Yves Le Yaouanq: Recipient of an ERC Consolidator Grant in Economics


In this latest edition of CRESTive Minds, we are proud to feature Yves Le Yaouanq, researcher at CREST-Ecole polytechnique, a recent recipient of the prestigious ERC Consolidator Grant for his PROSPECT project. Specializing in behavioral economics, Yves explores the cognitive biases that shape human decision-making, with a focus on prospective learning and dynamic choice.

In this interview, he shares insights into his academic journey, the inspirations behind his research, and the challenges of designing experiments that bridge theory and real-world application.

Join us as we delve into the innovative work that earned Yves this remarkable recognition.

Academic Journey

Question: Could you tell us about your academic journey and what led you to specialize in behavioral economics?

I began a doctorate at the Toulouse School of Economics focusing on environmental economics topics related to climate policies. This literature often approaches the problem from a normative angle (what is the best way to make the ecological transition?), but I was quickly drawn to a more descriptive question: why do we do so little? This was in the early 2010s, at a time when climate denial was still quite present in our societies. This questioning led me to discover the work of psychologists, as well as economists, on the subject of belief biases and their social implications. I found these subjects fascinating because they mobilize a very broad spectrum of methods: psychological intuition (even introspection), modeling, then experimentation and quantification. I then continued my work in this literature throughout my career, at the University of Munich after my thesis, and at CREST since 2021, while keeping an eye on topics in political economy.

Which experiences or encounters particularly influenced your research choices?

I was particularly influenced by my encounter with Roland Bénabou (Princeton University) and Jean Tirole (Toulouse School of Economics), who both supervised me during my thesis, and by reading their work. They were among the first to show how economic tools, like game theory, could help us better think about objects that we rarely associate with economists’ work, such as emotions: anxiety, desire for recognition, regret… Roland Bénabou and Jean Tirole played an essential role in showing a whole generation of economists, including myself, that these subjects were not only legitimate and important for our discipline, but that we also had, like psychologists, important assets to help us better understand them.

Notable Works

Your research ften relies on models and experiments. What challenges do you encounter in designing these experiments?

There are logistical and practical challenges that shouldn’t be underestimated, for example, ensuring that instructions are clear enough for participants while being concise. But the main challenge concerns the articulation between theory and experiment. I think that any good experiment teaches us something important about an economic model, whether that model has been explicitly formulated or is implicitly present in researchers’ minds. The main challenge of an experiment is knowing which aspect of a model we want to test, and ensuring that the protocol fulfills this objective. This requires anticipating all alternative interpretations of the results, and perfecting the experimental protocol until as many of these alternatives as possible have been eliminated.

Another issue I consider very important is the external validity of experiments. We must always question to what extent results obtained in an experimental setting can be generalized outside the laboratory. This is an essential but uncomfortable question because nothing allows us to answer it, except our efforts to ensure that the experiment isn’t too artificial and remains close to real-life situations we are interested in. This is one of the reasons why a large part of my methodological work consists of developing protocols that detect belief and learning biases on natural objects, like the outcome of an upcoming election, rather than using artificial tools as the literature tends to do (for example, urns filled with balls). The article Learning about one’s self, co-authored with Peter Schwardmann (Carnegie Mellon University) when we were colleagues at the University of Munich, is an example of such methodological work.

The PROSPECT Project

ERC_logo

Where did the idea for PROSPECT come from, and what current gaps in the literature do you hope to fill with this project?

The idea comes from the article I just mentioned. Most work in economics and psychology on belief biases deals with retrospective learning, that is, our ability to use information that has already realized. We know, for example, that individuals tend to ignore some valuable pieces of information, and to overweight others. While writing our article, Peter and I realized that the experimental methodology we had developed allowed us to study something new in this literature: prospective learning, that is, the ability to predict the value of information that has not yet been revealed.

This cognitive process might seem abstract, but we engage in it constantly. When we decide to read a book to learn something, we do so because we think the book contains important information – without knowing the exact nature of that information in advance, of course, otherwise we wouldn’t need to read the book! The same applies when we research house prices before buying one, read press articles about bitcoin to know if it’s still time to buy, or when we simply ask a question to our office neighbor. This process is therefore essential because it determines the quantity and type of information we voluntarily acquire. However, it has never been empirically evaluated. The objective of PROSPECT (Biases in prospective learning and dynamic choice) is to fill this gap, and to measure whether we are capable of making these types of predictions correctly, or if we are affected by systematic cognitive biases such as a tendency to underestimate the value of information we don’t yet have.

