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Princeton AI Club speaker: Prof. Max Welling, August 15th at 10 AM ET
by Emily C. Lawrence 04 Aug '22

04 Aug '22

The Princeton AI Club is thrilled to announce its next guest speaker – [ https://staff.fnwi.uva.nl/m.welling/ | Max Welling ] from the University of Amsterdam! His talk will focus on how we can apply machine learning to scientific discovery If you want to about new opportunities to apply machine learning to scientific discovery, sign-up for his virtual talk [ https://forms.gle/ms5hewGsMc8SUgaL7 | here ] , and join us on August 15 th , at 10 am Eastern Time (ET) ! The full details are below. Speaker: Prof. Max Welling (University of Amsterdam) Title: Deep Learning and the Natural Sciences: A Perfect Marriage? Date: Monday, August 15 at 4:00PM CEST (which is 10:00 AM EST.) Abstract: Artificial Intelligence has always had a deep connection with the natural sciences. Artificial neural networks were first conceived as abstract ions of biological neural networks, as do many subsequent algorithms such as reinforcement learning. Neural networks have also been inspired by many concepts in physics, for example Hopfield networks are modelled after Ising models. More recently, concepts from physics such as symmetries and the renormalization group have also been incorporated in the design of novel deep architectures. With these new tools, deep learning has completely revolutionized fields such as Natural Language Processing, and Computer Vision. Also in the reverse direction, machine learning has contributed tremendously to the analysis of data from experiments, such as particle collision events at CERN. However, recently, a new scientific paradigm is emerging where deep learning models are accelerating and improving scientific simulations, dubbed by some the fifth scientific paradigm. In this talk I will first give an introduction to the opportunities that lie ahead of us to apply machine learning to scientific discovery. In the second half of the talk, I will talk in more technical detail about symmetries in deep learning and our latest ideas on how to model the hidden layers of a deep neural network as a field described by a partial differential equation. Amazingly, this field-view of the hidden layers of an artificial cortex can be extended to a quantum field and mapped to an optical quantum computer design. How useful this intellectual exercise is in terms of practical quantum computers I will leave to the imagination of the audience. Bio: Prof. Dr. Max Welling is a research chair in Machine Learning at the University of Amsterdam and a Distinguished Scientist at MSR. He is a fellow at the Canadian Institute for Advanced Research (CIFAR) and the European Lab for Learning and Intelligent Systems (ELLIS) where he also serves on the founding board. His previous appointm z ents include VP at Qualcomm Technologies, professor at UC Irvine, postdoc at U. Toronto and UCL under supervision of prof. Geoffrey Hinton, and postdoc at Caltech under supervision of prof. Pietro Perona. He finished his PhD in theoretical high energy physics under supervision of Nobel laureate prof. Gerard ‘t Hooft. Max Welling has served as associate editor in chief of IEEE TPAMI from 2011-2015, he serves on the advisory board of the Neurips foundation since 2015 and has been program chair and general chair of Neurips in 2013 and 2014 respectively. He was also program chair of AISTATS in 2009 and ECCV in 2016 and general chair of MIDL 2018. Max Welling is recipient of the ECCV Koenderink Prize in 2010 and the ICML Test of Time award in 2021. He directs the Amsterdam Machine Learning Lab (AMLAB) and co-directs the Qualcomm-UvA deep learning lab (QUVA) and the Bosch-UvA Deep Learning lab (DELTA).

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Zhuqi Li will present his Pre FPO "Cross-layer optimization for video delivery on Wireless Networks" on Wednesday, August 10, 2022 at 3pm (EST) via Zoom.
by Nicki Mahler 03 Aug '22

