4 citations · 12 across the 12 of their papers we have counts for
12 papers
Entropic Riemannian Neural Optimal Transport
Alessandro Micheli, Silvia Sapora, Anthea Monod +1
Many machine learning problems involve data supported on curved spaces such as spheres, rotation groups, hyperbolic spaces, and general Riemannian manifolds, where Euclidean geomet…
GRACE: A Language Model Framework for Explainable Inverse Reinforcement Learning
Silvia Sapora, Devon Hjelm, Alexander Toshev +2
Inverse Reinforcement Learning aims to recover reward models from expert demonstrations, but traditional methods yield black-box models that are difficult to interpret and debug. I…
Programming by Backprop: An Instruction is Worth 100 Examples When Finetuning LLMs
Jonathan Cook, Silvia Sapora, Arash Ahmadian +4
Large language models (LLMs) are typically trained to acquire behaviours from demonstrations or experience, yet much of their training data is declarative: instructions, rules, and…
The Complexity Dynamics of Grokking
Branton DeMoss, Silvia Sapora, Jakob Foerster +2
We demonstrate the existence of a complexity phase transition in neural networks by studying the grokking phenomenon, where networks suddenly transition from memorization to genera…
Meta-Learning Objectives for Preference Optimization
Carlo Alfano, Silvia Sapora, Jakob Nicolaus Foerster +2
Evaluating preference optimization (PO) algorithms on LLM alignment is a challenging task that presents prohibitive costs, noise, and several variables like model size and hyper-pa…
EvIL: Evolution Strategies for Generalisable Imitation Learning
Silvia Sapora, Gokul Swamy, Chris Lu +2
Often times in imitation learning (IL), the environment we collect expert demonstrations in and the environment we want to deploy our learned policy in aren't exactly the same (e.g…
Learning mirror maps in policy mirror descent
Carlo Alfano, Sebastian Towers, Silvia Sapora +2
Policy Mirror Descent (PMD) is a popular framework in reinforcement learning, serving as a unifying perspective that encompasses numerous algorithms. These algorithms are derived t…
JAX-LOB: A GPU-Accelerated limit order book simulator to unlock large scale reinforcement learning for trading
Sascha Frey, Kang Li, Peer Nagy +5
Financial exchanges across the world use limit order books (LOBs) to process orders and match trades. For research purposes it is important to have large scale efficient simulators…
Generative AI for End-to-End Limit Order Book Modelling: A Token-Level Autoregressive Generative Model of Message Flow Using a Deep State Space Network
Peer Nagy, Sascha Frey, Silvia Sapora +4
Developing a generative model of realistic order flow in financial markets is a challenging open problem, with numerous applications for market participants. Addressing this, we pr…
Ownership at Large -- Open Problems and Challenges in Ownership Management
John Ahlgren, Maria Eugenia Berezin, Kinga Bojarczuk +10
Software-intensive organizations rely on large numbers of software assets of different types, e.g., source-code files, tables in the data warehouse, and software configurations. Wh…
WES: Agent-based User Interaction Simulation on Real Infrastructure
John Ahlgren, Maria Eugenia Berezin, Kinga Bojarczuk +9
We introduce the Web-Enabled Simulation (WES) research agenda, and describe FACEBOOK's WW system. We describe the application of WW to reliability, integrity and privacy at FACEBOO…
Absit invidia verbo: Comparing Deep Learning methods for offensive language
Silvia Sapora, Bogdan Lazarescu, Christo Lolov
This document describes our approach to building an Offensive Language Classifier. More specifically, the OffensEval 2019 competition required us to build three classifiers with sl…