Publications

Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics
Shoaib Ahmed Siddiqui
Nitarshan Rajkumar
Tegan Maharaj
David M. Krueger
Sara Hooker
Modern machine learning research relies on relatively few carefully curated datasets. Even in these datasets, and typically in `untidy' or r… (voir plus)aw data, practitioners are faced with significant issues of data quality and diversity which can be prohibitively labor intensive to address. Existing methods for dealing with these challenges tend to make strong assumptions about the particular issues at play, and often require a priori knowledge or metadata such as domain labels. Our work is orthogonal to these methods: we instead focus on providing a unified and efficient framework for Metadata Archaeology -- uncovering and inferring metadata of examples in a dataset. We curate different subsets of data that might exist in a dataset (e.g. mislabeled, atypical, or out-of-distribution examples) using simple transformations, and leverage differences in learning dynamics between these probe suites to infer metadata of interest. Our method is on par with far more sophisticated mitigation methods across different tasks: identifying and correcting mislabeled examples, classifying minority-group samples, prioritizing points relevant for training and enabling scalable human auditing of relevant examples.
2023-01-31
ICLR.cc/2023/Conference (notable)
doi.org
openreview.net
Revisiting Populations in multi-agent Communication
Paul Michel
Mathieu Rita
Kory Mathewson
Olivier Tieleman
Angeliki Lazaridou
Despite evidence from cognitive sciences that larger groups of speakers tend to develop more structured languages in human communication, sc… (voir plus)aling up to populations has failed to yield significant benefits in emergent multi-agent communication. In this paper we advocate for an alternate population-level training paradigm for referential games based on the idea of "partitioning" the agents into sender-receiver pairs and limiting co-adaptation across pairs. We show that this results in optimizing a different objective at the population level, where agents maximize (1) their respective "internal" communication accuracy and (2) some measure of alignment between agents. In experiments, we find that this leads to the emergence of languages that are significantly more compositional. Moreover, when agents are trained in populations that are not fully connected (ie. not all agent pairs interact at training time), this approach reduces multi-linguality and improves zero-shot communication with new agents (ie. agents are able to communicate successfully with other agents outside their training partners).
2023-01-31
ICLR.cc/2023/Conference (poster)
openreview.net
openreview.net
Social isolation is linked to classical risk factors of Alzheimer's disease-related dementias
Kimia Shafighi
Sylvia Villeneuve
Pedro Rosa Neto
AmanPreet Badhwar
Judes Poirier
Vaibhav Sharma
Yasser Iturria Medina
Patricia P. Silveira
Laurette Dubé
David Glahn
Danilo Bzdok
Alzheimer’s disease and related dementias is a major public health burden–compounding over upcoming years due to longevity. Recently, cl… (voir plus)inical evidence hinted at the experience of social isolation in expediting dementia onset. In 502,506 UK Biobank participants and 30,097 participants from the Canadian Longitudinal Study of Aging, we revisited traditional risk factors for developing dementia in the context of loneliness and lacking social support. Across these measures of subjective and objective social deprivation, we have identified strong links between individuals’ social capital and various indicators of Alzheimer’s disease and related dementias risk, which replicated across both population cohorts. The quality and quantity of daily social encounters had deep connections with key aetiopathological factors, which represent 1) personal habits and lifestyle factors, 2) physical health, 3) mental health, and 4) societal and external factors. Our population-scale assessment suggest that social lifestyle determinants are linked to most neurodegeneration risk factors, highlighting them as promising targets for preventive clinical action.
2023-01-31
PLOS ONE (publié)
doi.org
Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions
David Bieber
Rishab Goel
Dan Zheng
Hugo Larochelle
Daniel Tarlow
The execution behavior of a program often depends on external resources, such as program inputs or file contents, and so cannot be run in is… (voir plus)olation. Nevertheless, software developers benefit from fast iteration loops where automated tools identify errors as early as possible, even before programs can be compiled and run. This presents an interesting machine learning challenge: can we predict runtime errors in a"static"setting, where program execution is not possible? Here, we introduce a real-world dataset and task for predicting runtime errors, which we show is difficult for generic models like Transformers. We approach this task by developing an interpreter-inspired architecture with an inductive bias towards mimicking program executions, which models exception handling and"learns to execute"descriptions of the contents of external resources. Surprisingly, we show that the model can also predict the location of the error, despite being trained only on labels indicating the presence/absence and kind of error. In total, we present a practical and difficult-yet-approachable challenge problem related to learning program execution and we demonstrate promising new capabilities of interpreter-inspired machine learning models for code.
2023-01-31
ICLR.cc/2023/Conference (poster)
doi.org
openreview.net
The clinical value of Aspergillus-specific IgG antibody test in the diagnosis of nonneutropenic invasive pulmonary aspergillosis
Yajie Lu
Lulu Liu
Hongxing Li
Bilin Chen
Yu Gu
Li Wang
Chunlai Feng
Cheng Chen
Yanbin Chen
Wenkui Sun
Xuefan Cui
Min Cao
Yujian Tao
Jinjin Zhong
Huanhuan Zhong
Yueyan Ni
Yuchen Cai
Mengyue Song
Xiaoguang Liu
Yi Shi … (voir 1 de plus)
Xin Su
2023-01-31
Clinical Microbiology and Infection (publié)
doi.org
The Hidden Uniform Cluster Prior in Self-Supervised Learning
Mahmoud Assran
Randall Balestriero
Quentin Duval
Florian Bordes
Ishan Misra
Piotr Bojanowski
P Vincent
Michael G. Rabbat
Nicolas Ballas
A successful paradigm in representation learning is to perform self-supervised pretraining using tasks based on mini-batch statistics (e.g.,… (voir plus) SimCLR, VICReg, SwAV, MSN). We show that in the formulation of all these methods is an overlooked prior to learn features that enable uniform clustering of the data. While this prior has led to remarkably semantic representations when pretraining on class-balanced data, such as ImageNet, we demonstrate that it can hamper performance when pretraining on class-imbalanced data. By moving away from conventional uniformity priors and instead preferring power-law distributed feature clusters, we show that one can improve the quality of the learned representations on real-world class-imbalanced datasets. To demonstrate this, we develop an extension of the Masked Siamese Networks (MSN) method to support the use of arbitrary features priors.