03 Aug '22

Zhuqi Li will present his Pre FPO "Cross-layer optimization for video delivery on Wireless Networks" on Wednesday, August 10, 2022 at 3pm (EST) via Zoom. Zoom link: [ https://princeton.zoom.us/j/6871275896 | https://princeton.zoom.us/j/6871275896 ] The members of his committee are as follows: Advisor: Kyle Jamieson Readers: Victor Bahl (Microsoft), Amit Levy Examiners: Ravi Netravali, Jennifer Rexford All are welcome to attend. Abstract: A dominating portion of today's wireless network traffic is occupied by video content delivery for both video streaming and live video analytic purposes. The dynamic nature of wireless network presents significant challenges to both applications. On the one hand, the available throughput of the wireless network varies across time due to the change of the wireless channel and competing flows. On the other hand, the endpoints need to decide the video bitrate and routing selection without knowing the future throughput. In this talk, we will introduce three systems that optimize wireless video delivery from physical layer, network layer, and application layer to solve the challenges. We present the design and implementation of the following systems: (1) LAIA: a programmable wireless environment that allows rapid adjustments of the wireless channel to smooth the throughput variation. LAIA uses a large array of inexpensive antenna (LAIA) elements and includes algorithms to configure LAIA elements in real-time. (2) Spider: a multi-hop, millimeter-wave (mmWave) wireless relay network design to maximize end-to-end video analytic accuracy for a large camera cluster. Spider integrates a low-latency Wi-Fi control plane with a mmWave relay data plane, allowing agile re-routing around blockages. Spider proposes a novel video bit-rate allocation algorithm coupled with a scalable routing algorithm that works hand-in-hand toward the application-level objective of maximizing video analytics accuracy, rather than simply maximizing data throughput. (3) Dashlet: a system tailored for the high quality of experience in short video streaming applications. Dashlet proposes a novel out-of-order video chunk pre-buffering mechanism that leverages users’ swipe statistics as well as network throughput history to determine the pre-buffering order and bitrate.

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Seminar Series on Security & Privacy in Machine Learning
by Emily C. Lawrence 02 Aug '22

02 Aug '22

Challenges and Opportunities in Security & Privacy in Machine Learning Weekly talks starting Tuesday, June 7 About the seminar series The motivation for the seminar is to build a platform to discuss and disseminate the progress made by the community in solving some of the core challenges. We intend to host weekly talks from leading researchers in both academia and industry. Each session will be split into a talk (40 mins) followed by a Q&A + short discussion session (20 mins). Timing : Every Tuesday at 1pm Eastern Time (Virtual talks) Website: [ https://vsehwag.github.io/SPML_seminar/ | https://vsehwag.github.io/SPML_seminar/ ] Mailing list: [ https://groups.google.com/forum/#!forum/spml-seminars/join | Link to mailing list ] Calendar: [ https://calendar.google.com/calendar/u/0?cid=N2FwbTVxYzJsOGM2bXBiNGY4am1oMj… | Link to calendar ] You can find all additional details on the website. If you are interested, we recommend signing up for the mailing list and sync the calendar to stay up to date with the seminar schedule. -- Vikash Sehwag PhD Candidate Princeton University [ https://vsehwag.github.io/ | https://vsehwag.github.io ]

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Happening today! Our next guest speaker is Dr. Naila Murray from Facebook AI Research!
by Emily C. Lawrence 01 Aug '22

01 Aug '22

Happening today! The Princeton AI Club is excited to announce its next guest speaker – [ https://twitter.com/nailamurray | Dr. Naila Murray ] from Facebook AI Research! Her talk will focus on how to train deep vision models in low-data regimes If you want to hear all about how to train your network with little data, sign-up for her virtual talk [ https://forms.gle/fThq5gxa6Yzx1dkh8 | here ] , and join us on August 1 st , at 11 am Eastern Time (ET) ! The full details are below. Speaker: Dr. Naila Murray (Facebook AI Research) Title: Training deep vision models in low-data regimes Date: Monday, August 1st 2022, 11:00 AM ET Abstract: Large-scale vision models, trained on vast quantities of often unlabelled data, give state-of-the-art performance when fine-tuned for a wide variety of downstream tasks. For these downstream tasks, the amount of training data available is usually orders of magnitude less than what was used during pre-training. And in many cases, there is little to no labelled data available. In this talk, I discuss approaches to fine-tuning pre-trained models for vision tasks in low-data regimes. In all cases, which cover retrieval, video segmentation, and object detection, I show that incorporating domain-specific and modality-specific inductive biases lead to improved model performance when training data is limited. Bio: Naila Murray obtained a BSE in electrical engineering from Princeton University in 2007. In 2012, she received her Ph.D. from the Universitat Autonoma de Barcelona, in affiliation with the Computer Vision Center. She joined Xerox Research Centre Europe in 2013 as a research scientist in the computer vision team, working on topics including fine-grained visual categorization, image retrieval and visual attention. From 2015 to 2019 she led the computer vision team at Xerox Research Centre Europe, and continued to serve in this role after its acquisition and transition to becoming NAVER LABS Europe. In 2019, she became the director of science at NAVER LABS Europe. In 2020, she joined Facebook AI Research where she is a senior research engineering manager for EMEA. She has served as area chair for ICLR 2018, ICCV 2019, ICLR 2019, CVPR 2020, ECCV 2020, and program chair for ICLR 2021. Her current research interests include few-shot learning and domain adaptation.

