Publications

Foliar spectra accurately distinguish most temperate tree species and show strong phylogenetic signal
Florence Blanchard
Anne Bruneau
Étienne Laliberté
2024-04-19
American-Eurasian journal of botany (publié)
doi.org
BACS: Background Aware Continual Semantic Segmentation
Mostafa Elaraby
Ali Harakeh
Liam Paull
Semantic segmentation plays a crucial role in enabling comprehensive scene understanding for robotic systems. However, generating annotation… (voir plus)s is challenging, requiring labels for every pixel in an image. In scenarios like autonomous driving, there's a need to progressively incorporate new classes as the operating environment of the deployed agent becomes more complex. For enhanced annotation efficiency, ideally, only pixels belonging to new classes would be annotated. This approach is known as Continual Semantic Segmentation (CSS). Besides the common problem of classical catastrophic forgetting in the continual learning setting, CSS suffers from the inherent ambiguity of the background, a phenomenon we refer to as the"background shift'', since pixels labeled as background could correspond to future classes (forward background shift) or previous classes (backward background shift). As a result, continual learning approaches tend to fail. This paper proposes a Backward Background Shift Detector (BACS) to detect previously observed classes based on their distance in the latent space from the foreground centroids of previous steps. Moreover, we propose a modified version of the cross-entropy loss function, incorporating the BACS detector to down-weight background pixels associated with formerly observed classes. To combat catastrophic forgetting, we employ masked feature distillation alongside dark experience replay. Additionally, our approach includes a transformer decoder capable of adjusting to new classes without necessitating an additional classification head. We validate BACS's superior performance over existing state-of-the-art methods on standard CSS benchmarks.
2024-04-18
ArXiv (prépublication)
doi.org
arxiv.org
BLIS-Net: Classifying and Analyzing Signals on Graphs
Charles Xu
Laney Goldman
Valentina Guo
Benjamin Hollander-Bodie
Maedee Trank-Greene
Ian Adelstein
Edward De Brouwer
Rex Ying
Smita Krishnaswamy
Michael Perlmutter
Graph neural networks (GNNs) have emerged as a powerful tool for tasks such as node classification and graph classification. However, much l… (voir plus)ess work has been done on signal classification, where the data consists of many functions (referred to as signals) defined on the vertices of a single graph. These tasks require networks designed differently from those designed for traditional GNN tasks. Indeed, traditional GNNs rely on localized low-pass filters, and signals of interest may have intricate multi-frequency behavior and exhibit long range interactions. This motivates us to introduce the BLIS-Net (Bi-Lipschitz Scattering Net), a novel GNN that builds on the previously introduced geometric scattering transform. Our network is able to capture both local and global signal structure and is able to capture both low-frequency and high-frequency information. We make several crucial changes to the original geometric scattering architecture which we prove increase the ability of our network to capture information about the input signal and show that BLIS-Net achieves superior performance on both synthetic and real-world data sets based on traffic flow and fMRI data.
2024-04-17
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (publié)
doi.org
proceedings.mlr.press
Categorical Generative Model Evaluation via Synthetic Distribution Coarsening
Florence Regol
Mark J. Coates
As we expect to see a rapid integration of generative models in our day to day lives, the development of rigorous methods of evaluation and … (voir plus)analysis for generative models has never been more pressing. Multiple works have highlighted the shortcomings of widely used metrics and exposed how they fail to behave as expected in some settings. So far, the response has been to use a variety of metrics that target different desirable and interpretable properties such as fidelity, diversity, and authenticity, to obtain a clearer picture of a generative model’s capabilities. These methods mainly focus on ordinal data and they all suffer from the same unavoidable issues stemming from estimating quantities of high-dimensional data from a limited number of samples. We propose to take an alternative approach and to return to the synthetic data setting where the ground truth is explicit and known. We focus on nominal categorical data and introduce an evaluation method that can scale to the high-dimensional settings often encountered in practice. Our method involves successively binning the large space to obtain smaller probability spaces and coarser distributions where meaningful statistical estimates can be obtained. This allows us to provide probabilistic guarantees and sample complexities and we illustrate how our method can be applied to distinguish between the capabilities of several state-of-the-art categorical models.
2024-04-17
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (publié)
proceedings.mlr.press
proceedings.mlr.press
Conditions on Preference Relations that Guarantee the Existence of Optimal Policies
Jonathan Colaço Carr
Prakash Panangaden
Doina Precup
Learning from Preferential Feedback (LfPF) plays an essential role in training Large Language Models, as well as certain types of interactiv… (voir plus)e learning agents. However, a substantial gap exists between the theory and application of LfPF algorithms. Current results guaranteeing the existence of optimal policies in LfPF problems assume that both the preferences and transition dynamics are determined by a Markov Decision Process. We introduce the Direct Preference Process, a new framework for analyzing LfPF problems in partially-observable, non-Markovian environments. Within this framework, we establish conditions that guarantee the existence of optimal policies by considering the ordinal structure of the preferences. We show that a decision-making problem can have optimal policies -- that are characterized by recursive optimality equations -- even when no reward function can express the learning goal. These findings underline the need to explore preference-based learning strategies which do not assume that preferences are generated by reward.
