Timezone: »
In this panel we will discuss the following topics
- How close is the industry to creating AI native databases?
- How fast is research progressing in the field of bringing AI and databases close
- SQL is the most popular language in databases and pytorch/tensorflow are very popular in AI. Can they converge or we need a new language?
- Can existing databases be modified to host AI algorithms or we need to work from scratch?
- From a systems perspective is the GPU adoption of databases an opportunity to implement AI algorithms
- AI models are approaching the ~1TB and they contain Trillions of parameters. Are databaases ready to host/manage these type of models?
Author Information
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.
Alexander Ratner (Stanford University)
Benjamin Moseley (Carnegie Mellon University)
Konstantinos Karanasos (Microsoft)
Parisa Kordjamshidi (Michigan State University)
Parisa Kordjamshidi is an assistant professor of Computer Science & Engineering at Michigan State University. Her research interests are machine learning, natural language processing, and declarative learning-based programming. She has worked on the extraction of formal semantics and structured representations from natural language. She obtained NSF CAREER award on 2019. She is leading a project supported by Office of Naval research to perform basic research and develop a declarative learning-based programming framework for integration of domain knowledge into statistical/neural learning. She is a member of Editorial board of Journal of Artificial Intelligence Research (JAIR), a member of Editorial Board of Machine Learning and Artificial Intelligence, part of the journal of Frontiers in Artificial Intelligence and Frontiers in Big Data. She has published papers, organized international workshops and served as a (senior) program committee member or area chair of conferences such as IJCAI, AAAI, ACL, EMNLP, COLING, ECAI and a member of organizing committee of EMNLP-2021, ECML-PKDD-2019 and NAACL-2018 conferences.
Molham Aref (RelationalAI)
Arun Kumar (UC San Diego)
Arun Kumar is an Associate Professor in the Department of Computer Science and Engineering and the Halicioglu Data Science Institute and an HDSI Faculty Fellow at the University of California, San Diego. His primary research interests are in data management and systems for machine learning/artificial intelligence-based data analytics.
More from the Same Authors
-
2021 Spotlight: Tractable Regularization of Probabilistic Circuits »
Anji Liu · Guy Van den Broeck -
2021 : WRENCH: A Comprehensive Benchmark for Weak Supervision »
Jieyu Zhang · Yue Yu · · Yujing Wang · Yaming Yang · Mao Yang · Alexander Ratner -
2023 Poster: Online List Labeling with Predictions »
Samuel McCauley · Benjamin Moseley · Aidin Niaparast · Shikha Singh -
2023 Poster: A Pseudo-Semantic Loss for Deep Generative Models with Logical Constraints »
Kareem Ahmed · Kai-Wei Chang · Guy Van den Broeck -
2023 Poster: Collapsed Inference for Bayesian Deep Learning »
Zhe Zeng · Guy Van den Broeck -
2023 Poster: Characterizing the Impacts of Semi-supervised Learning for Weak Supervision »
Jeffrey Li · Jieyu Zhang · Ludwig Schmidt · Alexander Ratner -
2023 Poster: A Unified Approach to Count-Based Weakly Supervised Learning »
Vinay Shukla · Zhe Zeng · Kareem Ahmed · Guy Van den Broeck -
2023 Poster: On the Trade-off of Intra-/Inter-class Diversity for Supervised Pre-training »