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Princeton AI Club: Dr. Naila Murray from Facebook AI Research, August 1st 2022, at 11 AM ET
by Emily C. Lawrence 22 Jul '22

22 Jul '22

Reminder for our next guest speaker! The Princeton AI Club is excited to announce its next guest speaker – [ https://twitter.com/nailamurray | Dr. Naila Murray ] from Facebook AI Research! Her talk will focus on how to train deep vision models in low-data regimes If you want to hear all about how to train your network with little data, sign-up for her virtual talk [ https://forms.gle/fThq5gxa6Yzx1dkh8 | here ] , and join us on August 1 st , at 11 am Eastern Time (ET) ! The full details are below. Speaker: Dr. Naila Murray (Facebook AI Research) Title: Training deep vision models in low-data regimes Date: Monday, August 1st 2022, 11:00 AM ET Abstract: Large-scale vision models, trained on vast quantities of often unlabelled data, give state-of-the-art performance when fine-tuned for a wide variety of downstream tasks. For these downstream tasks, the amount of training data available is usually orders of magnitude less than what was used during pre-training. And in many cases, there is little to no labelled data available. In this talk, I discuss approaches to fine-tuning pre-trained models for vision tasks in low-data regimes. In all cases, which cover retrieval, video segmentation, and object detection, I show that incorporating domain-specific and modality-specific inductive biases lead to improved model performance when training data is limited. Bio: Naila Murray obtained a BSE in electrical engineering from Princeton University in 2007. In 2012, she received her Ph.D. from the Universitat Autonoma de Barcelona, in affiliation with the Computer Vision Center. She joined Xerox Research Centre Europe in 2013 as a research scientist in the computer vision team, working on topics including fine-grained visual categorization, image retrieval and visual attention. From 2015 to 2019 she led the computer vision team at Xerox Research Centre Europe, and continued to serve in this role after its acquisition and transition to becoming NAVER LABS Europe. In 2019, she became the director of science at NAVER LABS Europe. In 2020, she joined Facebook AI Research where she is a senior research engineering manager for EMEA. She has served as area chair for ICLR 2018, ICCV 2019, ICLR 2019, CVPR 2020, ECCV 2020, and program chair for ICLR 2021. Her current research interests include few-shot learning and domain adaptation.

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Kaiyu Yang will present his FPO "Neurosymbolic Machine Learning for Reasoning" on Thursday, July 28, 2022 at 3:00 PM in Friend 125 and Zoom.
by Louis W. Riehl 21 Jul '22

21 Jul '22

Kaiyu Yang will present his FPO "Neurosymbolic Machine Learning for Reasoning" on Thursday, July 28, 2022 at 3:00 PM in Friend 125 and Zoom. Location: Zoom link: [ https://princeton.zoom.us/j/2731344683 | https://princeton.zoom.us/j/2731344683 ] The members of Kaiyu’s committee are as follows: Examiners: Jia Deng (Adviser), Olga Russakovsky, Danqi Chen Readers: Karthik Narasimhan, Mayur Naik (University of Pennsylvania) A copy of his thesis is available upon request. Please email gradinfo(a)cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend his talk. Abstract follows below: Machine learning has made significant progress in the past decade. Its most successful paradigm, deep neural networks, consists of layers of continuous representation whose parameters are optimized on massive datasets via gradient descent. Deep neural networks have achieved superior performance on numerous tasks, such as object recognition, language understanding, and autonomous driving. However, they still struggle with reasoning tasks, which often require manipulating symbols and chaining multiple steps compositionally, e.g., solving math equations or writing computer programs. In this dissertation, we aim to bridge this gap and teach machines to reason—in ways that are precise, systematic, interpretable, and robust to ambiguity in real-world environments. To that end, we take a neurosymbolic approach combining the complementary strengths of machine learning and symbolic reasoning. Symbolic reasoning is precise and generalizes systematically. But it has been restricted to domains amenable to rigid formalization. In contrast, predominant machine learning methods are flexible but notoriously uninterpretable, data-hungry, and incapable of generalizing outside the training distribution. Integrating the strengths of both approaches is essential for building flexible reasoning machines with precise and systematic generalization. Concretely, this dissertation addresses neurosymbolic reasoning from two angles. First, we apply machine learning to tasks related to symbolic reasoning, such as automated theorem proving (Chapter 2). Second, we introduce inductive biases inspired by symbolic reasoning into machine learning models to improve their interpretability, generalization, and data efficiency (Chapter 3 and Chapter 4). Our results highlight the benefits of (1) neurosymbolic model architectures, (2) reasoning at a suitable level of abstraction, and (3) an explicit, compositional representation of reasoning, such as symbolic proofs. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