Which biases in prospective learning and dynamic decision-making do you find particularly intriguing or under-explored?

I have the intuition that we don’t experiment enough, in both our professional and personal lives. By “experimentation,” I mean any approach that consists of forcing ourselves to try different things hoping to find something “better” than the status quo.

Many anecdotes, particularly in arts and sciences, suggest that constrained experimentation can be beneficial. My favorite concerns jazz pianist Keith Jarrett, who, in 1975 in Cologne, played before thousands of people on a defective piano. This constraint forced him to use a restricted range to avoid the damaged parts of the instrument, and to show more creativity, particularly rhythmically, to compensate. This concert and the subsequent record were hugely successful. This raises the question of why such a talented pianist, so versed in the art of improvisation, had to wait until a strong constraint was imposed on him to release this additional creativity.

My hypothesis is that we generally tend to underestimate the benefits of experimentation, and therefore we engage in it insufficiently when we’re not constrained to do so. If this is the case, and if PROSPECT demonstrates it, we will then need to think about the means at our disposal to encourage more innovation efforts, both by individuals and within organizations.

Impact

How could your work on belief biases or self-learning be used to improve decisions in finance, politics, or other domains?

I’ll respond at a general level regarding belief biases and self-learning. First, I think the main beneficiaries of this research are individuals themselves: when we are informed about the main belief biases, we’re better equipped to detect them, in others but also in ourselves, and try to correct them to make better decisions. An important application in economic literature concerns the beliefs we form about our own future behaviors, in areas as varied as personal finance (consumption and saving behavior) or health (addictions, exercising habits…). These beliefs are often too optimistic, and simply knowing this encourages more introspection and measure when making decisions that commit us long-term (for example, taking on too much debt).

In the political domain, it is in my view a very complicated question. We know that societies are increasingly fracturing under the effect of social networks and “fake news,” and that it has become difficult to get different partisan groups to admit common factual truths. This is a major fact of contemporary Western democracies. The diagnosis is clear, but the remedies much less so. In my opinion, the most promising work in this area studies light, non-coercive interventions that respect the fundamental principle of free flow of information, aimed at encouraging introspection and a certain skepticism towards information among social media users. In the longer term, education programs aimed at promoting critical thinking and resistance to manipulation seem essential. In both cases, a good understanding of the underlying cognitive biases is useful for thinking about the most effective interventions and those least likely to create undesirable side effects.

Guidance

What advantages do you see in collaborating with neighboring disciplines like psychology or behavioral sciences?

We often deal with similar topics, but with different methods. It seems important for a behavioral economist to have a certain general knowledge of the disciplines you mention, to avoid rediscovering things already known. Reading work in other disciplines can also give innovative research ideas. While collaborations between economists and psychologists remain relatively rare, there is a long tradition of dialogue between the two disciplines, as shown by the major influence of Daniel Kahneman and Amos Tversky’s work (two psychologists) on economic research. The book by Kahneman, Thinking, Fast, and Slow, summarizes their joint work.

Is there an error or challenge you encountered in your career that has profoundly marked your approach to research today?

I long made the mistake of underestimating how long research projects take. I still make this mistake despite my own work on over-optimism (it’s a surprisingly robust bias!), but to a lesser extent. Seriously embarking on a research project is, in the vast majority of cases, committing to spending several years of work on it. For this reason, I think it is essential, when choosing a project, to give a lot of importance to what psychologists call “intrinsic motivation”: the meaning the project has in our eyes, the importance we attach to its results, the pleasure we take in working on it daily. Abundant evidence in psychology (some of it summarized in a classic book from 1985 by Edward L. Deci and Richard Ryan, Intrinsic Motivation and Self-Determination in Human Behavior) shows that this is a much healthier and more sustainable driver than its alternative, “extrinsic motivation,” which lies in the hope of external rewards.

