Scalable Approaches for a Theory of Many Minds
Maximilian Puelma Touzel
Amin Memarian
Matthew D Riemer
Andrei Mircea
Andrew Robert Williams
Elin Ahlstrand
Lucas Lehnert
Rupali Bhati
Guillaume Dumas
Irina Rish
A major challenge as we move towards building agents for real-world problems, which could involve a massive number of human and/or machine a… (voir plus)gents, is that we must learn to reason about the behavior of these many other agents. In this paper, we consider the problem of scaling a predictive Theory of Mind (ToM) model to a very large number of interacting agents with a fixed computational budget. Motivated by the limited diversity of agent types, existing approaches to scalable TOM learn versatile single-agent representations for quickly adapting to new agents encountered sequentially. We consider the more general setting that many agents are observed in parallel and formulate the corresponding Theory of Many Minds (ToMM) problem of estimating the joint policy. We frame the scaling behavior of solutions in terms of parameter sharing schemes and in particular propose two parameter-free architectural features that endow models with the ability to exploit action correlations: encoding a multi-agent context, and decoding through an abstracted joint action space. The increased predictive capabilities that have come with foundation models have made it easier to imagine the possibility of using these models to make simulations that imitate the behavior of many agents within complex real-world systems. Being able to perform these simulations in a general-purpose way would not only help make more capable agents, it also would be a very useful capability for applications in social science, political science, and economics.
2024-06-18
ICML.cc/2024/Workshop/Agentic_Markets (poster)
openreview.net
openreview.net
Assessing the Viability of Generative Modeling in Simulated Astronomical Observations
Patrick Janulewicz
Laurence Perreault-Levasseur
Tracy Webb
In this paper, we use methods for assessing the quality of generative models and apply them to a problem from the physical sciences. We turn… (voir plus) our attention to astrophysics, where cosmological simulations are often used to create mock observations that mimic telescope images. These simulations and their mock observations are often slow and challenging to generate, inspiring some to use generative modeling to enhance the amount of data available to study. In this work, we add realism to simulated images of galaxy clusters and use probability mass estimation to assess their fidelity compared to reality. We find that the simulations are biased compared to real observations and suggest that researchers applying generative modeling to these systems should proceed with caution.
2024-06-17
ICML.cc/2024/Workshop/SPIGM (poster)
openreview.net
openreview.net
Augmenting Evolutionary Models with Structure-based Retrieval
Yining Huang
Zuobai Zhang
Jian Tang
Debora Susan Marks
Pascal Notin
2024-06-17
ICML.cc/2024/Workshop/ML4LMS (poster)
openreview.net
openreview.net
Bias-inducing geometries: exactly solvable data model with fairness implications
Stefano Sarao Mannelli
Federica Gerace
Negar Rostamzadeh
Luca Saglietti
Machine learning (ML) may be oblivious to human bias but it is not immune to its perpetuation. Marginalisation and iniquitous group represen… (voir plus)tation are often traceable in the very data used for training, and may be reflected or even enhanced by the learning models. In this abstract, we aim to clarify the role played by data geometry in the emergence of ML bias. We introduce an exactly solvable high-dimensional model of data imbalance, where parametric control over the many bias-inducing factors allows for an extensive exploration of the bias inheritance mechanism. Through the tools of statistical physics, we analytically characterise the typical properties of learning models trained in this synthetic framework and obtain exact predictions for the observables that are commonly employed for fairness assessment. Simplifying the nature of the problem to its minimal components, we can retrace and unpack typical unfairness behaviour observed on real-world datasets
2024-06-17
ICML.cc/2024/Workshop/GRaM (publié)
openreview.net
openreview.net
Bias-inducing geometries: exactly solvable data model with fairness implications
Stefano Sarao Mannelli
Federica Gerace
Negar Rostamzadeh