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Xingyuan Sun will present his Pre-FPO "Using Machine Learning to Improve Fiber-Reinforced 3D Printed Objects" on Thursday, July 28, 2022 at 11:00 AM in CS 402 and Zoom.
by Louis W. Riehl 21 Jul '22

21 Jul '22

Xingyuan Sun will present his Pre-FPO "Using Machine Learning to Improve Fiber-Reinforced 3D Printed Objects" on Thursday, July 28, 2022 at 11:00 AM in CS 402 and Zoom. Zoom link: [ https://princeton.zoom.us/j/95907350060 | https://princeton.zoom.us/j/95907350060 ] Committee Members: Ryan P. Adams (Examiner, adviser), Szymon Rusinkiewicz (Examiner, adviser), Olga Russakovsky (Examiner), Sigrid Adriaenssens (reader), Felix Heide (reader). All are welcome to attend. Title: Using Machine Learning to Improve Fiber-Reinforced 3D Printed Objects Abstract: This dissertation studies fiber layout planning given a shape and external loads using machine learning, improving the object’s stiffness. However, simply planning fiber paths is not sufficient. Since fibers are stiff, their shape is smoothed after extrusion. We thus need another planning algorithm to generate an extruder path given any desired fiber path. To solve this task, we learn a two-stage neural network that we may consider an autoencoder. A decoder is first learned as a differentiable surrogate of the printing process, taking an extruder path and producing a resulting fiber path. An encoder is then trained to produce an extruder path given a desired fiber path, using the trained decoder to evaluate the quality of the planned extruder path. We compare this approach with direct supervised learning of the extruder path planning problem and direct optimization of the extruder path. Besides, we also study our algorithm in another case study on a soft robot, showing our approach can be applied to design problems with a similar structure. Results from simulation and real experiments show our algorithm generates higher quality solutions than direct supervised learning and solutions with competitive quality with direct optimization but greatly reduced running time. To plan fiber paths, we formalize the task as an optimization problem, solving the fiber layout to maximize the object’s stiffness with regularizers forcing paths to be feasible. We initialize every fiber path by iteratively using finite element analysis to calculate the stress field of the object and a greedy algorithm to “walk” on the field. We then use the adjoint method to calculate the gradient of the regularized stiffness with respect to the fiber layout, and optimize the layout using a gradient-based optimizer. We compare our algorithm with three baselines, including concentric fiber rings generated by Eiger, a leading digital manufacturing software, and two others producing greedy fiber paths on the stress field and a fiber orientation field calculated by smoothing the stress field, respectively. Experiments on simulation and real printings show objects with fiber paths from our algorithm improve the Pareto front, achieving larger stiffness while using less fiber. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

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Zoe Ashwood will present her FPO "Probabilistic Models for Characterizing Animal Learning and Decision-Making" on Monday, July 25, 2022 at 9:00 AM in PNI 101 and Zoom.
by Louis W. Riehl 18 Jul '22