Jieyu Zhang · Bohan Wang · Zhengyu Hu · Pang Wei Koh · Alexander Ratner -
2023 Poster: DataComp: In search of the next generation of multimodal datasets »
Samir Yitzhak Gadre · Gabriel Ilharco · Alex Fang · Jonathan Hayase · Georgios Smyrnis · Thao Nguyen · Ryan Marten · Mitchell Wortsman · Dhruba Ghosh · Jieyu Zhang · Eyal Orgad · Rahim Entezari · Giannis Daras · Sarah Pratt · Vivek Ramanujan · Yonatan Bitton · Kalyani Marathe · Stephen Mussmann · Richard Vencu · Mehdi Cherti · Ranjay Krishna · Pang Wei Koh · Olga Saukh · Alexander Ratner · Shuran Song · Hannaneh Hajishirzi · Ali Farhadi · Romain Beaumont · Sewoong Oh · Alex Dimakis · Jenia Jitsev · Yair Carmon · Vaishaal Shankar · Ludwig Schmidt -
2023 Poster: Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias »
Yue Yu · Yuchen Zhuang · Jieyu Zhang · Yu Meng · Alexander Ratner · Ranjay Krishna · Jiaming Shen · Chao Zhang -
2023 Oral: DataComp: In search of the next generation of multimodal datasets »
Samir Yitzhak Gadre · Gabriel Ilharco · Alex Fang · Jonathan Hayase · Georgios Smyrnis · Thao Nguyen · Ryan Marten · Mitchell Wortsman · Dhruba Ghosh · Jieyu Zhang · Eyal Orgad · Rahim Entezari · Giannis Daras · Sarah Pratt · Vivek Ramanujan · Yonatan Bitton · Kalyani Marathe · Stephen Mussmann · Richard Vencu · Mehdi Cherti · Ranjay Krishna · Pang Wei Koh · Olga Saukh · Alexander Ratner · Shuran Song · Hannaneh Hajishirzi · Ali Farhadi · Romain Beaumont · Sewoong Oh · Alex Dimakis · Jenia Jitsev · Yair Carmon · Vaishaal Shankar · Ludwig Schmidt -
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 : Panel »
Mayee Chen · Alexander Ratner · Robert Nowak · Cody Coleman · Ramya Korlakai Vinayak -
2022 : Panel »
Guy Van den Broeck · Cassio de Campos · Denis Maua · Kristian Kersting · Rianne van den Berg -
2022 Poster: Semantic Probabilistic Layers for Neuro-Symbolic Learning »
Kareem Ahmed · Stefano Teso · Kai-Wei Chang · Guy Van den Broeck · Antonio Vergari -
2022 Poster: Sparse Probabilistic Circuits via Pruning and Growing »
Meihua Dang · Anji Liu · Guy Van den Broeck -
2022 Poster: Understanding Programmatic Weak Supervision via Source-aware Influence Function »
Jieyu Zhang · Haonan Wang · Cheng-Yu Hsieh · Alexander Ratner -
2022 Poster: Algorithms with Prediction Portfolios »
Michael Dinitz · Sungjin Im · Thomas Lavastida · Benjamin Moseley · Sergei Vassilvitskii -
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 : Deep Learning with Relations »
Molham Aref -
2021 : The New DBfication of ML/AI »
Arun Kumar -
2021 Workshop: Databases and AI (DBAI) »
Nikolaos Vasiloglou · Parisa Kordjamshidi · Zenna Tavares · Maximilian Schleich · Nantia Makrynioti · Kirk Pruhs -
2021 Poster: Robust Online Correlation Clustering »
Silvio Lattanzi · Benjamin Moseley · Sergei Vassilvitskii · Yuyan Wang · Rudy Zhou -
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: Faster Matchings via Learned Duals »
Michael Dinitz · Sungjin Im · Thomas Lavastida · Benjamin Moseley · Sergei Vassilvitskii -
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 : WRENCH: A Comprehensive Benchmark for Weak Supervision »
Jieyu Zhang · Yue Yu · · Yujing Wang · Yaming Yang · Mao Yang · Alexander Ratner -
2021 Poster: Faster Matchings via Learned Duals »
Michael Dinitz · Sungjin Im · Thomas Lavastida · Benjamin Moseley · Sergei Vassilvitskii -
2021 Poster: Tractable Regularization of Probabilistic Circuits »
Anji Liu · Guy Van den Broeck -
