The Computational Complexity of Circuit Discovery for Inner Interpretability
ESI Neuroscience, Max-Planck Society & University of Bristol（ESI神经科学研究所、马克斯·普朗克学会与布里斯托尔大学）；Goethe University Frankfurt（法兰克福大学）；Memorial University of Newfoundland（纽芬兰纪念大学）
Many proposed applications of neural networks in machine learning, cognitive/brain science, and society hinge on the feasibility of inner interpretability via circuit discovery. This calls for empirical and theoretical explorations of viable algorithmic options. Despite advances in the design and testing of heuristics, there are concerns about their scalability and faithfulness at a time when we lack understanding of the complexity properties of the problems they are deployed to solve. To address this, we study circuit discovery with classical and parameterized computational complexity theory: (1) we describe a conceptual scaffolding to reason about circuit finding queries in terms of affordances for description, explanation, prediction and control; (2) we formalize a comprehensive set of queries for mechanistic explanation, and propose a formal framework for their analysis; (3) we use it to settle the complexity of many query variants and relaxations of practical interest on multi-layer perceptrons. Our findings reveal a challenging complexity landscape. Many queries are intractable, remain fixed-parameter intractable relative to model/circuit features, and inapproximable under additive, multiplicative, and probabilistic approximation schemes. To navigate this landscape, we prove there exist transformations to tackle some of these hard problems with better-understood heuristics, and prove the tractability or fixed-parameter tractability of more modest queries which retain useful affordances. This framework allows us to understand the scope and limits of interpretability queries, explore viable options, and compare their resource demands on existing and future architectures.
OCDB: Revisiting Causal Discovery with a Comprehensive Benchmark and Evaluation Framework
National Engineering Research Center for Big Data Technology and System（国家大数据技术与系统工程研究中心）；School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）；Université de Montréal（蒙特利尔大学）；Mila（米拉研究所）；Canada CIFAR AI Chair（加拿大CIFAR人工智能主席项目）
Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning
Peking University（北京大学）；Software Engineering Institution（软件工程研究院）；Key Lab of High Confidence Software Technologies, Ministry of Education（高可信软件技术重点实验室（教育部））；Center on Frontiers of Computing Studies（计算机前沿研究中心）；National Engineering Research Center of Software Engineering（国家软件工程工程研究中心）
Guangdong Key Laboratory of Brain-Inspired Intelligent Computation（广东省脑启发智能计算重点实验室）；Southern University of Science and Technology（南方科技大学）；Research Institute of Trust-worthy Autonomous Systems（可信自主系统研究所）；School of Computer Science, University of Birmingham（伯明翰大学计算机科学学院）
MINAR: Mechanistic Interpretability for Neural Algorithmic Reasoning
Pacific Northwest National Laboratory, Richland, WA（太平洋西北国家实验室）；Halıcıoğlu Data Science Institute, University of California, San Diego, San Diego, CA（哈利奇奥格鲁数据科学研究所，加州大学圣地亚哥分校）；Department of Computer and Information Science, University of Pennsylvania, Pennsylvaina, PA（计算机与信息科学系，宾夕法尼亚大学）；Department of Mathematics, University of Washington, Seattle, WA（数学系，华盛顿大学）
Circuit Insights: Towards Interpretability Beyond Activations
Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute（人工智能系，弗劳恩霍夫海因里希·赫兹研究所）；Department of Electrical Engineering and Computer Science, Technische Universität Berlin（电气工程与计算机科学系，柏林技术大学）；BIFOLD - Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；Centre of eXplainable Artificial Intelligence, Technological University Dublin（可解释人工智能中心，都柏林技术大学）
