Portrait de Martin Weiss

Martin Weiss

Postdoctorat - Polytechnique
Superviseur⋅e principal⋅e
Chris Pal
Co-supervisor
Hugo Larochelle
Sujets de recherche
Apprentissage profond
Raisonnement
Recherche d'information
Réseaux de neurones en graphes
Traitement du langage naturel
Site web
Google Scholar
GitHub

Publications

DeLLMphi: A Multi-Turn Method for Multi-Agent Forecasting
Andrew Robert Williams
Martin Weiss
Victoria Feere
Nasim Rahaman
Hugo Larochelle
The Delphi method is a structured forecasting process that engages experts in iterative prediction and reflection. Each round, experts submi… (voir plus)t forecasts to a mediator, receive an aggregated and synthesized response highlighting key arguments, and update their forecasts based on collective insight. However, Delphi panels are labour intensive, slow and hard to reproduce, requiring diverse knowledgeable participants to engage periodically across weeks or months. To address these constraints, we propose **DeLLMphi**, a forecasting method that replaces human experts and mediators with LLMs. We show (i) that providing example superforecaster reasoning traces and predictions helps to elicit more accurate forecasts from LLM experts, (ii) that the mediator plays the crucial role of surfacing different lines of reasoning and points of disagreement, and (iii) that multiple rounds and experts lead to better forecasts, showing that multi-turn interaction is key to DeLLMphi.
2025-11-03
NeurIPS.cc/2025/Workshop/MTI-LLM (poster)
openreview.net
openreview.net
Redesigning Information Markets in the Era of Language Models
Martin Weiss
Nasim Rahaman
Manuel Wüthrich
Yoshua Bengio
Li Erran Li
Bernhard Schölkopf
Chris Pal
2024-07-10
colmweb.org/COLM/2024/Conference (accepté)
openreview.net
openreview.net
Language Models Can Reduce Asymmetry in Information Markets
Nasim Rahaman
Martin Weiss
Manuel Wüthrich
Yoshua Bengio
Erran L. Li
Chris Pal
Bernhard Schölkopf
2024-03-21
ArXiv (prépublication)
doi.org
arxiv.org
Language Models Can Reduce Asymmetry in Information Markets
Nasim Rahaman
Martin Weiss
Manuel Wüthrich
Yoshua Bengio
Erran L. Li
Chris Pal
Bernhard Schölkopf
This work addresses the buyer's inspection paradox for information markets. The paradox is that buyers need to access information to determi… (voir plus)ne its value, while sellers need to limit access to prevent theft. To study this, we introduce an open-source simulated digital marketplace where intelligent agents, powered by language models, buy and sell information on behalf of external participants. The central mechanism enabling this marketplace is the agents' dual capabilities: they not only have the capacity to assess the quality of privileged information but also come equipped with the ability to forget. This ability to induce amnesia allows vendors to grant temporary access to proprietary information, significantly reducing the risk of unauthorized retention while enabling agents to accurately gauge the information's relevance to specific queries or tasks. To perform well, agents must make rational decisions, strategically explore the marketplace through generated sub-queries, and synthesize answers from purchased information. Concretely, our experiments (a) uncover biases in language models leading to irrational behavior and evaluate techniques to mitigate these biases, (b) investigate how price affects demand in the context of informational goods, and (c) show that inspection and higher budgets both lead to higher quality outcomes.
2024-03-21
ArXiv (prépublication)
doi.org
arxiv.org
Language Models Can Reduce Asymmetry in Information Markets
Nasim Rahaman
Martin Weiss
Manuel Wüthrich
Yoshua Bengio
Erran L. Li
Chris Pal
Bernhard Schölkopf
This work addresses the buyer's inspection paradox for information markets. The paradox is that buyers need to access information to determi… (voir plus)ne its value, while sellers need to limit access to prevent theft. To study this, we introduce an open-source simulated digital marketplace where intelligent agents, powered by language models, buy and sell information on behalf of external participants. The central mechanism enabling this marketplace is the agents' dual capabilities: they not only have the capacity to assess the quality of privileged information but also come equipped with the ability to forget. This ability to induce amnesia allows vendors to grant temporary access to proprietary information, significantly reducing the risk of unauthorized retention while enabling agents to accurately gauge the information's relevance to specific queries or tasks. To perform well, agents must make rational decisions, strategically explore the marketplace through generated sub-queries, and synthesize answers from purchased information. Concretely, our experiments (a) uncover biases in language models leading to irrational behavior and evaluate techniques to mitigate these biases, (b) investigate how price affects demand in the context of informational goods, and (c) show that inspection and higher budgets both lead to higher quality outcomes.
