structure. However, many contemporary ML problems -- including adversarial robustness, hyperparameter tuning, and actor-critic -- fall ... |
Title: Semi-Supervised Cascaded Clustering for Classification of Noisy Label Data Abstract: The performance of supervised classification techniques often deteriorates when the data has noisy labels. Even the semi-supervised classification approaches have largely focused only on the problem of handling missing labels. M... |
Title: Wavelet neural operator: a neural operator for parametric partial differential equations Abstract: With massive advancements in sensor technologies and Internet-of-things, we now have access to terabytes of historical data; however, there is a lack of clarity in how to best exploit the data to predict future eve... |
Title: Efficient Few-Shot Fine-Tuning for Opinion Summarization Abstract: Abstractive summarization models are typically pre-trained on large amounts of generic texts, then fine-tuned on tens or hundreds of thousands of annotated samples. However, in opinion summarization, large annotated datasets of reviews paired wit... |
Title: Compound virtual screening by learning-to-rank with gradient boosting decision tree and enrichment-based cumulative gain Abstract: Learning-to-rank, a machine learning technique widely used in information retrieval, has recently been applied to the problem of ligand-based virtual screening, to accelerate the ear... |
Title: Making SGD Parameter-Free Abstract: We develop an algorithm for parameter-free stochastic convex optimization (SCO) whose rate of convergence is only a double-logarithmic factor larger than the optimal rate for the corresponding known-parameter setting. In contrast, the best previously known rates for parameter-... |
Title: Evaluating Transferability for Covid 3D Localization Using CT SARS-CoV-2 segmentation models Abstract: Recent studies indicate that detecting radiographic patterns on CT scans can yield high sensitivity and specificity for Covid-19 localization. In this paper, we investigate the appropriateness of deep learning ... |
Title: Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification Abstract: Recently, self-attention mechanisms have shown impressive performance in various NLP and CV tasks, which can help capture sequential characteristics and derive global information. In this work, we explore ... |
Title: Domino Saliency Metrics: Improving Existing Channel Saliency Metrics with Structural Information Abstract: Channel pruning is used to reduce the number of weights in a Convolutional Neural Network (CNN). Channel pruning removes slices of the weight tensor so that the convolution layer remains dense. The removal ... |
Title: Accelerating phase-field-based simulation via machine learning Abstract: Phase-field-based models have become common in material science, mechanics, physics, biology, chemistry, and engineering for the simulation of microstructure evolution. Yet, they suffer from the drawback of being computationally very costly... |
Title: Using Deep Reinforcement Learning to solve Optimal Power Flow problem with generator failures Abstract: Deep Reinforcement Learning (DRL) is being used in many domains. One of the biggest advantages of DRL is that it enables the continuous improvement of a learning agent. Secondly, the DRL framework is robust an... |
Title: Prediction of fish location by combining fisheries data and sea bottom temperature forecasting Abstract: This paper combines fisheries dependent data and environmental data to be used in a machine learning pipeline to predict the spatio-temporal abundance of two species (plaice and sole) commonly caught by the B... |
Title: Dynamic Sparse R-CNN Abstract: Sparse R-CNN is a recent strong object detection baseline by set prediction on sparse, learnable proposal boxes and proposal features. In this work, we propose to improve Sparse R-CNN with two dynamic designs. First, Sparse R-CNN adopts a one-to-one label assignment scheme, where t... |
Title: MAD: Self-Supervised Masked Anomaly Detection Task for Multivariate Time Series Abstract: In this paper, we introduce Masked Anomaly Detection (MAD), a general self-supervised learning task for multivariate time series anomaly detection. With the increasing availability of sensor data from industrial systems, be... |
Title: Data Cleansing for Indoor Positioning Wi-Fi Fingerprinting Datasets Abstract: Wearable and IoT devices requiring positioning and localisation services grow in number exponentially every year. This rapid growth also produces millions of data entries that need to be pre-processed prior to being used in any indoor ... |