18 Jul '22

Zoe Ashwood will present her FPO "Probabilistic Models for Characterizing Animal Learning and Decision-Making" on Monday, July 25, 2022 at 9:00 AM in PNI 101 and Zoom. Location: PNI 101 Zoom link: [ https://princeton.zoom.us/j/93691725152?pwd=RlpLWkRObG9lMWs5cTAxZTRRSFVkZz09 | https://princeton.zoom.us/j/93691725152?pwd=RlpLWkRObG9lMWs5cTAxZTRRSFVkZz09 ] The members of Zoe’s committee are as follows: Examiners: Jonathan Pillow (Adviser), Ryan Adams, Tom Griffiths Readers: Barbara Engelhardt, Alexandre Pouget (University of Geneva) A copy of her thesis is available upon request. Please email gradinfo(a)cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend her talk. Abstract follows below: A central goal in neuroscience is to identify the strategies used by animals and humans as they make decisions, and to characterize the learning rules that shape these policies in the first place. In this thesis, I discuss three projects aimed at tackling this goal. In the first, we introduce a novel framework, the GLM-HMM, for characterizing mouse and human choice policies during binary decision-making tasks. The GLM-HMM is a hidden Markov model with Bernoulli Generalized Linear Model observations. By fitting the GLM-HMM to hundreds of thousands of decisions, our framework revealed that — contrary to common wisdom — mice and humans use multiple strategies over the course of a single session to perform perceptual decision-making tasks. In the second project, we sought to uncover the learning rules used by mice and rats as they learned to perform this type of task. Our model tracked trial-to-trial changes in the animals’ choice policies, and separated these changes into components explainable by a reinforcement learning rule, and components that remained unexplained. Whereas the average contribution of the conventional REINFORCE learning rule to the policy update for mice learning a common task was just 30%, we found that adding baseline parameters allowed the learning rule to explain 92% of the animals’ policy updates under our model. Finally, I discuss our approach to applying inverse reinforcement learning (IRL) to the trajectories of mice exploring a maze environment. While IRL has been widely applied in robotics and healthcare settings to infer the unknown reward function of an agent, it has yet to be applied extensively in neuroscience. One potential reason for this is that existing IRL frameworks assume that an agent’s reward function is fixed over time. To overcome this limitation, we introduce ‘DIRL’, an IRL framework that allows for time-varying intrinsic rewards. Our method returns interpretable reward functions for two separate cohorts of mice, and provides a novel characterization of exploratory behavior. Overall, we anticipate ‘DIRL’ having broad applicability in neuroscience, and that it could also facilitate the design of biologically-inspired reward functions for training artificial agents to perform analogous tasks. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

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Claudia Roberts will present her Pre FPO "Human-machine Cooperation in Machine-Learning Resistant Real-World Applications" on Thursday, July 14 2:00pm ET via Zoom.
by Nicki Mahler 12 Jul '22

12 Jul '22

Claudia Roberts will present her Pre FPO "Human-machine Cooperation in Machine-Learning Resistant Real-World Applications" on Thursday, July 14 2:00pm ET via Zoom. Zoom link: [ https://princeton.zoom.us/j/2202514985 | https://princeton.zoom.us/j/2202514985 ] Advisor: Arvind Narayanan (examiner) Committee members: Adji Bousso Dieng (reader), Andrés Monroy-Hernández (examiner), Barbara Engelhardt (reader), Matt Salganik (examiner) Title: Human-machine Cooperation in Machine-Learning Resistant Real-World Applications All are welcome to attend. Abstract: Automation tools like machine learning are a necessity in our big data world. However, not all applications lend themselves to machine-learning based automation. Numerous societal problems emerge when we attempt to measure and quantify something that resists measurement and subsequently, develop mathematical intermediaries to attempt to model human behavior. However, using machine-learning backed automation to reduce the scale of many real-world applications is still helpful. In this dissertation, we explore three different ways in which we can use human-machine cooperation in machine-learning resistant applications to keep or increase utility and reduce harm when using these systems in the wild. The first chapter, based on “Friend Request Pending: A Comparative Assessment of Engineering and Social Science Inspired Approaches to Analyzing Complex Birth Cohort Survey Data,” shows how the first mode of human-machine cooperation—explaining things to computers by adding domain knowledge—increases utility of machine learning models in the machine-learning resistant task of grade point average (GPA) prediction. The Fragile Families Challenge is a mass collaboration social science data challenge whose aim is to learn how various early childhood variables predict the long-term outcomes of children. We describe our two-step approach to the Fragile Families Challenge. In step 1, we use a variety of fully automated approaches to predict child academic achievement. We fit 124 models, which involve most possible combinations of 8 model types, 2 imputation strategies, 2 standardization approaches, and 2 automatic variable selection techniques using 2 different thresholds. Then, in step 2, we attempt to improve on the results from step 1 with manual variable selection based on a detailed review of the codebooks. We manually selected 3,694 variables believed to be predictive of academic achievement, using a comprehensive review of student success literature to guide the decision-making process. The best models from step 1 were re-estimated using the manually selected variables. We show that manual variable selection improved the majority of the top 10 models in step 1, but did not improve the best of the top 10. Results indicate that variable selection inspired by social science methodologies can, in most cases, significantly improve models trained completely automatically. The second chapter, based on “Selectively Contextual Bandits,” shows how the second mode of human-machine cooperation—humans and computers working together in decision making—maintains utility while reducing harm of machine learning models in the machine-learning resistant task of image personalization. Personalization is an integral part of most web-service applications and determines which experience to display to each member. A popular algorithmic framework used in industrial personalization systems are contextual bandits, which seek to learn a personalized treatment assignment policy in the presence of treatment effects that vary with the observed contextual features of the members. In order to keep the optimization task tractable, such systems can myopically make independent personalization decisions that can conspire to create a suboptimal experience in the aggregate of the member’s interaction with the web-service. We design a new family of online learning algorithms that benefit from personalization while optimizing the aggregate impact of the many independent decisions. Our approach selectively interpolates between any contextual bandit algorithm and any context-free multi-armed bandit algorithm and leverages the contextual information for a treatment decision only if this information promises significant gains over a decision that does not take it into account. Apart from helping users of personalization systems feel less targeted, simplifying the treatment assignment policy by making it selectively reliant on the context can help improve the rate of learning. We evaluate our approach on several datasets including a video subscription web-service and show the benefits of such a hybrid policy. Finally, the last chapter, based on “On the Bias-Variance Characteristics of LIME and SHAP in High Sparsity Movie Recommendation Explanation Tasks,” shows how the third mode of human-machine cooperation—computers explaining to humans what it’s thinking—can improve utility and reduce harm of machine learning models in the machine-learning resistant task of movie recommendations. We evaluate two popular local explainability techniques, LIME and SHAP, on a movie recommendation task. We discover that the two methods behave very differently depending on the sparsity of the data set. LIME does better than SHAP in dense segments of the data set and SHAP does better in sparse segments. We trace this difference to the differing bias-variance characteristics of the underlying estimators of LIME and SHAP. We find that SHAP exhibits lower variance in sparse segments of the data compared to LIME. We attribute this lower variance to the completeness constraint property inherent in SHAP and missing in LIME. This constraint acts as a regularizer and therefore increases the bias of the SHAP estimator but decreases its variance, leading to a favorable bias-variance trade-off especially in high sparsity data settings. With this insight, we introduce the same constraint into LIME and formulate a novel local explainabilty framework called Completeness-Constrained LIME (CLIMB) that is superior to LIME and much faster than SHAP.