Yves Le Yaouanq’s work exemplifies how behavioral economics can address some of today’s most pressing challenges, from understanding cognitive biases to improving decision-making in finance, politics, and beyond. His recent ERC Consolidator Grant for the PROSPECT project highlights the innovative nature of his research, further strengthening CREST’s reputation for excellence. Yves now joins the six other ERC projects currently held at CREST, contributing to a vibrant community of researchers tackling critical questions across economics, sociology, finance, and statistics. His journey is paving the way for new approaches to understanding human behavior and its societal implications.

Yves Le Yaouanq: Recipient of an ERC Consolidator Grant in Economics


In this latest edition of CRESTive Minds, we are proud to feature Yves Le Yaouanq, researcher at CREST-Ecole polytechnique, a recent recipient of the prestigious ERC Consolidator Grant for his PROSPECT project. Specializing in behavioral economics, Yves explores the cognitive biases that shape human decision-making, with a focus on prospective learning and dynamic choice.

In this interview, he shares insights into his academic journey, the inspirations behind his research, and the challenges of designing experiments that bridge theory and real-world application.

Join us as we delve into the innovative work that earned Yves this remarkable recognition.

Academic Journey

Question: Could you tell us about your academic journey and what led you to specialize in behavioral economics?

I began a doctorate at the Toulouse School of Economics focusing on environmental economics topics related to climate policies. This literature often approaches the problem from a normative angle (what is the best way to make the ecological transition?), but I was quickly drawn to a more descriptive question: why do we do so little? This was in the early 2010s, at a time when climate denial was still quite present in our societies. This questioning led me to discover the work of psychologists, as well as economists, on the subject of belief biases and their social implications. I found these subjects fascinating because they mobilize a very broad spectrum of methods: psychological intuition (even introspection), modeling, then experimentation and quantification. I then continued my work in this literature throughout my career, at the University of Munich after my thesis, and at CREST since 2021, while keeping an eye on topics in political economy.

Which experiences or encounters particularly influenced your research choices?

I was particularly influenced by my encounter with Roland Bénabou (Princeton University) and Jean Tirole (Toulouse School of Economics), who both supervised me during my thesis, and by reading their work. They were among the first to show how economic tools, like game theory, could help us better think about objects that we rarely associate with economists’ work, such as emotions: anxiety, desire for recognition, regret… Roland Bénabou and Jean Tirole played an essential role in showing a whole generation of economists, including myself, that these subjects were not only legitimate and important for our discipline, but that we also had, like psychologists, important assets to help us better understand them.

Notable Works

Your research ften relies on models and experiments. What challenges do you encounter in designing these experiments?

There are logistical and practical challenges that shouldn’t be underestimated, for example, ensuring that instructions are clear enough for participants while being concise. But the main challenge concerns the articulation between theory and experiment. I think that any good experiment teaches us something important about an economic model, whether that model has been explicitly formulated or is implicitly present in researchers’ minds. The main challenge of an experiment is knowing which aspect of a model we want to test, and ensuring that the protocol fulfills this objective. This requires anticipating all alternative interpretations of the results, and perfecting the experimental protocol until as many of these alternatives as possible have been eliminated.

Another issue I consider very important is the external validity of experiments. We must always question to what extent results obtained in an experimental setting can be generalized outside the laboratory. This is an essential but uncomfortable question because nothing allows us to answer it, except our efforts to ensure that the experiment isn’t too artificial and remains close to real-life situations we are interested in. This is one of the reasons why a large part of my methodological work consists of developing protocols that detect belief and learning biases on natural objects, like the outcome of an upcoming election, rather than using artificial tools as the literature tends to do (for example, urns filled with balls). The article Learning about one’s self, co-authored with Peter Schwardmann (Carnegie Mellon University) when we were colleagues at the University of Munich, is an example of such methodological work.

The PROSPECT Project

ERC_logo

Where did the idea for PROSPECT come from, and what current gaps in the literature do you hope to fill with this project?

The idea comes from the article I just mentioned. Most work in economics and psychology on belief biases deals with retrospective learning, that is, our ability to use information that has already realized. We know, for example, that individuals tend to ignore some valuable pieces of information, and to overweight others. While writing our article, Peter and I realized that the experimental methodology we had developed allowed us to study something new in this literature: prospective learning, that is, the ability to predict the value of information that has not yet been revealed.

