Publications

A Diffusion-Model of Joint Interactive Navigation

Matthew Niedoba

Jonathan Wilder Lavington

Yunpeng Liu

Vasileios Lioutas

Justice Sefas

Xiaoxuan Liang

Dylan Green

Setareh Dabiri

Berend Zwartsenberg

Adam Ścibior

Frank N. Wood

Simulation of autonomous vehicle systems requires that simulated traffic participants exhibit diverse and realistic behaviors. The use of pr… (voir plus)erecorded real-world traffic scenarios in simulation ensures realism but the rarity of safety critical events makes large scale collection of driving scenarios expensive. In this paper, we present DJINN - a diffusion based method of generating traffic scenarios. Our approach jointly diffuses the trajectories of all agents, conditioned on a flexible set of state observations from the past, present, or future. On popular trajectory forecasting datasets, we report state of the art performance on joint trajectory metrics. In addition, we demonstrate how DJINN flexibly enables direct test-time sampling from a variety of valuable conditional distributions including goal-based sampling, behavior-class sampling, and scenario editing.

2023-09-20

NeurIPS.cc/2023/Conference (poster)

doi.org

openreview.net

On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes

Jia Lin Hau

Erick Delage

Mohammad Ghavamzadeh

Marek Petrik

2023-09-20

NeurIPS.cc/2023/Conference (poster)

openreview.net

HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution

Eric Nguyen

Michael Poli

Marjan Faizi

Armin W Thomas

Callum Birch-Sykes

Michael Wornow

Aman Patel

Clayton M. Rabideau

Stefano Massaroli

Yoshua Bengio

Stefano Ermon

Stephen Baccus

Christopher Re

2023-09-20

NeurIPS.cc/2023/Conference (spotlight)

doi.org

openreview.net

Importance-aware Co-teaching for Offline Model-based Optimization

Ye Yuan

Can Chen

Zixuan Liu

Willie Neiswanger

Xue Liu

2023-09-20

NeurIPS.cc/2023/Conference (poster)

openreview.net

Improving Language Plasticity via Pretraining with Active Forgetting

Yihong Chen

Kelly Marchisio

Roberta Raileanu

David Ifeoluwa Adelani

Pontus Stenetorp

Sebastian Riedel

Mikel Artetxe

Pretrained language models (PLMs) are today the primary model for natural language processing. Despite their impressive downstream performan… (voir plus)ce, it can be difficult to apply PLMs to new languages, a barrier to making their capabilities universally accessible. While prior work has shown it possible to address this issue by learning a new embedding layer for the new language, doing so is both data and compute inefficient. We propose to use an active forgetting mechanism during pretraining, as a simple way of creating PLMs that can quickly adapt to new languages. Concretely, by resetting the embedding layer every K updates during pretraining, we encourage the PLM to improve its ability of learning new embeddings within limited number of updates, similar to a meta-learning effect. Experiments with RoBERTa show that models pretrained with our forgetting mechanism not only demonstrate faster convergence during language adaptation, but also outperform standard ones in a low-data regime, particularly for languages that are distant from English. Code will be available at https://github.com/facebookresearch/language-model-plasticity.

2023-09-20

NeurIPS.cc/2023/Conference (poster)

doi.org

openreview.net

Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions

Stefano Massaroli

Michael Poli

Daniel Y Fu

Hermann Kumbong

Rom Nishijima Parnichkun

Aman Timalsina

David W. Romero

Quinn McIntyre

Beidi Chen

Atri Rudra

Ce Zhang

Christopher Re

Stefano Ermon

Yoshua Bengio

Recent advances in attention-free sequence models rely on convolutions as alternatives to the attention operator at the core of Transformers… (voir plus). In particular, long convolution sequence models have achieved state-of-the-art performance in many domains, but incur a significant cost during auto-regressive inference workloads -- naively requiring a full pass (or caching of activations) over the input sequence for each generated token -- similarly to attention-based models. In this paper, we seek to enable