Title: Learning Abstract and Transferable Representations for Planning Abstract: We are concerned with the question of how an agent can acquire its own representations from sensory data. We restrict our focus to learning representations for long-term planning, a class of problems that state-of-the-art learning methods ... |
Title: Hypercomplex Image-to-Image Translation Abstract: Image-to-image translation (I2I) aims at transferring the content representation from an input domain to an output one, bouncing along different target domains. Recent I2I generative models, which gain outstanding results in this task, comprise a set of diverse d... |
Title: NN-EUCLID: deep-learning hyperelasticity without stress data Abstract: We propose a new approach for unsupervised learning of hyperelastic constitutive laws with physics-consistent deep neural networks. In contrast to supervised learning, which assumes the availability of stress-strain pairs, the approach only u... |
Title: SVTS: Scalable Video-to-Speech Synthesis Abstract: Video-to-speech synthesis (also known as lip-to-speech) refers to the translation of silent lip movements into the corresponding audio. This task has received an increasing amount of attention due to its self-supervised nature (i.e., can be trained without manua... |
Title: Exploring Rawlsian Fairness for K-Means Clustering Abstract: We conduct an exploratory study that looks at incorporating John Rawls' ideas on fairness into existing unsupervised machine learning algorithms. Our focus is on the task of clustering, specifically the k-means clustering algorithm. To the best of our ... |
Title: Minimum Cost Intervention Design for Causal Effect Identification Abstract: Pearl's do calculus is a complete axiomatic approach to learn the identifiable causal effects from observational data. When such an effect is not identifiable, it is necessary to perform a collection of often costly interventions in the ... |
Title: Few-Shot Document-Level Relation Extraction Abstract: We present FREDo, a few-shot document-level relation extraction (FSDLRE) benchmark. As opposed to existing benchmarks which are built on sentence-level relation extraction corpora, we argue that document-level corpora provide more realism, particularly regard... |
Title: A Manifold Two-Sample Test Study: Integral Probability Metric with Neural Networks Abstract: Two-sample tests are important areas aiming to determine whether two collections of observations follow the same distribution or not. We propose two-sample tests based on integral probability metric (IPM) for high-dimens... |
Title: Immiscible Color Flows in Optimal Transport Networks for Image Classification Abstract: In classification tasks, it is crucial to meaningfully exploit information contained in data. Here, we propose a physics-inspired dynamical system that adapts Optimal Transport principles to effectively leverage color distrib... |
Title: Towards Job-Transition-Tag Graph for a Better Job Title Representation Learning Abstract: Works on learning job title representation are mainly based on \textit{Job-Transition Graph}, built from the working history of talents. However, since these records are usually messy, this graph is very sparse, which affec... |
Title: On Continual Model Refinement in Out-of-Distribution Data Streams Abstract: Real-world natural language processing (NLP) models need to be continually updated to fix the prediction errors in out-of-distribution (OOD) data streams while overcoming catastrophic forgetting. However, existing continual learning (CL)... |
Title: EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis Abstract: We describe EmoBank, a corpus of 10k English sentences balancing multiple genres, which we annotated with dimensional emotion metadata in the Valence-Arousal-Dominance (VAD) representation f... |
Title: Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning Abstract: The success of machine learning is fueled by the increasing availability of computing power and large training datasets. The training data is used to learn new models or update existing ones, assuming that it ... |
Title: Modelling calibration uncertainty in networks of environmental sensors Abstract: Networks of low-cost sensors are becoming ubiquitous, but often suffer from poor accuracies and drift. Regular colocation with reference sensors allows recalibration but is complicated and expensive. Alternatively the calibration ca... |
Title: Lifelong Ensemble Learning based on Multiple Representations for Few-Shot Object Recognition Abstract: Service robots are integrating more and more into our daily lives to help us with various tasks. In such environments, robots frequently face new objects while working in the environment and need to learn them ... |