This cognitive process might seem abstract, but we engage in it constantly. When we decide to read a book to learn something, we do so because we think the book contains important information – without knowing the exact nature of that information in advance, of course, otherwise we wouldn’t need to read the book! The same applies when we research house prices before buying one, read press articles about bitcoin to know if it’s still time to buy, or when we simply ask a question to our office neighbor. This process is therefore essential because it determines the quantity and type of information we voluntarily acquire. However, it has never been empirically evaluated. The objective of PROSPECT (Biases in prospective learning and dynamic choice) is to fill this gap, and to measure whether we are capable of making these types of predictions correctly, or if we are affected by systematic cognitive biases such as a tendency to underestimate the value of information we don’t yet have.

Which biases in prospective learning and dynamic decision-making do you find particularly intriguing or under-explored?

I have the intuition that we don’t experiment enough, in both our professional and personal lives. By “experimentation,” I mean any approach that consists of forcing ourselves to try different things hoping to find something “better” than the status quo.

Many anecdotes, particularly in arts and sciences, suggest that constrained experimentation can be beneficial. My favorite concerns jazz pianist Keith Jarrett, who, in 1975 in Cologne, played before thousands of people on a defective piano. This constraint forced him to use a restricted range to avoid the damaged parts of the instrument, and to show more creativity, particularly rhythmically, to compensate. This concert and the subsequent record were hugely successful. This raises the question of why such a talented pianist, so versed in the art of improvisation, had to wait until a strong constraint was imposed on him to release this additional creativity.

My hypothesis is that we generally tend to underestimate the benefits of experimentation, and therefore we engage in it insufficiently when we’re not constrained to do so. If this is the case, and if PROSPECT demonstrates it, we will then need to think about the means at our disposal to encourage more innovation efforts, both by individuals and within organizations.

Impact

How could your work on belief biases or self-learning be used to improve decisions in finance, politics, or other domains?

I’ll respond at a general level regarding belief biases and self-learning. First, I think the main beneficiaries of this research are individuals themselves: when we are informed about the main belief biases, we’re better equipped to detect them, in others but also in ourselves, and try to correct them to make better decisions. An important application in economic literature concerns the beliefs we form about our own future behaviors, in areas as varied as personal finance (consumption and saving behavior) or health (addictions, exercising habits…). These beliefs are often too optimistic, and simply knowing this encourages more introspection and measure when making decisions that commit us long-term (for example, taking on too much debt).

In the political domain, it is in my view a very complicated question. We know that societies are increasingly fracturing under the effect of social networks and “fake news,” and that it has become difficult to get different partisan groups to admit common factual truths. This is a major fact of contemporary Western democracies. The diagnosis is clear, but the remedies much less so. In my opinion, the most promising work in this area studies light, non-coercive interventions that respect the fundamental principle of free flow of information, aimed at encouraging introspection and a certain skepticism towards information among social media users. In the longer term, education programs aimed at promoting critical thinking and resistance to manipulation seem essential. In both cases, a good understanding of the underlying cognitive biases is useful for thinking about the most effective interventions and those least likely to create undesirable side effects.

Guidance

What advantages do you see in collaborating with neighboring disciplines like psychology or behavioral sciences?

We often deal with similar topics, but with different methods. It seems important for a behavioral economist to have a certain general knowledge of the disciplines you mention, to avoid rediscovering things already known. Reading work in other disciplines can also give innovative research ideas. While collaborations between economists and psychologists remain relatively rare, there is a long tradition of dialogue between the two disciplines, as shown by the major influence of Daniel Kahneman and Amos Tversky’s work (two psychologists) on economic research. The book by Kahneman, Thinking, Fast, and Slow, summarizes their joint work.

Is there an error or challenge you encountered in your career that has profoundly marked your approach to research today?

I long made the mistake of underestimating how long research projects take. I still make this mistake despite my own work on over-optimism (it’s a surprisingly robust bias!), but to a lesser extent. Seriously embarking on a research project is, in the vast majority of cases, committing to spending several years of work on it. For this reason, I think it is essential, when choosing a project, to give a lot of importance to what psychologists call “intrinsic motivation”: the meaning the project has in our eyes, the importance we attach to its results, the pleasure we take in working on it daily. Abundant evidence in psychology (some of it summarized in a classic book from 1985 by Edward L. Deci and Richard Ryan, Intrinsic Motivation and Self-Determination in Human Behavior) shows that this is a much healthier and more sustainable driver than its alternative, “extrinsic motivation,” which lies in the hope of external rewards.

