arXiv research

FrugalML optimizes API selection for cost and accuracy.

More and more AI services are provided through APIs on cloud where predictive models are hidden behind APIs. To build trust with users and reduce potential application risk, it is important to interpret how such predictive models hidden behind APIs make their decisions. The biggest challenge of interpreting such predic…

This paper investigates how machine learning APIs change over time and proposes an efficient method to monitor these changes.

Research evaluates model extraction attacks on complex ML models and introduces a defense.

To save manual effort, developers often translate programs from one programming language to another, instead of implementing it from scratch. Translating application program interfaces (APIs) used in one language to functionally equivalent ones available in another language is an important aspect of program translation…

This paper introduces libconform v0.1.0, a Python library for the conformal prediction framework, licensed under the MIT-license. libconform is not yet stable. This paper describes the main algorithms implemented and documents the API of libconform. Also some details about the implementation and changes in future versi…

Training machine learning (ML) models is expensive in terms of computational power, amounts of labeled data and human expertise. Thus, ML models constitute intellectual property (IP) and business value for their owners. Embedding digital watermarks during model training allows a model owner to later identify their mode…

In this work, we attempt to solve the Hit Song Science problem, which aims to predict which songs will become chart-topping hits. We constructed a dataset with approximately 1.8 million hit and non-hit songs and extracted their audio features using the Spotify Web API. We test four models on our dataset. Our best model…

Adversarial examples are important for understanding the behavior of neural models, and can improve their robustness through adversarial training. Recent work in natural language processing generated adversarial examples by assuming white-box access to the attacked model, and optimizing the input directly against it (E…

OpenML-Python API simplifies access to OpenML for Python users.

TorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection

tf_geometric simplifies graph deep learning in TensorFlow.

PHOTONAI simplifies machine learning model development in Python.

As online systems based on machine learning are offered to public or paid subscribers via application programming interfaces (APIs), they become vulnerable to frequent exploits and attacks. This paper studies adversarial machine learning in the practical case when there are rate limitations on API calls. The adversary …

Paper tackles target shift in zero-shot learning using adversarial learning.

With the growth of user-generated content, we observe the constant rise of the number of companies, such as search engines, content aggregators, etc., that operate with tremendous amounts of web content not being the services hosting it. Thus, aiming to locate the most important content and promote it to the users, the…

VarDetect monitors API queries to detect model extraction attacks.

PyKale bridges interdisciplinary ML with Python, enabling accurate predictions.

I present a web service for querying an embedding of entities in the Wikidata knowledge graph. The embedding is trained on the Wikidata dump using Gensim's Word2Vec implementation and a simple graph walk. A REST API is implemented. Together with the Wikidata API the web service exposes a multilingual resource for over …

Machine learning has been applied to a broad range of applications and some of them are available online as application programming interfaces (APIs) with either free (trial) or paid subscriptions. In this paper, we study adversarial machine learning in the form of back-box attacks on online classifier APIs. We start w…

CK simplifies ML model deployment and reproducibility with open APIs and DevOps.

Open-source Vizier optimizes complex systems for Google and beyond.

DeepRacing uses neural networks to predict trajectories for autonomous racing in video games.

New federated learning protocols improve on knowledge distillation's poor performance.

We consider the infinite-horizon discounted optimal control problem formalized by Markov Decision Processes. We focus on several approximate variations of the Policy Iteration algorithm: Approximate Policy Iteration, Conservative Policy Iteration (CPI), a natural adaptation of the Policy Search by Dynamic Programming a…

InferPy is a Python package for probabilistic modeling with deep neural networks. It defines a user-friendly API that trades-off model complexity with ease of use, unlike other libraries whose focus is on dealing with very general probabilistic models at the cost of having a more complex API. In particular, this packag…

Method generates adversarial examples to improve classifier robustness.

Wrapper improves black-box model auditability and decision trustworthiness.

MAPIE provides uncertainty quantification for ML models.

PettingZoo library accelerates multi-agent reinforcement learning research.

Proposes an online model for LLM cascading with adaptive API selection.

Torch-Struct simplifies structured prediction for deep learning.

In the past few years, Convolutional Neural Networks (CNNs) have been achieving state-of-the-art performance on a variety of problems. Many companies employ resources and money to generate these models and provide them as an API, therefore it is in their best interest to protect them, i.e., to avoid that someone else c…

Regression or classification? This is perhaps the most basic question faced when tackling a new supervised learning problem. We present an Evolutionary Deep Learning (EDL) algorithm that automatically solves this by identifying the question type with high accuracy, along with a proposed deep architecture. Typically, a …

Paper explores how non-neural simulators can enhance DP synthetic data generation.

High-performance Deep Neural Networks (DNNs) are increasingly deployed in many real-world applications e.g., cloud prediction APIs. Recent advances in model functionality stealing attacks via black-box access (i.e., inputs in, predictions out) threaten the business model of such applications, which require a lot of tim…

Python library for integrating TDA with machine learning.

LIBTwinSVM offers a free library for efficient Twin Support Vector Machines.

Deep learning models misclassify malware with added benign features.

KL-constrained API shows optimization issues and improved with regularization.

fastai simplifies deep learning with a layered API.

AutoRec automates deep recommendation models using AutoML.

This paper addresses the model-free nonlinear optimal problem with generalized cost functional, and a data-based reinforcement learning technique is developed. It is known that the nonlinear optimal control problem relies on the solution of the Hamilton-Jacobi-Bellman (HJB) equation, which is a nonlinear partial differ…

pystacked combines machine learning models for improved predictions.

Rule-based models are often used for data analysis as they combine interpretability with predictive power. We present RuleKit, a versatile tool for rule learning. Based on a sequential covering induction algorithm, it is suitable for classification, regression, and survival problems. The presence of a user-guided induc…

Bayesian Matrix Factorization (BMF) is a powerful technique for recommender systems because it produces good results and is relatively robust against overfitting. Yet BMF is more computationally intensive and thus more challenging to implement for large datasets. In this work we present SMURFF a high-performance featur…

tsbootstrap handles time series uncertainty without assuming independence.

A new method uses conformal prediction to create reliable confidence masks for image super-resolution.