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Princeton AI Club Seminar Series: Yoshua Bengio, July 14 at 4pm
by Emily C. Lawrence 08 Jul '22

08 Jul '22

Princeton AI Club Seminar Speaker : Prof. Yoshua Bengio Title : GFlowNets and AI for Science Date : Thursday, July 14 at 4:00 PM ET Location : Zoom (register to receive the link) Registration : [ https://forms.gle/iFoKEty2BVmp3nKQ9 | https://forms.gle/iFoKEty2BVmp3nKQ9 ] For more info : [ https://aiclub.princeton.edu/seminars/ | https://aiclub.princeton.edu/seminars/ ] Abstract : Machine learning research is expanding its reach, beyond the traditional realm of the tech industry and into the activities of other scientists, opening the door to truly transformative advances in these disciplines. In this talk I will focus on two aspects, modeling and experimental design, that are intertwined in the theory-experiment-analysis active learning loop that constitutes a core element of the scientific methodology. Computers will be necessary to go beyond the currently purely manual research loop and take advantage of high-throughput experimental setups and large-scale experimental datasets. I will introduce a novel machine learning framework called GFlowNets (for “Generative Flow Networks”), related to reinforcement learning, generative modeling and variational methods and conceived as an ML-driven replacement for MCMC. GFlowNets were first used to propose a highly diverse set of molecular candidates and were then incorporated in an active learning framework for efficiently looking for molecules with desirable properties. More recently, we have been exploring how GFlowNets can generate not just molecular graphs but also causal graphs and Bayesian posterior distributions in function space. I will describe our research program to build on these bases and develop machine learning methodologies for efficiently exploring the space of causal theories as well as the space of experiments while characterizing the ambiguities left by finite datasets and non-identifiability, as well as our plans to apply these tools in areas of great societal need like the unmet challenge of antimicrobial resistance. Bio : Recognized worldwide as one of the leading experts in artificial intelligence, Yoshua Bengio is most known for his pioneering work in deep learning, earning him the 2018 A.M. Turing Award, “the Nobel Prize of Computing,” with Geoffrey Hinton and Yann LeCun. He is a Full Professor at Université de Montréal, and the Founder and Scientific Director of Mila – Quebec AI Institute. He co-directs the CIFAR Learning in Machines & Brains program as Senior Fellow and acts as Scientific Director of IVADO. In 2019, he was awarded the prestigious Killam Prize and in 2021, became the second most cited computer scientist in the world. He is a Fellow of both the Royal Society of London and Canada, Knight of the Legion of Honor of France and Officer of the Order of Canada. Concerned about the social impact of AI and the objective that AI benefits all, he actively contributed to the Montreal Declaration for the Responsible Development of Artificial Intelligence.

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