Yves Le Yaouanq’s work exemplifies how behavioral economics can address some of today’s most pressing challenges, from understanding cognitive biases to improving decision-making in finance, politics, and beyond. His recent ERC Consolidator Grant for the PROSPECT project highlights the innovative nature of his research, further strengthening CREST’s reputation for excellence. Yves now joins the six other ERC projects currently held at CREST, contributing to a vibrant community of researchers tackling critical questions across economics, sociology, finance, and statistics. His journey is paving the way for new approaches to understanding human behavior and its societal implications.

19 CREST Papers selected for the 2024 NeurIPS Conference on Neural Information Processing Systems


In December 2024, CREST researchers and PhD students, will present their last papers during the 2024 NeurIPS conference that will be held in Vancouver, Canada.

About the NeurIPS Conference

Founded in 1987, the conference has evolved into a prominent annual interdisciplinary event, featuring multiple tracks that include invited talks, demonstrations, symposia, and oral and poster presentations of peer-reviewed papers.

In addition to the main program, the event hosts a professional exhibition highlighting real-world applications of machine learning, as well as tutorials and topical workshops designed to foster the exchange of ideas in a more informal setting.

NeurIPS, alongside ICML, ranks among the top three most prestigious international conferences in Artificial Intelligence.

CREST’s papers to be presented

The conference’s focus resonates with CREST’s contributions to AI, particularly in applying statistical and mathematical frameworks to emerging challenges. CREST researchers have developed innovative methods in areas like optimal transport, reinforcement learning, and auction theory.

19 papers from CREST researchers and PhD students will be presented during the conference.

Title Authors
Progressive Entropic Optimal Transport Solvers P. Kassraie, AA. Pooladian, M. Klein, J. Thornton, J. Niles-Weed, M. Cuturi
Learning Elastic Costs to Shape Monge Displacements M. Klein, AA. Pooladian, P. Ablin, E. Ndiaye, J. Niles-Weed, M. Cuturi
GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics D. Klein, T. Uscidda, F. Theis, M. Cuturi
Statistical and Geometrical properties of regularized Kernel Kullback-Leibler divergence C. Chazal, A. Korba, F. Bach
Constrained Sampling with Primal-Dual Langevin Monte Carlo L. F. O. Chamon, M. R. Karimi, A. Korba
Mirror and Preconditioned Gradient Descent in Wasserstein Space C. Bonet, T. Uscidda, A. David, P-C. Aubin-Frankowski, A. Korba
Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learning, Otmane Sakhi, Imad Aouali, Pierre Alquier, Nicolas Chopin
Wasserstein convergence of Čech persistence diagrams for samplings of submanifolds C. Arnal, D. Cohen-Steiner, V. Divol
The Value of Reward Lookahead in Reinforcement Learning Nadav Merlis, Dorian Baudry, Vianney Perchet
DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation Felipe Garrido, Benjamin Heymann, Maxime Vono, Patrick Loiseau, Vianney Perchet
Lookback Prophet Inequalities Ziyad Benomar, Dorian Baudry, Vianney Perchet
Addressing Bias in Online Selection with Limited Budget of Comparisons Ziyad Benomar, Evgenii Chzhen, Nicolas Schreuder, Vianney Perchet
Local and Adaptive Mirror Descents in Extensive-Form Games Côme Fiegel, Pierre Menard, Tadashi Kozuno, Remi Munos, Vianney Perchet, Michal Valko
Strategic Multi-Armed Bandit Problems Under Debt-Free Reporting Ahmed Ben Yahmed, Clément Calauzènes, Vianney Perchet
Improved learning rates in multi-unit uniform price auctions Marius Potfer, Dorian Baudry, Hugo Richard, Vianney Perchet, Cheng Wan
Optimizing the coalition gain in Online Auctions with Greedy Structured Bandits Dorian Baudry, Hugo Richard, Maria Cherifa, Vianney Perchet, Clément Calauzènes
Improved Algorithms for Contextual Dynamic Pricing Matilde Tullii, Solenne Gaucher, Nadav Merlis, Vianney Perchet
Learning-Augmented Priority Queues Ziyad Benomar · Christian Coester
Reinforcement Learning with Lookahead Information Nadav Merlis

 

If you want to check papers to be presented during NeurIPS 2024, please visit: https://nips.cc/virtual/2024/papers.html?filter=titles

Source: https://neurips.cc/About

19 CREST Papers selected for the 2024 NeurIPS Conference on Neural Information Processing Systems


In December 2024, CREST researchers and PhD students, will present their last papers during the 2024 NeurIPS conference that will be held in Vancouver, Canada.

