Publications

Improving and Generalizing Flow-Based Generative Models with Minibatch Optimal Transport
Alexander Tong
Nikolay Malkin
Guillaume Huguet
Yanlei Zhang
Jarrid Rector-Brooks
Kilian FATRAS
Guy Wolf
Yoshua Bengio
Continuous normalizing flows (CNFs) are an attractive generative modeling technique, but they have been held back by limitations in their si… (see more)mulation-based maximum likelihood training. We introduce the generalized \textit{conditional flow matching} (CFM) technique, a family of simulation-free training objectives for CNFs. CFM features a stable regression objective like that used to train the stochastic flow in diffusion models but enjoys the efficient inference of deterministic flow models. In contrast to both diffusion models and prior CNF training algorithms, CFM does not require the source distribution to be Gaussian or require evaluation of its density. A variant of our objective is optimal transport CFM (OT-CFM), which creates simpler flows that are more stable to train and lead to faster inference, as evaluated in our experiments. Furthermore, OT-CFM is the first method to compute dynamic OT in a simulation-free way. Training CNFs with CFM improves results on a variety of conditional and unconditional generation tasks, such as inferring single cell dynamics, unsupervised image translation, and Schrödinger bridge inference.
2024-03-11
TMLR (accepted)
openreview.net
openreview.net
IntentGPT: Few-shot Intent Discovery with Large Language Models
Juan A. Rodriguez
Nicholas Botzer
David Vazquez
Chris Pal
Marco Pedersoli
Issam Hadj Laradji
2024-03-11
ICLR.cc/2024/Workshop/LLMAgents (poster)
doi.org
openreview.net
IntentGPT: Few-shot Intent Discovery with Large Language Models
Juan A. Rodriguez
Nicholas Botzer
David Vazquez
Chris Pal
Marco Pedersoli
Issam Hadj Laradji
2024-03-11
ICLR.cc/2024/Workshop/LLMAgents (poster)
openreview.net
openreview.net
Investigating Robot Influence on Human Behaviour By Leveraging Entrainment Effects
Lixiao Zhu
AJung Moon
2024-03-11
Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (published)
doi.org
Language-guided Skill Learning with Temporal Variational Inference
Haotian Fu
Pratyusha Sharma
Elias Stengel-Eskin
George Konidaris
Nicolas Le Roux
Marc-Alexandre Côté
Xingdi Yuan
We present an algorithm for skill discovery from expert demonstrations. The algorithm first utilizes Large Language Models (LLMs) to propose… (see more) an initial segmentation of the trajectories. Following that, a hierarchical variational inference framework incorporates the LLM-generated segmentation information to discover reusable skills by merging trajectory segments. To further control the trade-off between compression and reusability, we introduce a novel auxiliary objective based on the Minimum Description Length principle that helps guide this skill discovery process. We test our system on BabyAI, a grid world navigation environment, as well as ALFRED, a household simulation environment.Our results demonstrate that agents equipped with our method can discover skills that help accelerate learning and outperform baseline skill learning approaches on new long-horizon tasks.
2024-03-11
ICLR.cc/2024/Workshop/LLMAgents (poster)
openreview.net
openreview.net
Long-term survival and functional outcomes of critically ill patients with hematologic malignancies: a Canadian multicenter prospective study
Laveena Munshi
Guillaume Dumas
Bram Rochwerg
Farah Shoukat
Michael Detsky
Dean A. Fergusson
Bruno Ferreyro
Paul Heffernan
Margaret Herridge
Sheldon Magder
Mark Minden
Rakesh Patel
Salman Qureshi
Aaron Schimmer
Santhosh Thyagu
Han Ting Wang
Sangeeta Mehta
2024-03-11
Intensive Care Medicine (published)
doi.org
Perspectives on Robotic Systems for the Visually Impaired
Christopher Yee Wong
Rahatul Amin Ananto
Tanaka Akiyama
Joseph Paul Nemargut
AJung Moon
2024-03-11
Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (published)
doi.org
Prioritizing Safeguarding Over Autonomy: Risks of LLM Agents for Science
Xiangru Tang
Qiao Jin
Kunlun Zhu
Tongxin Yuan
Yichi Zhang
Wangchunshu Zhou
Meng Qu
Yilun Zhao
Jian Tang
Zhuosheng Zhang
Arman Cohan
Zhiyong Lu
Mark Gerstein
2024-03-11
ICLR.cc/2024/Workshop/LLMAgents (poster)
openreview.net
openreview.net
Stealing Part of a Production Language Model
Nicholas Carlini
Daniel Paleka
Krishnamurthy Dj Dvijotham
Thomas Steinke
Jonathan Hayase
A. Feder Cooper
Katherine Lee
Matthew Jagielski
Milad Nasr
Arthur Conmy
Eric Wallace
David Rolnick
Florian Tramèr
We introduce the first model-stealing attack that extracts precise, nontrivial information from black-box production language models like Op… (see more)enAI's ChatGPT or Google's PaLM-2. Specifically, our attack recovers the embedding projection layer (up to symmetries) of a transformer model, given typical API access. For under \
2024-03-11
ArXiv (preprint)
doi.org
arxiv.org
Stealing Part of a Production Language Model
Nicholas Carlini
Daniel Paleka
Krishnamurthy Dj Dvijotham
Thomas Steinke
Jonathan Hayase
A. Feder Cooper
Katherine Lee
Matthew Jagielski
Milad Nasr
Arthur Conmy
Eric Wallace
David Rolnick
Florian Tramèr
We introduce the first model-stealing attack that extracts precise, nontrivial information from black-box production language models like Op… (see more)enAI's ChatGPT or Google's PaLM-2. Specifically, our attack recovers the embedding projection layer (up to symmetries) of a transformer model, given typical API access. For under \
2024-03-11
ArXiv (preprint)
doi.org
arxiv.org
Stealing Part of a Production Language Model
Nicholas Carlini
Daniel Paleka
Krishnamurthy Dvijotham
Thomas Steinke
Jonathan Hayase
A. Feder Cooper
Katherine Lee
Matthew Jagielski
Milad Nasr
Arthur Conmy
Eric Wallace
David Rolnick
Florian Tramèr
2024-03-11
ArXiv (preprint)
doi.org
arxiv.org
Stochastic gradient descent-based inference for dynamic network models with attractors
Hancong Pan
Xiaojing Zhu
Cantay Caliskan
Dino P. Christenson
Konstantinos Spiliopoulos
Dylan Walker
Eric Kolaczyk
In Coevolving Latent Space Networks with Attractors (CLSNA) models, nodes in a latent space represent social actors, and edges indicate thei… (see more)r dynamic interactions. Attractors are added at the latent level to capture the notion of attractive and repulsive forces between nodes, borrowing from dynamical systems theory. However, CLSNA reliance on MCMC estimation makes scaling difficult, and the requirement for nodes to be present throughout the study period limit practical applications. We address these issues by (i) introducing a Stochastic gradient descent (SGD) parameter estimation method, (ii) developing a novel approach for uncertainty quantification using SGD, and (iii) extending the model to allow nodes to join and leave over time. Simulation results show that our extensions result in little loss of accuracy compared to MCMC, but can scale to much larger networks. We apply our approach to the longitudinal social networks of members of US Congress on the social media platform X. Accounting for node dynamics overcomes selection bias in the network and uncovers uniquely and increasingly repulsive forces within the Republican Party.
2024-03-11
ArXiv (preprint)
doi.org
arxiv.org