publications

2023

1. I See You! Robust Measurement of Adversarial Behavior

   Lars Ankile, Matheus XV Ferreira, and David Parkes

   In Multi-Agent Security Workshop @ NeurIPS’23 2023

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   We introduce the study of non-manipulable measures of manipulative behavior in multi-agent systems. We do this through a case study of decentralized finance (DeFi) and blockchain systems, which are salient as real-world, rapidly emerging multi-agent systems with financial incentives for malicious behavior, for the participation in algorithmic and AI systems, and in highlighting the need for new methods with which to measure levels of manipulative behavior. We introduce a new surveillance metric for measuring malicious behavior in this context and demonstrate its effectiveness in a natural experiment to the Uniswap DeFi ecosystem.

2. Discovering User Types: Mapping User Traits by Task-Specific Behaviors in Reinforcement Learning

   Lars Lien Ankile, Brian Ham, Kevin Mao, and 4 more authors

   In First Workshop on Theory of Mind in Communicating Agents @ ICML’23 2023

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   When assisting human users in reinforcement learning (RL), we can represent users as RL agents and study key parameters, called user traits, to inform intervention design. We study the relationship between user behaviors (policy classes) and user traits. Given an environment, we introduce an intuitive tool for studying the breakdown of "user types": broad sets of traits that result in the same behavior. We show that seemingly different real-world environments admit the same set of user types and formalize this observation as an equivalence relation defined on environments. By transferring intervention design between environments within the same equivalence class, we can help rapidly personalize interventions.

3. Denoising Diffusion Probabilistic Models as a Defense against Adversarial Attacks

   Lars Lien Ankile, Anna Midgley, and Sebastian Weisshaar

   Eight International Workshop on Health Intelligence @ AAAI’24 2023

2022

1. M.Sc.

   Exploration of Forecasting Paradigms and a Generalized Forecasting Framework

   Lars Lien Ankile, and Kjartan Krange

   2022

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   First, this paper presents a total ordering of the theoretical lower bound loss of different forecasting paradigms in the following descending order: Model selection, Model combination, Non-parametric univariate models, and Non-parametric multivariate models. Second, we create a generalized forecasting framework to test the above forecasting paradigms ex-ante. We implement the framework by creating a novel datacube consisting of daily stock prices and 100,000 quarterly reports from about 1600 global companies and several daily macro time series, all from 2000 to spring 2022. Lastly, we utilize the framework and show that modern multivariate time series approaches are powerful but domain-dependent. We demonstrate the domain-dependent accuracy by showing convincing results when predicting corporate bankruptcy risk, moderate results when predicting stock price volatility, and lacking results when finally predicting company market capitalization. Given the domain-dependent convincing results and mostly unrealized theoretical lower bound loss of multivariate approaches, we hope to encourage further research on non-parametric, multi-signal approaches that leverage a wider array of available information.

2. arXiv

   Deep Learning and Linear Programming for Automated Ensemble Forecasting and Interpretation

   Lars Lien Ankile, and Kjartan Krange

   arXiv preprint arXiv:2201.00426 2022

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   This paper presents an ensemble forecasting method that shows strong results on the M4 Competition dataset by decreasing feature and model selection assumptions, termed DONUT (DO Not UTilize human beliefs). Our assumption reductions, primarily consisting of auto-generated features and a more diverse model pool for the ensemble, significantly outperform the statistical, feature-based ensemble method FFORMA by Montero-Manso et al. (2020). We also investigate feature extraction with a Long Short-term Memory Network (LSTM) Autoencoder and find that such features contain crucial information not captured by standard statistical feature approaches. The ensemble weighting model uses LSTM and statistical features to combine the models accurately. The analysis of feature importance and interaction shows a slight superiority for LSTM features over the statistical ones alone. Clustering analysis shows that essential LSTM features differ from most statistical features and each other. We also find that increasing the solution space of the weighting model by augmenting the ensemble with new models is something the weighting model learns to use, thus explaining part of the accuracy gains. Moreover, we present a formal ex-post-facto analysis of an optimal combination and selection for ensembles, quantifying differences through linear optimization on the M4 dataset. Our findings indicate that classical statistical time series features, such as trend and seasonality, alone do not capture all relevant information for forecasting a time series. On the contrary, our novel LSTM features contain significantly more predictive power than the statistical ones alone, but combining the two feature sets proved the best in practice.

2020

1. arXiv

   Deep Convolutional Neural Networks: A survey of the foundations, selected improvements, and some current applications

   Lars Lien Ankile, Morgan Feet Heggland, and Kjartan Krange

   arXiv preprint arXiv:2011.12960 2020

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2019

1. arXiv

   Approximate Strategy-Proofness in Large, Two-Sided Matching Markets

   Lars Lien Ankile, Kjartan Krange, and Yuto Yagi

   arXiv preprint arXiv:1912.04800 2019

   arXiv PDF Code