About the NeurIPS Conference

Founded in 1987, the conference has evolved into a prominent annual interdisciplinary event, featuring multiple tracks that include invited talks, demonstrations, symposia, and oral and poster presentations of peer-reviewed papers.

In addition to the main program, the event hosts a professional exhibition highlighting real-world applications of machine learning, as well as tutorials and topical workshops designed to foster the exchange of ideas in a more informal setting.

NeurIPS, alongside ICML, ranks among the top three most prestigious international conferences in Artificial Intelligence.

CREST’s papers to be presented

The conference’s focus resonates with CREST’s contributions to AI, particularly in applying statistical and mathematical frameworks to emerging challenges. CREST researchers have developed innovative methods in areas like optimal transport, reinforcement learning, and auction theory.

19 papers from CREST researchers and PhD students will be presented during the conference.

Title Authors
Progressive Entropic Optimal Transport Solvers P. Kassraie, AA. Pooladian, M. Klein, J. Thornton, J. Niles-Weed, M. Cuturi
Learning Elastic Costs to Shape Monge Displacements M. Klein, AA. Pooladian, P. Ablin, E. Ndiaye, J. Niles-Weed, M. Cuturi
GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics D. Klein, T. Uscidda, F. Theis, M. Cuturi
Statistical and Geometrical properties of regularized Kernel Kullback-Leibler divergence C. Chazal, A. Korba, F. Bach
Constrained Sampling with Primal-Dual Langevin Monte Carlo L. F. O. Chamon, M. R. Karimi, A. Korba
Mirror and Preconditioned Gradient Descent in Wasserstein Space C. Bonet, T. Uscidda, A. David, P-C. Aubin-Frankowski, A. Korba
Logarithmic Smoothing for Pessimistic Off-Policy Evaluation, Selection and Learning, Otmane Sakhi, Imad Aouali, Pierre Alquier, Nicolas Chopin
Wasserstein convergence of Čech persistence diagrams for samplings of submanifolds C. Arnal, D. Cohen-Steiner, V. Divol
The Value of Reward Lookahead in Reinforcement Learning Nadav Merlis, Dorian Baudry, Vianney Perchet
DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation Felipe Garrido, Benjamin Heymann, Maxime Vono, Patrick Loiseau, Vianney Perchet
Lookback Prophet Inequalities Ziyad Benomar, Dorian Baudry, Vianney Perchet
Addressing Bias in Online Selection with Limited Budget of Comparisons Ziyad Benomar, Evgenii Chzhen, Nicolas Schreuder, Vianney Perchet
Local and Adaptive Mirror Descents in Extensive-Form Games Côme Fiegel, Pierre Menard, Tadashi Kozuno, Remi Munos, Vianney Perchet, Michal Valko
Strategic Multi-Armed Bandit Problems Under Debt-Free Reporting Ahmed Ben Yahmed, Clément Calauzènes, Vianney Perchet
Improved learning rates in multi-unit uniform price auctions Marius Potfer, Dorian Baudry, Hugo Richard, Vianney Perchet, Cheng Wan
Optimizing the coalition gain in Online Auctions with Greedy Structured Bandits Dorian Baudry, Hugo Richard, Maria Cherifa, Vianney Perchet, Clément Calauzènes
Improved Algorithms for Contextual Dynamic Pricing Matilde Tullii, Solenne Gaucher, Nadav Merlis, Vianney Perchet
Learning-Augmented Priority Queues Ziyad Benomar · Christian Coester
Reinforcement Learning with Lookahead Information Nadav Merlis

 

If you want to check papers to be presented during NeurIPS 2024, please visit: https://nips.cc/virtual/2024/papers.html?filter=titles

Source: https://neurips.cc/About