Title: The Isabelle ENIGMA Abstract: We significantly improve the performance of the E automated theorem prover on the Isabelle Sledgehammer problems by combining learning and theorem proving in several ways. In particular, we develop targeted versions of the ENIGMA guidance for the Isabelle problems, targeted versions... |
Title: Sequencer: Deep LSTM for Image Classification Abstract: In recent computer vision research, the advent of the Vision Transformer (ViT) has rapidly revolutionized various architectural design efforts: ViT achieved state-of-the-art image classification performance using self-attention found in natural language pro... |
Title: Nonstationary Bandit Learning via Predictive Sampling Abstract: Although Thompson sampling is widely used in stationary environments, it does not effectively account for nonstationarities. To address this limitation, we propose predictive sampling, a policy that balances between exploration and exploitation in n... |
Title: Rethinking Classifier and Adversarial Attack Abstract: Various defense models have been proposed to resist adversarial attack algorithms, but existing adversarial robustness evaluation methods always overestimate the adversarial robustness of these models (i.e., not approaching the lower bound of robustness). To... |
Title: CE-based white-box adversarial attacks will not work using super-fitting Abstract: Deep neural networks are widely used in various fields because of their powerful performance. However, recent studies have shown that deep learning models are vulnerable to adversarial attacks, i.e., adding a slight perturbation t... |
Title: State Representation Learning for Goal-Conditioned Reinforcement Learning Abstract: This paper presents a novel state representation for reward-free Markov decision processes. The idea is to learn, in a self-supervised manner, an embedding space where distances between pairs of embedded states correspond to the ... |
Title: Word Tour: One-dimensional Word Embeddings via the Traveling Salesman Problem Abstract: Word embeddings are one of the most fundamental technologies used in natural language processing. Existing word embeddings are high-dimensional and consume considerable computational resources. In this study, we propose WordT... |
Title: DADApy: Distance-based Analysis of DAta-manifolds in Python Abstract: DADApy is a python software package for analysing and characterising high-dimensional data manifolds. It provides methods for estimating the intrinsic dimension and the probability density, for performing density-based clustering and for compa... |
Title: Uncertainty-Autoencoder-Based Privacy and Utility Preserving Data Type Conscious Transformation Abstract: We propose an adversarial learning framework that deals with the privacy-utility tradeoff problem under two types of conditions: data-type ignorant, and data-type aware. Under data-type aware conditions, the... |
Title: Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs Abstract: This paper aims to theoretically analyze the complexity of feature transformations encoded in DNNs with ReLU layers. We propose metrics to measure three types of complexities of transformations based on the information theory. We fu... |
Title: DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs Abstract: As Deep Learning (DL) systems are widely deployed for mission-critical applications, debugging such systems becomes essential. Most existing works identify and repair suspicious neurons on the trained Deep Neural Network (DNN... |
Title: Self-supervised learning unveils morphological clusters behind lung cancer types and prognosis Abstract: Histopathological images of tumors contain abundant information about how tumors grow and how they interact with their micro-environment. Characterizing and improving our understanding of phenotypes could rev... |
Title: Explain to Not Forget: Defending Against Catastrophic Forgetting with XAI Abstract: The ability to continuously process and retain new information like we do naturally as humans is a feat that is highly sought after when training neural networks. Unfortunately, the traditional optimization algorithms often requi... |
Title: Probabilistic Symmetry for Improved Trajectory Forecasting Abstract: Trajectory prediction is a core AI problem with broad applications in robotics and autonomous driving. While most existing works focus on deterministic prediction, producing probabilistic forecasts to quantify prediction uncertainty is critical... |
Title: Zero-Episode Few-Shot Contrastive Predictive Coding: Solving intelligence tests without prior training Abstract: Video prediction models often combine three components: an encoder from pixel space to a small latent space, a latent space prediction model, and a generative model back to pixel space. However, the l... |