2024-03-21
ArXiv (prépublication)
doi.org
arxiv.org
Visual Question Answering From Another Perspective: CLEVR Mental Rotation Tests
Christopher Beckham
Martin Weiss
Florian Golemo
Sina Honari
Derek Nowrouzezahrai
Chris Pal
2023-04-01
Pattern Recognition (publié)
doi.org
openreview.net
Proactive Contact Tracing
Prateek Gupta
Tegan Maharaj
Martin Weiss
Nasim Rahaman
Hannah Alsdurf
Nanor Minoyan
Soren Harnois-Leblanc
Joanna Merckx
Andrew Robert Williams
Victor Schmidt
Pierre-Luc St-Charles
Akshay Patel
Yang Zhang
David Buckeridge
Chris Pal
Bernhard Schölkopf
Yoshua Bengio
2023-03-13
PLOS Digital Health (publié)
doi.org
Proactive Contact Tracing
Prateek Gupta
Tegan Maharaj
Martin Weiss
Nasim Rahaman
Hannah Alsdurf
Nanor Minoyan
Soren Harnois-Leblanc
Joanna Merckx
Andrew Williams
Victor Schmidt
Pierre-Luc St-Charles
Akshay Patel
Yang Zhang
David L Buckeridge
Chris Pal
Bernhard Schölkopf
Yoshua Bengio
The COVID-19 pandemic has spurred an unprecedented demand for interventions that can reduce disease spread without excessively restricting d… (voir plus)aily activity, given negative impacts on mental health and economic outcomes. Digital contact tracing (DCT) apps have emerged as a component of the epidemic management toolkit. Existing DCT apps typically recommend quarantine to all digitally-recorded contacts of test-confirmed cases. Over-reliance on testing may, however, impede the effectiveness of such apps, since by the time cases are confirmed through testing, onward transmissions are likely to have occurred. Furthermore, most cases are infectious over a short period; only a subset of their contacts are likely to become infected. These apps do not fully utilize data sources to base their predictions of transmission risk during an encounter, leading to recommendations of quarantine to many uninfected people and associated slowdowns in economic activity. This phenomenon, commonly termed as “pingdemic,” may additionally contribute to reduced compliance to public health measures. In this work, we propose a novel DCT framework, Proactive Contact Tracing (PCT), which uses multiple sources of information (e.g. self-reported symptoms, received messages from contacts) to estimate app users’ infectiousness histories and provide behavioral recommendations. PCT methods are by design proactive, predicting spread before it occurs. We present an interpretable instance of this framework, the Rule-based PCT algorithm, designed via a multi-disciplinary collaboration among epidemiologists, computer scientists, and behavior experts. Finally, we develop an agent-based model that allows us to compare different DCT methods and evaluate their performance in negotiating the trade-off between epidemic control and restricting population mobility. Performing extensive sensitivity analysis across user behavior, public health policy, and virological parameters, we compare Rule-based PCT to i) binary contact tracing (BCT), which exclusively relies on test results and recommends a fixed-duration quarantine, and ii) household quarantine (HQ). Our results suggest that both BCT and Rule-based PCT improve upon HQ, however, Rule-based PCT is more efficient at controlling spread of disease than BCT across a range of scenarios. In terms of cost-effectiveness, we show that Rule-based PCT pareto-dominates BCT, as demonstrated by a decrease in Disability Adjusted Life Years, as well as Temporary Productivity Loss. Overall, we find that Rule-based PCT outperforms existing approaches across a varying range of parameters. By leveraging anonymized infectiousness estimates received from digitally-recorded contacts, PCT is able to notify potentially infected users earlier than BCT methods and prevent onward transmissions. Our results suggest that PCT-based applications could be a useful tool in managing future epidemics.
