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.
Study examines machine learning competitions' impact on AI development.
AI platforms disrupt investment by personalizing deal sourcing and insights.
Study predicts individual treatment effects in ride-sharing competitions.
Pakistan examines digital mergers using traditional competition tools.
Kaggle chronicles 15 years of competitions, innovation, and data science.
This paper presents the first two editions of Visual Doom AI Competition, held in 2016 and 2017. The challenge was to create bots that compete in a multi-player deathmatch in a first-person shooter (FPS) game, Doom. The bots had to make their decisions based solely on visual information, i.e., a raw screen buffer. To p…
We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018. This data driven competition asked participants to develop computer programs capable of solving supervised learning problems where the i.i.d. assumption did not hold. Large data sets w…
For the last two decades, most financial markets have undergone an evolution toward electronification. The market for corporate bonds is one of the last major financial markets to follow this unavoidable path. Traditionally quote-driven i.e., dealer-driven) rather than order-driven, the market for corporate bonds is st…
The boom of deep learning induced many industries and academies to introduce machine learning based approaches into their concern, competitively. However, existing machine learning frameworks are limited to sufficiently fulfill the collaboration and management for both data and models. We proposed NSML, a machine learn…
Competition aims to develop sample-efficient reinforcement learning methods.
Kaggle competitions offer valuable insights for business forecasting.
The paper analyzes RfQ processes on MD2C platforms using probabilistic models.
In this paper we consider an online recommendation setting, where a platform recommends a sequence of items to its users at every time period. The users respond by selecting one of the items recommended or abandon the platform due to fatigue from seeing less useful items. Assuming a parametric stochastic model of user …
This study designs a financial risk control platform using big data and machine learning.
Many hyperparameter optimization (HyperOpt) methods assume restricted computing resources and mainly focus on enhancing performance. Here we propose a novel cloud-based HyperOpt (CHOPT) framework which can efficiently utilize shared computing resources while supporting various HyperOpt algorithms. We incorporate conven…
Modeling incentives for content creators on algorithm-curated platforms.
User smeznar achieved 8th place in PGDL by predicting generalization of deep learning models.
Study shows how competition affects learning in matching markets, proving it's possible to balance stability, fairness, and regret.
A new method for analyzing product competition using low-dimensional embeddings.
Frameworks for writing, compiling, and optimizing deep learning (DL) models have recently enabled progress in areas like computer vision and natural language processing. Extending these frameworks to accommodate the rapidly diversifying landscape of DL models and hardware platforms presents challenging tradeoffs betwee…
Increased data gathering capacity, together with the spread of data analytics techniques, has prompted an unprecedented concentration of information related to the individuals' preferences in the hands of a few gatekeepers. In the present paper, we show how platforms' performances still appear astonishing in relation t…
Study shows competition feedback can make ML predictors biased towards specific user groups.
Federated Learning speeds up speech recognition training by 7x and reduces error rate by 6%.
This paper describes Task 2 of the DCASE 2018 Challenge, titled "General-purpose audio tagging of Freesound content with AudioSet labels". This task was hosted on the Kaggle platform as "Freesound General-Purpose Audio Tagging Challenge". The goal of the task is to build an audio tagging system that can recognize the c…
Machine learning is often used in competitive scenarios: Participants learn and fit static models, and those models compete in a shared platform. The common assumption is that in order to win a competition one has to have the best predictive model, i.e., the model with the smallest out-sample error. Is that necessarily…
Deep learning predicts RNA degradation from crowdsourced data.
CLEANN detects and mitigates neural network Trojans without labeled data.
Public debates are a common platform for presenting and juxtaposing diverging views on important issues. In this work we propose a methodology for tracking how ideas flow between participants throughout a debate. We use this approach in a case study of Oxford-style debates---a competitive format where the winner is det…
State-of-the-art image recognition systems use sophisticated Convolutional Neural Networks (CNNs) that are designed and trained to identify numerous object classes. Such networks are fairly resource intensive to compute, prohibiting their deployment on resource-constrained embedded platforms. On one hand, the ability t…
Model shows incentives in shared order book can lead to free-rider problem.
IntraLayer connects digital finance platforms efficiently.
ApolloRL offers a platform for RL research in autonomous driving.
Consider a platform that wants to learn a personalized policy for each user, but the platform faces the risk of a user abandoning the platform if she is dissatisfied with the actions of the platform. For example, a platform is interested in personalizing the number of newsletters it sends, but faces the risk that the u…
This paper uses bandit algorithms to reduce the cost of user interface experimentation in online retail.
Federated learning platform for drug discovery without sharing data.
This paper proposes a distributed deep learning framework for privacy-preserving medical data training. In order to avoid patients' data leakage in medical platforms, the hidden layers in the deep learning framework are separated and where the first layer is kept in platform and others layers are kept in a centralized …
Robotics: Rolling robots on a moving platform can be controlled.
Though deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples. As state-of-the-art reinforcement learning (RL) systems require an exponentially increasing number of samples, their development is restricted to a continually shrin…
Method generates resource-optimized ML models for different platforms.
PAMS is a Python-based platform for simulating artificial markets.
GC 2022 challenges real-time trend detection in financial tick data.
Convolutional Neural Network (CNN) based Deep Learning (DL) has achieved great progress in many real-life applications. Meanwhile, due to the complex model structures against strict latency and memory restriction, the implementation of CNN models on the resource-limited platforms is becoming more challenging. This work…
Collectives can manipulate learning platforms by coordinated data submission, requiring strategic assessments and algorithms.
We propose a contextual bandit based model to capture the learning and social welfare goals of a web platform in the presence of myopic users. By using payments to incentivize these agents to explore different items/recommendations, we show how the platform can learn the inherent attributes of items and achieve a subli…
GEM detects malicious accounts using adaptive embeddings from heterogeneous graphs.
Simulates DeLend Platform behavior to optimize operational parameters.
Recent advances in artificial intelligence have been driven by the presence of increasingly realistic and complex simulated environments. However, many of the existing environments provide either unrealistic visuals, inaccurate physics, low task complexity, restricted agent perspective, or a limited capacity for intera…
A method for fast, accurate cross-temporal forecasts using machine learning.