Luca Saglietti
Machine learning (ML) may be oblivious to human bias but it is not immune to its perpetuation. Marginalisation and iniquitous group represen… (voir plus)tation are often traceable in the very data used for training, and may be reflected or even enhanced by the learning models. In this abstract, we aim to clarify the role played by data geometry in the emergence of ML bias. We introduce an exactly solvable high-dimensional model of data imbalance, where parametric control over the many bias-inducing factors allows for an extensive exploration of the bias inheritance mechanism. Through the tools of statistical physics, we analytically characterise the typical properties of learning models trained in this synthetic framework and obtain exact predictions for the observables that are commonly employed for fairness assessment. Simplifying the nature of the problem to its minimal components, we can retrace and unpack typical unfairness behaviour observed on real-world datasets
2024-06-17
ICML.cc/2024/Workshop/GRaM (publié)
openreview.net
openreview.net
Demystifying amortized causal discovery with transformers
Francesco Montagna
Max Cairney-Leeming
Dhanya Sridhar
Francesco Locatello
Supervised learning approaches for causal discovery from observational data often achieve competitive performance despite seemingly avoiding… (voir plus) explicit assumptions that traditional methods make for identifiability. In this work, we investigate CSIvA \citep{ke2023learning}, a transformer-based model promising to train on synthetic data and transfer to real data. First, we bridge the gap with existing identifiability theory and show that constraints on the training data distribution implicitly define a prior on the test observations. Consistent with classical approaches, good performance is achieved when we have a good prior on the test data, and the underlying model is identifiable. At the same time, we find new trade-offs. Training on datasets generated from different classes of causal models, unambiguously identifiable in isolation, improves the test generalization. Performance is still guaranteed, as the ambiguous cases resulting from the mixture of identifiable causal models are unlikely to occur (which we formally prove). Overall, our study finds that amortized causal discovery still needs to obey identifiability theory, but it also differs from classical methods in how the assumptions are formulated, trading more reliance on assumptions on the noise type for fewer hypotheses on the mechanisms.
2024-06-17
ICML.cc/2024/Workshop/SPIGM (poster)
openreview.net
openreview.net
Equivariant Flow Matching for Molecular Conformer Generation
Majdi Hassan
Nikhil Shenoy
Jungyoon Lee
Hannes Stärk
Stephan Thaler
Dominique Beaini
2024-06-17
ICML.cc/2024/Workshop/ML4LMS (poster)
openreview.net
openreview.net
Geometry-Aware Generative Autoencoders for Metric Learning and Generative Modeling on Data Manifolds
Xingzhi Sun
Danqi Liao
Kincaid MacDonald
Yanlei Zhang
Guillaume Huguet
Guy Wolf
Ian Adelstein
Tim G. J. Rudner
Smita Krishnaswamy
Non-linear dimensionality reduction methods have proven successful at learning low-dimensional representations of high-dimensional point clo… (voir plus)uds on or near data manifolds. However, existing methods are not easily extensible—that is, for large datasets, it is prohibitively expensive to add new points to these embeddings. As a result, it is very difficult to use existing embeddings generatively, to sample new points on and along these manifolds. In this paper, we propose GAGA (geometry-aware generative autoencoders) a framework which merges the power of generative deep learning with non-linear manifold learning by: 1) learning generalizable geometry-aware neural network embeddings based on non-linear dimensionality reduction methods like PHATE and diffusion maps, 2) deriving a non-euclidean pullback metric on the embedded space to generate points faithfully along manifold geodesics, and 3) learning a flow on the manifold that allows us to transport populations. We provide illustration on easily-interpretable synthetic datasets and showcase results on simulated and real single cell datasets. In particular, we show that the geodesic-based generation can be especially important for scientific datasets where the manifold represents a state space and geodesics can represent dynamics of entities over this space.