2023-01-31
ICLR.cc/2023/Conference (poster)
doi.org
openreview.net
A theoretical and computational equilibria analysis of a multi-player kidney exchange program
Margarida Carvalho
Andrea Lodi
2023-01-31
European Journal of Operational Research (publié)
doi.org
arxiv.org
Understanding Zero-shot Adversarial Robustness for Large-Scale Models
Chengzhi Mao
Scott Geng
Junfeng Yang
Xin Wang
Carl Vondrick
Pretrained large-scale vision-language models like CLIP have exhibited strong generalization over unseen tasks. Yet imperceptible adversaria… (voir plus)l perturbations can significantly reduce CLIP's performance on new tasks. In this work, we identify and explore the problem of adapting large-scale models for zero-shot adversarial robustness. We first identify two key factors during model adaption--training losses and adaptation methods--that affect the model's zero-shot adversarial robustness. We then propose a text-guided contrastive adversarial training loss, which aligns the text embeddings and the adversarial visual features with contrastive learning on a small set of training data. We apply this training loss to two adaption methods, model finetuning and visual prompt tuning. We find that visual prompt tuning is more effective in the absence of texts, while finetuning wins in the existence of text guidance. Overall, our approach significantly improves the zero-shot adversarial robustness over CLIP, seeing an average improvement of 31 points over ImageNet and 15 zero-shot datasets. We hope this work can shed light on understanding the zero-shot adversarial robustness of large-scale models.
2023-01-31
ICLR.cc/2023/Conference (poster)
doi.org
openreview.net
Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask?
Mansheej Paul
Feng Chen
Brett W. Larsen
Jonathan Frankle
Surya Ganguli
Gintare Karolina Dziugaite
Modern deep learning involves training costly, highly overparameterized networks, thus motivating the search for sparser networks that can s… (voir plus)till be trained to the same accuracy as the full network (i.e. matching). Iterative magnitude pruning (IMP) is a state of the art algorithm that can find such highly sparse matching subnetworks, known as winning tickets. IMP operates by iterative cycles of training, masking smallest magnitude weights, rewinding back to an early training point, and repeating. Despite its simplicity, the underlying principles for when and how IMP finds winning tickets remain elusive. In particular, what useful information does an IMP mask found at the end of training convey to a rewound network near the beginning of training? How does SGD allow the network to extract this information? And why is iterative pruning needed? We develop answers in terms of the geometry of the error landscape. First, we find that
2023-01-31
ICLR.cc/2023/Conference (notable)
doi.org
openreview.net
Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top
Eduard Gorbunov
Samuel Horváth
Peter Richtárik
Gauthier Gidel
Byzantine-robustness has been gaining a lot of attention due to the growth of the interest in collaborative and federated learning. However,… (voir plus) many fruitful directions, such as the usage of variance reduction for achieving robustness and communication compression for reducing communication costs, remain weakly explored in the field. This work addresses this gap and proposes Byz-VR-MARINA - a new Byzantine-tolerant method with variance reduction and compression. A key message of our paper is that variance reduction is key to fighting Byzantine workers more effectively. At the same time, communication compression is a bonus that makes the process more communication efficient. We derive theoretical convergence guarantees for Byz-VR-MARINA outperforming previous state-of-the-art for general non-convex and Polyak-Lojasiewicz loss functions. Unlike the concurrent Byzantine-robust methods with variance reduction and/or compression, our complexity results are tight and do not rely on restrictive assumptions such as boundedness of the gradients or limited compression. Moreover, we provide the first analysis of a Byzantine-tolerant method supporting non-uniform sampling of stochastic gradients. Numerical experiments corroborate our theoretical findings.
2023-01-31
ICLR.cc/2023/Conference (poster)
doi.org
openreview.net
Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning
John Nguyen
Jianyu Wang
Kshitiz Malik
Maziar Sanjabi
Michael G. Rabbat
AI Meta
2023-01-31
ICLR.cc/2023/Conference (notable)
doi.org
openreview.net
BARVINN: Arbitrary Precision DNN Accelerator Controlled by a RISC-V CPU
Mohammadhossein Askarihemmat
Sean Wagner
Olexa Bilaniuk
Yassine Hariri
Yvon Savaria
Jean-Pierre David
We present a DNN accelerator that allows inference at arbitrary precision with dedicated processing elements that are configurable at the bi… (voir plus)t level. Our DNN accelerator has 8 Processing Elements controlled by a RISC-V controller with a combined 8.2 TMACs of computational power when implemented with the recent Alveo U250 FPGA platform. We develop a code generator tool that ingests CNN models in ONNX format and generates an executable command stream for the RISC-V controller. We demonstrate the scalable throughput of our accelerator by running different DNN kernels and models when different quantization levels are selected. Compared to other low precision accelerators, our accelerator provides run time programmability without hardware reconfiguration and can accelerate DNNs with multiple quantization levels, regardless of the target FPGA size. BARVINN is an open source project and it is available at https://github.com/hossein1387/BARVINN.
2023-01-30
Proceedings of the 28th Asia and South Pacific Design Automation Conference (publié)
doi.org
arxiv.org