2020 : Q & A and Panel Session with Dan Weld, Kristen Grauman, Scott Yih, Emma Brunskill, and Alex Ratner »
Kristen Grauman · Wen-tau Yih · Alexander Ratner · Emma Brunskill · Douwe Kiela · Daniel S. Weld -
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 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: Fair Hierarchical Clustering »
Sara Ahmadian · Alessandro Epasto · Marina Knittel · Ravi Kumar · Mohammad Mahdian · Benjamin Moseley · Philip Pham · Sergei Vassilvitskii · Yuyan Wang -
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: Backprop with Approximate Activations for Memory-efficient Network Training »
Ayan Chakrabarti · Benjamin Moseley -
2019 Poster: Cost Effective Active Search »
Shali Jiang · Roman Garnett · Benjamin Moseley -
2019 Poster: Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices »
Vincent Chen · Sen Wu · Alexander Ratner · Jen Weng · Christopher Ré -
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: Efficient nonmyopic batch active search »
Shali Jiang · Gustavo Malkomes · Matthew Abbott · Benjamin Moseley · Roman Garnett -
2018 Spotlight: Efficient nonmyopic batch active search »
Shali Jiang · Gustavo Malkomes · Matthew Abbott · Benjamin Moseley · Roman Garnett -
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 -
2017 Workshop: Learning with Limited Labeled Data: Weak Supervision and Beyond »
Isabelle Augenstein · Stephen Bach · Eugene Belilovsky · Matthew Blaschko · Christoph Lampert · Edouard Oyallon · Emmanouil Antonios Platanios · Alexander Ratner · Christopher Ré -
2017 : Coffee break and Poster Session II »
Mohamed Kane · Albert Haque · Vagelis Papalexakis · John Guibas · Peter Li · Carlos Arias · Eric Nalisnick · Padhraic Smyth · Frank Rudzicz · Xia Zhu · Theodore Willke · Noemie Elhadad · Hans Raffauf · Harini Suresh · Paroma Varma · Yisong Yue · Ognjen (Oggi) Rudovic · Luca Foschini · Syed Rameel Ahmad · Hasham ul Haq · Valerio Maggio · Giuseppe Jurman · Sonali Parbhoo · Pouya Bashivan · Jyoti Islam · Mirco Musolesi · Chris Wu · Alexander Ratner · Jared Dunnmon · Cristóbal Esteban · Aram Galstyan · Greg Ver Steeg · Hrant Khachatrian · Marc Górriz · Mihaela van der Schaar · Anton Nemchenko · Manasi Patwardhan · Tanay Tandon -
2017 Workshop: Machine Learning for Health (ML4H) - What Parts of Healthcare are Ripe for Disruption by Machine Learning Right Now? »
Jason Fries · Alex Wiltschko · Andrew Beam · Isaac S Kohane · Jasper Snoek · Peter Schulam · Madalina Fiterau · David Kale · Rajesh Ranganath · Bruno Jedynak · Michael Hughes · Tristan Naumann · Natalia Antropova · Adrian Dalca · SHUBHI ASTHANA · Prateek Tandon · Jaz Kandola · Uri Shalit · Marzyeh Ghassemi · Tim Althoff · Alexander Ratner · Jumana Dakka -
2017 Poster: Learning to Compose Domain-Specific Transformations for Data Augmentation »
Alexander Ratner · Henry Ehrenberg · Zeshan Hussain · Jared Dunnmon · Christopher Ré -
2017 Poster: Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search »
Benjamin Moseley · Joshua Wang -
2017 Oral: Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search »
Benjamin Moseley · Joshua Wang -
2016 Poster: New Liftable Classes for First-Order Probabilistic Inference »
Seyed Mehran Kazemi · Angelika Kimmig · Guy Van den Broeck · David Poole -
2016 Poster: Data Programming: Creating Large Training Sets, Quickly »
Alexander Ratner · Christopher M De Sa · Sen Wu · Daniel Selsam · Christopher Ré -
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