Timezone: »
We design a predictive layer for structured-output prediction (SOP) that can be plugged into any neural network guaranteeing its predictions are consistent with a set of predefined symbolic constraints. Our Semantic Probabilistic Layer (SPL) can model intricate correlations, and hard constraints, over a structured output space all while being amenable to end-to-end learning via maximum likelihood.SPLs combine exact probabilistic inference with logical reasoning in a clean and modular way, learning complex distributions and restricting their support to solutions of the constraint. As such, they can faithfully, and efficiently, model complex SOP tasks beyond the reach of alternative neuro-symbolic approaches. We empirically demonstrate that SPLs outperform these competitors in terms of accuracy on challenging SOP tasks such as hierarchical multi-label classification, pathfinding and preference learning, while retaining perfect constraint satisfaction.
Author Information
Kareem Ahmed (UCLA)
Stefano Teso (University of Trento)
Kai-Wei Chang (UCLA)
Guy Van den Broeck (UCLA)
I am an Assistant Professor and Samueli Fellow at UCLA, in the Computer Science Department, where I direct the Statistical and Relational Artificial Intelligence (StarAI) lab. My research interests are in Machine Learning (Statistical Relational Learning, Tractable Learning), Knowledge Representation and Reasoning (Graphical Models, Lifted Probabilistic Inference, Knowledge Compilation), Applications of Probabilistic Reasoning and Learning (Probabilistic Programming, Probabilistic Databases), and Artificial Intelligence in general.
Antonio Vergari (University of Edinburgh)
More from the Same Authors
-
2021 Spotlight: Interactive Label Cleaning with Example-based Explanations »
Stefano Teso · Andrea Bontempelli · Fausto Giunchiglia · Andrea Passerini -
2021 Spotlight: Tractable Regularization of Probabilistic Circuits »
Anji Liu · Guy Van den Broeck -
2022 : Group Excess Risk Bound of Overparameterized Linear Regression with Constant-Stepsize SGD »
Arjun Subramonian · Levent Sagun · Kai-Wei Chang · Yizhou Sun -
2022 : Empowering Language Models with Knowledge Graph Reasoning for Question Answering »
Ziniu Hu · Yichong Xu · Wenhao Yu · Shuohang Wang · Ziyi Yang · Chenguang Zhu · Kai-Wei Chang · Yizhou Sun -
2022 : GlanceNets: Interpretable, Leak-proof Concept-based Models »
Emanuele Marconato · Andrea Passerini · Stefano Teso -
2022 : Panel Discussion: "Heading for a Unifying View on nCSI" »
Tobias Gerstenberg · Sriraam Natarajan · - Mausam · Guy Van den Broeck · Devendra Dhami -
2022 : AI can learn from data. But can it learn to reason? »
Guy Van den Broeck -
2022 : GlanceNets: Interpretable, Leak-proof Concept-based Models »
Emanuele Marconato · Andrea Passerini · Stefano Teso -
2022 Spotlight: GlanceNets: Interpretable, Leak-proof Concept-based Models »
Emanuele Marconato · Andrea Passerini · Stefano Teso -
2022 : Panel »
Guy Van den Broeck · Cassio de Campos · Denis Maua · Kristian Kersting · Rianne van den Berg -
2022 : Q & A »
Antonio Vergari · YooJung Choi · Robert Peharz -
2022 Tutorial: Probabilistic Circuits: Representations, Inference, Learning and Applications »
Antonio Vergari · YooJung Choi · Robert Peharz -
2022 : Tutorial part 1 »
Antonio Vergari · YooJung Choi · Robert Peharz -
2022 : Inferring mood disorder symptoms from multivariate time-series sensory data »
Bryan Li · Filippo Corponi · Gerard Anmella · Ariadna Mas Musons · Miriam Sanabra · Diego Hidalgo-Mazzei · Antonio Vergari -
2022 Poster: On the Discrimination Risk of Mean Aggregation Feature Imputation in Graphs »
Arjun Subramonian · Kai-Wei Chang · Yizhou Sun -
2022 Poster: Sparse Probabilistic Circuits via Pruning and Growing »
Meihua Dang · Anji Liu · Guy Van den Broeck -
2022 Poster: Controllable Text Generation with Neurally-Decomposed Oracle »
Tao Meng · Sidi Lu · Nanyun Peng · Kai-Wei Chang -
2022 Poster: GlanceNets: Interpretable, Leak-proof Concept-based Models »
Emanuele Marconato · Andrea Passerini · Stefano Teso -
2022 Poster: Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering »
Pan Lu · Swaroop Mishra · Tanglin Xia · Liang Qiu · Kai-Wei Chang · Song-Chun Zhu · Oyvind Tafjord · Peter Clark · Ashwin Kalyan -
