publications

2024

1. ML

   Disentangling and Integrating Relational and Sensory Information in Transformer Architectures

   Awni Altabaa, and John Lafferty

   2024

   Abs arXiv Bib Code Website

   The Transformer architecture processes sequences by implementing a form of neural message-passing that consists of iterative information retrieval (attention), followed by local processing (position-wise MLP). Two types of information are essential under this general computational paradigm: "sensory" information about individual objects, and "relational" information describing the relationships between objects. Standard attention naturally encodes the former, but does not explicitly encode the latter. In this paper, we present an extension of Transformers where multi-head attention is augmented with two distinct types of attention heads, each routing information of a different type. The first type is the standard attention mechanism of Transformers, which captures object-level features, while the second type is a novel attention mechanism we propose to explicitly capture relational information. The two types of attention heads each possess different inductive biases, giving the resulting architecture greater efficiency and versatility. The promise of this approach is demonstrated empirically across a range of tasks.

   @misc{altabaa2024disentangling,
     title = {Disentangling and Integrating Relational and Sensory Information in Transformer Architectures},
     author = {Altabaa, Awni and Lafferty, John},
     year = {2024},
     eprint = {2405.16727},
     archiveprefix = {arXiv},
     primaryclass = {cs.LG}
   }

2. RL

   On the Role of Information Structure in Reinforcement Learning for Partially-Observable Sequential Teams and Games

   Awni Altabaa, and Zhuoran Yang

   Mar 2024

   Abs arXiv Bib

   In a sequential decision-making problem, the information structure is the description of how events in the system occurring at different points in time affect each other. Classical models of reinforcement learning (e.g., MDPs, POMDPs, Dec-POMDPs, and POMGs) assume a very simple and highly regular information structure, while more general models like predictive state representations do not explicitly model the information structure. By contrast, real-world sequential decision-making problems typically involve a complex and time-varying interdependence of system variables, requiring a rich and flexible representation of information structure. In this paper, we argue for the perspective that explicit representation of information structures is an important component of analyzing and solving reinforcement learning problems. We propose novel reinforcement learning models with an explicit representation of information structure, capturing classical models as special cases. We show that this leads to a richer analysis of sequential decision-making problems and enables more tailored algorithm design. In particular, we characterize the "complexity" of the observable dynamics of any sequential decision-making problem through a graph-theoretic analysis of the DAG representation of its information structure. The central quantity in this analysis is the minimal set of variables that d-separates the past observations from future observations. Furthermore, through constructing a generalization of predictive state representations, we propose tailored reinforcement learning algorithms and prove that the sample complexity is in part determined by the information structure. This recovers known tractability results and gives a novel perspective on reinforcement learning in general sequential decision-making problems, providing a systematic way of identifying new tractable classes of problems.

   @misc{altabaaRoleInformationStructure2024,
     title = {On the {{Role}} of {{Information Structure}} in {{Reinforcement Learning}} for {{Partially-Observable Sequential Teams}} and {{Games}}},
     author = {Altabaa, Awni and Yang, Zhuoran},
     year = {2024},
     month = mar,
     number = {arXiv:2403.00993},
     eprint = {2403.00993},
     primaryclass = {cs, stat},
     publisher = {arXiv},
     doi = {10.48550/arXiv.2403.00993},
     urldate = {2024-03-14},
     archiveprefix = {arxiv}
   }

3. ML

   Approximation of Relation Functions and Attention Mechanisms

   Awni Altabaa, and John Lafferty

   Feb 2024

   Abs arXiv Bib

   Inner products of neural network feature maps arises in a wide variety of machine learning frameworks as a method of modeling relations between inputs. This work studies the approximation properties of inner products of neural networks. It is shown that the inner product of a multi-layer perceptron with itself is a universal approximator for symmetric positive-definite relation functions. In the case of asymmetric relation functions, it is shown that the inner product of two different multi-layer perceptrons is a universal approximator. In both cases, a bound is obtained on the number of neurons required to achieve a given accuracy of approximation. In the symmetric case, the function class can be identified with kernels of reproducing kernel Hilbert spaces, whereas in the asymmetric case the function class can be identified with kernels of reproducing kernel Banach spaces. Finally, these approximation results are applied to analyzing the attention mechanism underlying Transformers, showing that any retrieval mechanism defined by an abstract preorder can be approximated by attention through its inner product relations. This result uses the Debreu representation theorem in economics to represent preference relations in terms of utility functions.