Title: Second Order Path Variationals in Non-Stationary Online Learning Abstract: We consider the problem of universal dynamic regret minimization under exp-concave and smooth losses. We show that appropriately designed Strongly Adaptive algorithms achieve a dynamic regret of $\tilde O(d^2 n^{1/5} C_n^{2/5} \vee d^2)$,... |
Title: CoCa: Contrastive Captioners are Image-Text Foundation Models Abstract: Exploring large-scale pretrained foundation models is of significant interest in computer vision because these models can be quickly transferred to many downstream tasks. This paper presents Contrastive Captioner (CoCa), a minimalist design ... |
Title: Generalized Knowledge Distillation via Relationship Matching Abstract: The knowledge of a well-trained deep neural network (a.k.a. the "teacher") is valuable for learning similar tasks. Knowledge distillation extracts knowledge from the teacher and integrates it with the target model (a.k.a. the "student"), whic... |
Title: Modeling Task Interactions in Document-Level Joint Entity and Relation Extraction Abstract: We target on the document-level relation extraction in an end-to-end setting, where the model needs to jointly perform mention extraction, coreference resolution (COREF) and relation extraction (RE) at once, and gets eval... |
Title: ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters Abstract: The incredible feats of athleticism demonstrated by humans are made possible in part by a vast repertoire of general-purpose motor skills, acquired through years of practice and experience. These skills not only ... |
Title: Self-Taught Metric Learning without Labels Abstract: We present a novel self-taught framework for unsupervised metric learning, which alternates between predicting class-equivalence relations between data through a moving average of an embedding model and learning the model with the predicted relations as pseudo... |
Title: Spatial-Temporal Meta-path Guided Explainable Crime Prediction Abstract: Exposure to crime and violence can harm individuals' quality of life and the economic growth of communities. In light of the rapid development in machine learning, there is a rise in the need to explore automated solutions to prevent crimes... |
Title: Virtual Analog Modeling of Distortion Circuits Using Neural Ordinary Differential Equations Abstract: Recent research in deep learning has shown that neural networks can learn differential equations governing dynamical systems. In this paper, we adapt this concept to Virtual Analog (VA) modeling to learn the ord... |
Title: Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction Abstract: Machine learning (ML) models have been widely successful in the prediction of material properties. However, large labeled datasets required for training accurate ML models are elusive and computationally expensive to g... |
Title: Uncertainty estimation of pedestrian future trajectory using Bayesian approximation Abstract: Past research on pedestrian trajectory forecasting mainly focused on deterministic predictions which provide only point estimates of future states. These future estimates can help an autonomous vehicle plan its trajecto... |
Title: fairlib: A Unified Framework for Assessing and Improving Classification Fairness Abstract: This paper presents fairlib, an open-source framework for assessing and improving classification fairness. It provides a systematic framework for quickly reproducing existing baseline models, developing new methods, evalua... |
Title: Machine Learning based Framework for Robust Price-Sensitivity Estimation with Application to Airline Pricing Abstract: We consider the problem of dynamic pricing of a product in the presence of feature-dependent price sensitivity. Based on the Poisson semi-parametric approach, we construct a flexible yet interpr... |
Title: Provably Confidential Language Modelling Abstract: Large language models are shown to memorize privacy information such as social security numbers in training data. Given the sheer scale of the training corpus, it is challenging to screen and filter these privacy data, either manually or automatically. In this p... |
Title: DeeptDCS: Deep Learning-Based Estimation of Currents Induced During Transcranial Direct Current Stimulation Abstract: Objective: Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique used to generate conduction currents in the head and disrupt brain functions. To rapidly ev... |
Title: SMLT: A Serverless Framework for Scalable and Adaptive Machine Learning Design and Training Abstract: In today's production machine learning (ML) systems, models are continuously trained, improved, and deployed. ML design and training are becoming a continuous workflow of various tasks that have dynamic resource... |
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