2023-09-20

NeurIPS.cc/2023/Conference (poster)

doi.org

openreview.net

Learning Reliable Logical Rules with SATNet

Zhaoyu Li

Jinpei Guo

Yuhe Jiang

Xujie Si

2023-09-20

NeurIPS.cc/2023/Conference (poster)

doi.org

openreview.net

Multi-Head Adapter Routing for Cross-Task Generalization

Lucas Caccia

Edoardo Ponti

Zhan Su

Matheus Pereira

Nicolas Le Roux

Alessandro Sordoni

Parameter-efficient fine-tuning (PEFT) for cross-task generalization consists in pre-training adapters on a multi-task training set before f… (voir plus)ew-shot adaptation to test tasks. Polytropon [Ponti et al., 2023] (

2023-09-20

NeurIPS.cc/2023/Conference (poster)

doi.org

openreview.net

Neural Graph Generation from Graph Statistics.

Kiarash Zahirnia

Yaochen Hu

Mark J. Coates

Oliver Schulte

2023-09-20

NeurIPS.cc/2023/Conference (poster)

openreview.net

Optimal Extragradient-Based Algorithms for Stochastic Variational Inequalities with Separable Structure

Angela Yuan

Chris Junchi Li

Gauthier Gidel

Michael Jordan

Quanquan Gu

Simon Shaolei Du

We consider the problem of solving stochastic monotone variational inequalities with a separable structure using a stochastic first-order or… (voir plus)acle. Building on standard extragradient for variational inequalities we propose a novel algorithm---stochastic \emph{accelerated gradient-extragradient} (AG-EG)---for strongly monotone variational inequalities (VIs). Our approach combines the strengths of extragradient and Nesterov acceleration. By showing that its iterates remain in a bounded domain and applying scheduled restarting, we prove that AG-EG has an optimal convergence rate for strongly monotone VIs. Furthermore, when specializing to the particular case of bilinearly coupled strongly-convex-strongly-concave saddle-point problems, including bilinear games, our algorithm achieves fine-grained convergence rates that match the respective lower bounds, with the stochasticity being characterized by an additive statistical error term that is optimal up to a constant prefactor.

2023-09-20

NeurIPS.cc/2023/Conference (poster)

openreview.net

Retrieval-Augmented Multiple Instance Learning

Yufei Cui

Ziquan Liu

Yixin CHEN

Yuchen Lu

Xinyue Yu

Xue Liu

Tei-Wei Kuo

Miguel R. D. Rodrigues

Chun Jason Xue

Antoni B. Chan

Multiple Instance Learning (MIL) is a crucial weakly supervised learning method applied across various domains, e.g., medical diagnosis base… (voir plus)d on whole slide images (WSIs). Recent advancements in MIL algorithms have yielded exceptional performance when the training and test data originate from the same domain, such as WSIs obtained from the same hospital. However, this paper reveals a performance deterioration of MIL models when tested on an out-of-domain test set, exemplified by WSIs sourced from a novel hospital. To address this challenge, this paper introduces the Retrieval-AugMented MIL (RAM-MIL) framework, which integrates Optimal Transport (OT) as the distance metric for nearest neighbor retrieval. The development of RAM-MIL is driven by two key insights. First, a theoretical discovery indicates that reducing the input's intrinsic dimension can minimize the approximation error in attention-based MIL. Second, previous studies highlight a link between input intrinsic dimension and the feature merging process with the retrieved data. Empirical evaluations conducted on WSI classification demonstrate that the proposed RAM-MIL framework achieves state-of-the-art performance in both in-domain scenarios, where the training and retrieval data are in the same domain, and more crucially, in out-of-domain scenarios, where the (unlabeled) retrieval data originates from a different domain. Furthermore, the use of the transportation matrix derived from OT renders the retrieval results interpretable at the instance level, in contrast to the vanilla

2023-09-20

NeurIPS.cc/2023/Conference (poster)

openreview.net