   @misc{altabaaApproximationRelationFunctions2024,
     title = {Approximation of Relation Functions and Attention Mechanisms},
     author = {Altabaa, Awni and Lafferty, John},
     year = {2024},
     month = feb,
     number = {arXiv:2402.08856},
     eprint = {2402.08856},
     primaryclass = {cs, stat},
     publisher = {arXiv},
     doi = {10.48550/arXiv.2402.08856},
     urldate = {2024-03-14},
     archiveprefix = {arxiv}
   }

2023

1. ML

   Learning Hierarchical Relational Representations through Relational Convolutions

   Awni Altabaa, and John Lafferty

   Oct 2023

   Abs arXiv Bib Code Website

   A maturing area of research in deep learning is the study of architectures and inductive biases for learning representations of relational features. In this paper, we focus on the problem of learning representations of hierarchical relations, proposing an architectural framework we call "relational convolutional networks". Given a collection of objects, pairwise relations are modeled via inner products of feature maps. We formalize a relational convolution operation in which graphlet filters are matched against patches of the input (i.e, groupings of objects), capturing the relational pattern in each group of objects. We also propose mechanisms for explicitly learning groupings of objects which are relevant to the downstream task. Composing these operations yields representations of higher-order, hierarchical relations. We present the motivation and details of the architecture, together with a set of experiments to demonstrate how relational convolutional networks can provide an effective framework for modeling relational tasks that have hierarchical structure.

   @misc{altabaaRelationalConvolutionalNetworks2023,
     title = {Learning Hierarchical Relational Representations through Relational Convolutions},
     shorttitle = {Relational Convolutional Networks},
     author = {Altabaa, Awni and Lafferty, John},
     year = {2023},
     month = oct,
     eprint = {2310.03240},
     archiveprefix = {arXiv},
     primaryclass = {cs.LG}
   }

2. Neuro/ML

   The Relational Bottleneck as an Inductive Bias for Efficient Abstraction

   Taylor W. Webb, Steven M. Frankland, Awni Altabaa, and 6 more authors

   Trends in Cognitive Science (TICS), Sep 2023

   Abs arXiv Bib

   A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience. This effort has often been framed in terms of a dichotomy between empiricist and nativist approaches, most recently embodied by debates concerning deep neural networks and symbolic cognitive models. Here, we highlight a recently emerging line of work that suggests a novel reconciliation of these approaches, by exploiting an inductive bias that we term the relational bottleneck. We review a family of models that employ this approach to induce abstractions in a data-efficient manner, emphasizing their potential as candidate models for the acquisition of abstract concepts in the human mind and brain.

   @article{webbRelationalBottleneckInductive2023,
     title = {The Relational Bottleneck as an Inductive Bias for Efficient Abstraction},
     author = {Webb, Taylor W. and Frankland, Steven M. and Altabaa, Awni and Krishnamurthy, Kamesh and Campbell, Declan and Russin, Jacob and O'Reilly, Randall and Lafferty, John and Cohen, Jonathan D.},
     year = {2023},
     month = sep,
     journal = {Trends in Cognitive Science (TICS)}
   }

3. ML

   Abstractors and Relational Cross-Attention: An Inductive Bias for Explicit Relational Reasoning in Transformers