2023-03-01
PLOS Digital Health (publié)
doi.org
Proactive Contact Tracing
Prateek Gupta
Tegan Maharaj
Martin Weiss
Nasim Rahaman
Hannah Alsdurf
Nanor Minoyan
Soren Harnois-Leblanc
Joanna Merckx
Andrew Williams
Victor Schmidt
Pierre-Luc St-Charles
Akshay Patel
Yang Zhang
David Buckeridge
Chris Pal
Bernhard Schölkopf
Yoshua Bengio
The COVID-19 pandemic has spurred an unprecedented demand for interventions that can reduce disease spread without excessively restricting d… (voir plus)aily activity, given negative impacts on mental health and economic outcomes. Digital contact tracing (DCT) apps have emerged as a component of the epidemic management toolkit. Existing DCT apps typically recommend quarantine to all digitally-recorded contacts of test-confirmed cases. Over-reliance on testing may, however, impede the effectiveness of such apps, since by the time cases are confirmed through testing, onward transmissions are likely to have occurred. Furthermore, most cases are infectious over a short period; only a subset of their contacts are likely to become infected. These apps do not fully utilize data sources to base their predictions of transmission risk during an encounter, leading to recommendations of quarantine to many uninfected people and associated slowdowns in economic activity. This phenomenon, commonly termed as “pingdemic,” may additionally contribute to reduced compliance to public health measures. In this work, we propose a novel DCT framework, Proactive Contact Tracing (PCT), which uses multiple sources of information (e.g. self-reported symptoms, received messages from contacts) to estimate app users’ infectiousness histories and provide behavioral recommendations. PCT methods are by design proactive, predicting spread before it occurs. We present an interpretable instance of this framework, the Rule-based PCT algorithm, designed via a multi-disciplinary collaboration among epidemiologists, computer scientists, and behavior experts. Finally, we develop an agent-based model that allows us to compare different DCT methods and evaluate their performance in negotiating the trade-off between epidemic control and restricting population mobility. Performing extensive sensitivity analysis across user behavior, public health policy, and virological parameters, we compare Rule-based PCT to i) binary contact tracing (BCT), which exclusively relies on test results and recommends a fixed-duration quarantine, and ii) household quarantine (HQ). Our results suggest that both BCT and Rule-based PCT improve upon HQ, however, Rule-based PCT is more efficient at controlling spread of disease than BCT across a range of scenarios. In terms of cost-effectiveness, we show that Rule-based PCT pareto-dominates BCT, as demonstrated by a decrease in Disability Adjusted Life Years, as well as Temporary Productivity Loss. Overall, we find that Rule-based PCT outperforms existing approaches across a varying range of parameters. By leveraging anonymized infectiousness estimates received from digitally-recorded contacts, PCT is able to notify potentially infected users earlier than BCT methods and prevent onward transmissions. Our results suggest that PCT-based applications could be a useful tool in managing future epidemics.