2024-06-17
ICML.cc/2024/Workshop/GRaM (publié)
openreview.net
openreview.net
Is a Good Description Worth a Thousand Pictures? Reducing Multimodal Alignment to Text-Based, Unimodal Alignment
Amin Memarian
Touraj Laleh
Irina Rish
Ardavan S. Nobandegani
Generative AI systems (ChatGPT, Llama, etc.) are increasingly adopted across a range of high-stake domains, including healthcare and crimina… (voir plus)l justice system. This rapid adoption indeed raises moral and ethical concerns. The emerging field of AI alignment aims to make AI systems that respect human values. In this work, we focus on evaluating the ethics of multimodal AI systems involving both text and images --- a relatively under-explored area, as most alignment work is currently focused on language models. Specifically, here we investigate whether the multimodal alignment problem (i.e., the problem of aligning a multimodal system) could be effectively reduced to the (text-based) unimodal alignment problem, wherein a language model would make a moral judgment purely based on a description of an image. Focusing on GPT-4 and LLaVA as two prominent examples of multimodal systems, here we demonstrate, rather surprisingly, that this reduction can be achieved with a relatively small loss in moral judgment performance in the case of LLaVa, and virtually no loss in the case of GPT-4.
2024-06-17
ICML.cc/2024/Workshop/MFHAIA (poster)
openreview.net
openreview.net
IDs for AI Systems
Alan Chan
Noam Kolt
Peter Wills
Usman Anwar
Christian Schroeder de Witt
Nitarshan Rajkumar
Lewis Hammond
David Scott Krueger
Lennart Heim
Markus Anderljung
2024-06-17
ArXiv (prépublication)
doi.org
arxiv.org
IDs for AI Systems
Alan Chan
Noam Kolt
Peter Wills
Usman Anwar
Christian Schroeder de Witt
Nitarshan Rajkumar
Lewis Hammond
David Scott Krueger
Lennart Heim
Markus Anderljung
AI systems are increasingly pervasive, yet information needed to decide whether and how to engage with them may not exist or be accessible. … (voir plus)A user may not be able to verify whether a system has certain safety certifications. An investigator may not know whom to investigate when a system causes an incident. It may not be clear whom to contact to shut down a malfunctioning system. Across a number of domains, IDs address analogous problems by identifying particular entities (e.g., a particular Boeing 747) and providing information about other entities of the same class (e.g., some or all Boeing 747s). We propose a framework in which IDs are ascribed to instances of AI systems (e.g., a particular chat session with Claude 3), and associated information is accessible to parties seeking to interact with that system. We characterize IDs for AI systems, provide concrete examples where IDs could be useful, argue that there could be significant demand for IDs from key actors, analyze how those actors could incentivize ID adoption, explore a potential implementation of our framework for deployers of AI systems, and highlight limitations and risks. IDs seem most warranted in settings where AI systems could have a large impact upon the world, such as in making financial transactions or contacting real humans. With further study, IDs could help to manage a world where AI systems pervade society.
2024-06-17
ArXiv (prépublication)
doi.org
arxiv.org
IDs for AI Systems
Alan Chan
Noam Kolt
Peter Wills
Usman Anwar
Christian Schroeder de Witt
Nitarshan Rajkumar
Lewis Hammond
David Scott Krueger
Lennart Heim
Markus Anderljung
AI systems are increasingly pervasive, yet information needed to decide whether and how to engage with them may not exist or be accessible. … (voir plus)A user may not be able to verify whether a system has certain safety certifications. An investigator may not know whom to investigate when a system causes an incident. It may not be clear whom to contact to shut down a malfunctioning system. Across a number of domains, IDs address analogous problems by identifying particular entities (e.g., a particular Boeing 747) and providing information about other entities of the same class (e.g., some or all Boeing 747s). We propose a framework in which IDs are ascribed to instances of AI systems (e.g., a particular chat session with Claude 3), and associated information is accessible to parties seeking to interact with that system. We characterize IDs for AI systems, provide concrete examples where IDs could be useful, argue that there could be significant demand for IDs from key actors, analyze how those actors could incentivize ID adoption, explore a potential implementation of our framework for deployers of AI systems, and highlight limitations and risks. IDs seem most warranted in settings where AI systems could have a large impact upon the world, such as in making financial transactions or contacting real humans. With further study, IDs could help to manage a world where AI systems pervade society.
2024-06-17
ArXiv (prépublication)
doi.org
arxiv.org