   Awni Altabaa, Taylor Webb, Jonathan Cohen, and 1 more author

   12th International Conference on Learning Representations (ICLR), Apr 2023

   Abs arXiv Bib Code Website Venue

   An extension of Transformers is proposed that enables explicit relational reasoning through a novel module called the Abstractor. At the core of the Abstractor is a variant of attention called relational cross-attention. The approach is motivated by an architectural inductive bias for relational learning that disentangles relational information from extraneous features about individual objects. This enables explicit relational reasoning, supporting abstraction and generalization from limited data. The Abstractor is first evaluated on simple discriminative relational tasks and compared to existing relational architectures. Next, the Abstractor is evaluated on purely relational sequence-to-sequence tasks, where dramatic improvements are seen in sample efficiency compared to standard Transformers. Finally, Abstractors are evaluated on a collection of tasks based on mathematical problem solving, where modest but consistent improvements in performance and sample efficiency are observed.

   @article{altabaaAbstractorsRelationalCrossattention2023,
     venue = {https://openreview.net/forum?id=XNa6r6ZjoB},
     title = {Abstractors and Relational Cross-Attention: An Inductive Bias for Explicit Relational Reasoning in Transformers},
     shorttitle = {Abstractors and Relational Cross-Attention},
     author = {Altabaa, Awni and Webb, Taylor and Cohen, Jonathan and Lafferty, John},
     journal = {12th International Conference on Learning Representations (ICLR)},
     year = {2023},
     month = apr,
     number = {arXiv:2304.00195},
     eprint = {2304.00195},
     primaryclass = {cs, stat},
     publisher = {{arXiv}},
     urldate = {2023-10-30},
     archiveprefix = {arxiv}
   }

4. RL

   Decentralized Multi-Agent Reinforcement Learning for Continuous-Space Stochastic Games

   Awni Altabaa, Bora Yongacoglu, and Serdar Yüksel

   2023 IEEE American Control Conference (ACC), Mar 2023

   Abs arXiv Bib Code Venue

   Stochastic games are a popular framework for studying multi-agent reinforcement learning (MARL). Recent advances in MARL have focused primarily on games with finitely many states. In this work, we study multi-agent learning in stochastic games with general state spaces and an information structure in which agents do not observe each other’s actions. In this context, we propose a decentralized MARL algorithm and we prove the near-optimality of its policy updates. Furthermore, we study the global policy-updating dynamics for a general class of best-reply based algorithms and derive a closed-form characterization of convergence probabilities over the joint policy space.

   @article{altabaaDecentralizedMultiAgentReinforcement2023,
     venue = {https://ieeexplore.ieee.org/abstract/document/10155828},
     title = {Decentralized Multi-Agent Reinforcement Learning for Continuous-Space Stochastic Games},
     author = {Altabaa, Awni and Yongacoglu, Bora and Y{\"u}ksel, Serdar},
     year = {2023},
     month = mar,
     journal = {2023 IEEE American Control Conference (ACC)}
   }

2022

1. Undergrad Project

   geneDRAGNN: Gene Disease Prioritization Using Graph Neural Networks

   Awni Altabaa, David Huang, Ciaran Byles-Ho, and 3 more authors

   2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Aug 2022

   Abs Venue

   Many human diseases exhibit a complex genetic etiology impacted by various genes and proteins in a large network of interactions. The process of evaluating gene-disease associations through in-vivo experiments is both time-consuming and expensive. Thus, network-based computational methods capable of modeling the complex interplay between molecular components can lead to more targeted evaluation. In this paper, we propose and evaluate geneDRAGNN: a general data processing and machine learning methodology for exploiting information about gene-gene interaction networks for predicting gene-disease association. We demonstrate that information derived from the gene-gene interaction network can significantly improve the performance of gene-disease association prediction models. We apply this methodology to lung adenocarcinoma, a histological subtype of lung cancer. We identify new potential gene-disease associations and provide supportive evidence for the association through gene-set enrichment and literature based analysis.