2021 Workshop: Advances in Programming Languages and Neurosymbolic Systems (AIPLANS) »
Breandan Considine · Disha Shrivastava · David Yu-Tung Hui · Chin-Wei Huang · Shawn Tan · Xujie Si · Prakash Panangaden · Guy Van den Broeck · Daniel Tarlow -
2021 : AI workloads inside databases »
Guy Van den Broeck · Alexander Ratner · Benjamin Moseley · Konstantinos Karanasos · Parisa Kordjamshidi · Molham Aref · Arun Kumar -
2021 Poster: Interactive Label Cleaning with Example-based Explanations »
Stefano Teso · Andrea Bontempelli · Fausto Giunchiglia · Andrea Passerini -
2021 Poster: A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference »
Antonio Vergari · YooJung Choi · Anji Liu · Stefano Teso · Guy Van den Broeck -
2021 : PYLON: A PyTorch Framework for Learning with Constraints »
Kareem Ahmed · Tao Li · Nu Mai Thy Ton · Quan Guo · Kai-Wei Chang · Parisa Kordjamshidi · Vivek Srikumar · Guy Van den Broeck · Sameer Singh -
2021 Oral: A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference »
Antonio Vergari · YooJung Choi · Anji Liu · Stefano Teso · Guy Van den Broeck -
2021 Poster: Tractable Regularization of Probabilistic Circuits »
Anji Liu · Guy Van den Broeck -
2020 : Contributed talks 6: Group Fairness by Probabilistic Modeling with Latent Fair Decisions »
YooJung Choi · Guy Van den Broeck -
2020 Poster: Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations »
Zhe Zeng · Paolo Morettin · Fanqi Yan · Antonio Vergari · Guy Van den Broeck -
2020 Poster: Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond »
Kaidi Xu · Zhouxing Shi · Huan Zhang · Yihan Wang · Kai-Wei Chang · Minlie Huang · Bhavya Kailkhura · Xue Lin · Cho-Jui Hsieh -
2020 Spotlight: Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations »
Zhe Zeng · Paolo Morettin · Fanqi Yan · Antonio Vergari · Guy Van den Broeck -
2020 Poster: Efficient Generation of Structured Objects with Constrained Adversarial Networks »
Luca Di Liello · Pierfrancesco Ardino · Jacopo Gobbi · Paolo Morettin · Stefano Teso · Andrea Passerini -
2020 Poster: Counterexample-Guided Learning of Monotonic Neural Networks »
Aishwarya Sivaraman · Golnoosh Farnadi · Todd Millstein · Guy Van den Broeck -
2019 : Invited Talk (Guy Van den Broeck) »
Guy Van den Broeck -
2019 Poster: Towards Hardware-Aware Tractable Learning of Probabilistic Models »
Laura Galindez Olascoaga · Wannes Meert · Nimish Shah · Marian Verhelst · Guy Van den Broeck -
2019 Poster: On Tractable Computation of Expected Predictions »
Pasha Khosravi · YooJung Choi · Yitao Liang · Antonio Vergari · Guy Van den Broeck -
2019 Poster: Smoothing Structured Decomposable Circuits »
Andy Shih · Guy Van den Broeck · Paul Beame · Antoine Amarilli -
2019 Spotlight: Smoothing Structured Decomposable Circuits »
Andy Shih · Guy Van den Broeck · Paul Beame · Antoine Amarilli -
2018 Poster: Approximate Knowledge Compilation by Online Collapsed Importance Sampling »
Tal Friedman · Guy Van den Broeck -
2018 Oral: Approximate Knowledge Compilation by Online Collapsed Importance Sampling »
Tal Friedman · Guy Van den Broeck -
2017 Workshop: NIPS Highlights (MLTrain), Learn How to code a paper with state of the art frameworks »
Alex Dimakis · Nikolaos Vasiloglou · Guy Van den Broeck · Alexander Ihler · Assaf Araki -
2016 Poster: New Liftable Classes for First-Order Probabilistic Inference »
Seyed Mehran Kazemi · Angelika Kimmig · Guy Van den Broeck · David Poole -
2016 Poster: Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings »
Tolga Bolukbasi · Kai-Wei Chang · James Y Zou · Venkatesh Saligrama · Adam T Kalai -
2016 Poster: A Credit Assignment Compiler for Joint Prediction »
Kai-Wei Chang · He He · Stephane Ross · Hal Daumé III · John Langford -
2015 Poster: Tractable Learning for Complex Probability Queries »
Jessa Bekker · Jesse Davis · Arthur Choi · Adnan Darwiche · Guy Van den Broeck -
2013 Poster: On the Complexity and Approximation of Binary Evidence in Lifted Inference »
Guy Van den Broeck · Adnan Darwiche -
2013 Spotlight: On the Complexity and Approximation of Binary Evidence in Lifted Inference »
Guy Van den Broeck · Adnan Darwiche -
2011 Poster: On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference »
Guy Van den Broeck -
2011 Oral: On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference »
Guy Van den Broeck