A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
FrugalML optimizes API selection for cost and accuracy.
Proposes an online model for LLM cascading with adaptive API selection.
TorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection
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 …
Method generates adversarial examples to improve classifier robustness.
LIBTwinSVM offers a free library for efficient Twin Support Vector Machines.
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…
This paper investigates how machine learning APIs change over time and proposes an efficient method to monitor these changes.
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…
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 …
PHOTONAI simplifies machine learning model development in Python.
OpenML-Python API simplifies access to OpenML for Python users.
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…
VarDetect monitors API queries to detect model extraction attacks.
liquidSVM is a package written in C++ that provides SVM-type solvers for various classification and regression tasks. Because of a fully integrated hyper-parameter selection, very carefully implemented solvers, multi-threading and GPU support, and several built-in data decomposition strategies it provides unprecedented…
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 …
Designs a classifier for malware detection using MDL principle.
CK simplifies ML model deployment and reproducibility with open APIs and DevOps.
Balance corrects biased survey data for more accurate insights.
Open-source Vizier optimizes complex systems for Google and beyond.
Research evaluates model extraction attacks on complex ML models and introduces a defense.
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…
PettingZoo library accelerates multi-agent reinforcement learning research.
tf_geometric simplifies graph deep learning in TensorFlow.
A recent trend observed in traditionally challenging fields such as computer vision and natural language processing has been the significant performance gains shown by deep learning (DL). In many different research fields, DL models have been evolving rapidly and become ubiquitous. Despite researchers' excitement, unfo…
Paper explores how non-neural simulators can enhance DP synthetic data generation.
Proposes a new algorithm for efficient online model selection of LLMs considering the increasing-then-converging trend.
Python library for integrating TDA with machine learning.
Deep learning models misclassify malware with added benign features.
KL-constrained API shows optimization issues and improved with regularization.
Recent studies have highlighted that deep neural networks (DNNs) are vulnerable to adversarial attacks, even in a black-box scenario. However, most of the existing black-box attack algorithms need to make a huge amount of queries to perform attacks, which is not practical in the real world. We note one of the main reas…
fastai simplifies deep learning with a layered API.
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…
By monitoring the time evolution of the most liquid Futures contracts traded globally as acquired using the Bloomberg API from 03 January 2000 until 15 December 2014 we were able to forecast the S&P 500 index beating the Buy and Hold trading strategy. Our approach is based on convolution computations of 42 of the most …
Ever increasing number of Android malware, has always been a concern for cybersecurity professionals. Even though plenty of anti-malware solutions exist, a rational and pragmatic approach for the same is rare and has to be inspected further. In this paper, we propose a novel two-set feature selection approach based on …
Math model helps bees decide between winter survival and raising young.
Transformer models improve malware classification, especially for imbalanced datasets.
torchsom simplifies SOMs in PyTorch with GPU acceleration and scikit-learn API.
CSA fills a gap in RLVR-trained LLM deployment by providing anytime-valid selective risk control.
Wrapper improves black-box model auditability and decision trustworthiness.
GARCH models predict stock volatility in Indian sectors.
Online social networks (OSN) contain extensive amount of information about the underlying society that is yet to be explored. One of the most feasible technique to fetch information from OSN, crawling through Application Programming Interface (API) requests, poses serious concerns over the the guarantees of the estimat…
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…
tsbootstrap handles time series uncertainty without assuming independence.
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…
Improves model classification accuracy in black-box settings.
BoFire optimizes chemistry experiments using Bayesian Optimization.