2023-03-01
PLOS Digital Health (publié)
doi.org
Proactive Contact Tracing
Prateek Gupta
Tegan Maharaj
Martin Weiss
Nasim Rahaman
Hannah Alsdurf
Nanor Minoyan
Soren Harnois-Leblanc
Joanna Merckx
Andrew Williams
Victor Schmidt
Pierre-Luc St-Charles
Akshay Patel
Yang Zhang
David L Buckeridge
Chris Pal
Bernhard Schölkopf
Yoshua Bengio
The COVID-19 pandemic has spurred an unprecedented demand for interventions that can reduce disease spread without excessively restricting d… (voir plus)aily activity, given negative impacts on mental health and economic outcomes. Digital contact tracing (DCT) apps have emerged as a component of the epidemic management toolkit. Existing DCT apps typically recommend quarantine to all digitally-recorded contacts of test-confirmed cases. Over-reliance on testing may, however, impede the effectiveness of such apps, since by the time cases are confirmed through testing, onward transmissions are likely to have occurred. Furthermore, most cases are infectious over a short period; only a subset of their contacts are likely to become infected. These apps do not fully utilize data sources to base their predictions of transmission risk during an encounter, leading to recommendations of quarantine to many uninfected people and associated slowdowns in economic activity. This phenomenon, commonly termed as “pingdemic,” may additionally contribute to reduced compliance to public health measures. In this work, we propose a novel DCT framework, Proactive Contact Tracing (PCT), which uses multiple sources of information (e.g. self-reported symptoms, received messages from contacts) to estimate app users’ infectiousness histories and provide behavioral recommendations. PCT methods are by design proactive, predicting spread before it occurs. We present an interpretable instance of this framework, the Rule-based PCT algorithm, designed via a multi-disciplinary collaboration among epidemiologists, computer scientists, and behavior experts. Finally, we develop an agent-based model that allows us to compare different DCT methods and evaluate their performance in negotiating the trade-off between epidemic control and restricting population mobility. Performing extensive sensitivity analysis across user behavior, public health policy, and virological parameters, we compare Rule-based PCT to i) binary contact tracing (BCT), which exclusively relies on test results and recommends a fixed-duration quarantine, and ii) household quarantine (HQ). Our results suggest that both BCT and Rule-based PCT improve upon HQ, however, Rule-based PCT is more efficient at controlling spread of disease than BCT across a range of scenarios. In terms of cost-effectiveness, we show that Rule-based PCT pareto-dominates BCT, as demonstrated by a decrease in Disability Adjusted Life Years, as well as Temporary Productivity Loss. Overall, we find that Rule-based PCT outperforms existing approaches across a varying range of parameters. By leveraging anonymized infectiousness estimates received from digitally-recorded contacts, PCT is able to notify potentially infected users earlier than BCT methods and prevent onward transmissions. Our results suggest that PCT-based applications could be a useful tool in managing future epidemics.
2023-03-01
PLOS Digital Health (publié)
doi.org
A General-Purpose Neural Architecture for Geospatial Systems
Nasim Rahaman
Martin Weiss
Frederik Träuble
Francesco Locatello
Alexandre Lacoste
Yoshua Bengio
Chris Pal
Li Erran Li
Bernhard Schölkopf
2022-11-02
OpenReview.net/Anonymous_Preprint (inconnu)
doi.org
openreview.net
Latent Variable Sequential Set Transformers for Joint Multi-Agent Motion Prediction
Roger Girgis
Florian Golemo
Felipe Codevilla
Martin Weiss
Jim Aldon D'Souza
Samira Ebrahimi Kahou
Felix Heide
Chris Pal
Robust multi-agent trajectory prediction is essential for the safe control of robotic systems. A major challenge is to efficiently learn a r… (voir plus)epresentation that approximates the true joint distribution of contextual, social, and temporal information to enable planning. We propose Latent Variable Sequential Set Transformers which are encoder-decoder architectures that generate scene-consistent multi-agent trajectories. We refer to these architectures as “AutoBots”. The encoder is a stack of interleaved temporal and social multi-head self-attention (MHSA) modules which alternately perform equivariant processing across the temporal and social dimensions. The decoder employs learnable seed parameters in combination with temporal and social MHSA modules allowing it to perform inference over the entire future scene in a single forward pass efficiently. AutoBots can produce either the trajectory of one ego-agent or a distribution over the future trajectories for all agents in the scene. For the single-agent prediction case, our model achieves top results on the global nuScenes vehicle motion prediction leaderboard, and produces strong results on the Argoverse vehicle prediction challenge. In the multi-agent setting, we evaluate on the synthetic partition of TrajNet++ dataset to showcase the model’s socially-consistent predictions. We also demonstrate our model on general sequences of sets and provide illustrative experiments modelling the sequential structure of the multiple strokes that make up symbols in the Omniglot data. A distinguishing feature of AutoBots is that all models are trainable on a single desktop GPU (1080 Ti) in under 48h.
2022-01-01
ICLR (publié)
openreview.net
openreview.net