2024-04-17
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (publié)
doi.org
proceedings.mlr.press
Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias
Yu Yang
Eric Gan
Gintare Karolina Dziugaite
Baharan Mirzasoleiman
Neural networks trained with (stochastic) gradient descent have an inductive bias towards learning simpler solutions. This makes them highly… (voir plus) prone to learning spurious correlations in the training data, that may not hold at test time. In this work, we provide the first theoretical analysis of the effect of simplicity bias on learning spurious correlations. Notably, we show that examples with spurious features are provably separable based on the model's output early in training. We further illustrate that if spurious features have a small enough noise-to-signal ratio, the network's output on the majority of examples is almost exclusively determined by the spurious features, leading to poor worst-group test accuracy. Finally, we propose SPARE, which identifies spurious correlations early in training and utilizes importance sampling to alleviate their effect. Empirically, we demonstrate that SPARE outperforms state-of-the-art methods by up to 21.1% in worst-group accuracy, while being up to 12x faster. We also show that SPARE is a highly effective but lightweight method to discover spurious correlations.
2024-04-17
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (publié)
doi.org
proceedings.mlr.press
Introducing v0.5 of the AI Safety Benchmark from MLCommons
Bertie Vidgen
Adarsh Agrawal
Ahmed M. Ahmed
Victor Akinwande
Namir Al-Nuaimi
Najla Alfaraj
Elie Alhajjar
Lora Aroyo
Trupti Bavalatti
Borhane Blili-Hamelin
K. Bollacker
Rishi Bomassani
Marisa Ferrara Boston
Sim'eon Campos
Kal Chakra
Canyu Chen
Cody Coleman
Zacharie Delpierre Coudert
Leon Strømberg Derczynski
Debojyoti Dutta … (voir 77 de plus)
Ian Eisenberg
James R. Ezick
Heather Frase
Brian Fuller
Ram Gandikota
Agasthya Gangavarapu
Ananya Gangavarapu
James Gealy
Rajat Ghosh
James Goel
Usman Gohar
Sujata Goswami
Scott A. Hale
Wiebke Hutiri
Joseph Marvin Imperial
Surgan Jandial
Nicholas C. Judd
Felix Juefei-Xu
Foutse Khomh
Bhavya Kailkhura
Hannah Rose Kirk
Kevin Klyman
Chris Knotz
Michael Kuchnik
Shachi H. Kumar
Chris Lengerich
Bin Li
Zeyi Liao
Eileen Peters Long
Victor Lu
Yifan Mai
Priyanka Mary Mammen
Kelvin Manyeki
Sean McGregor
Virendra Mehta
Shafee Mohammed
Emanuel Moss
Lama Nachman
Dinesh Jinenhally Naganna
Amin Nikanjam
Besmira Nushi
Luis Oala
Iftach Orr
Alicia Parrish
Çigdem Patlak
William Pietri
Forough Poursabzi-Sangdeh
Eleonora Presani
Fabrizio Puletti
Paul Rottger
Saurav Sahay
Tim Santos
Nino Scherrer
Alice Schoenauer Sebag
Patrick Schramowski
Abolfazl Shahbazi
Vin Sharma
Xudong Shen
Vamsi Sistla
Leonard Tang
Davide Testuggine
Vithursan Thangarasa
Elizabeth A Watkins
Rebecca Weiss
Christoper A. Welty
Tyler Wilbers
Adina Williams
Carole-Jean Wu
Poonam Yadav
Xianjun Yang
Yi Zeng
Wenhui Zhang
Fedor Zhdanov
Jiacheng Zhu
Percy Liang
Peter Mattson
Joaquin Vanschoren
2024-04-17
ArXiv (prépublication)
doi.org
arxiv.org
On learning history-based policies for controlling Markov decision processes
Gandharv Patil
Aditya Mahajan
Doina Precup
Reinforcementlearning(RL)folkloresuggeststhathistory-basedfunctionapproximationmethods,suchas recurrent neural nets or history-based state a… (voir plus)bstraction, perform better than their memory-less counterparts, due to the fact that function approximation in Markov decision processes (MDP) can be viewed as inducing a Partially observable MDP. However, there has been little formal analysis of such history-based algorithms, as most existing frameworks focus exclusively on memory-less features. In this paper, we introduce a theoretical framework for studying the behaviour of RL algorithms that learn to control an MDP using history-based feature abstraction mappings. Furthermore, we use this framework to design a practical RL algorithm and we numerically evaluate its effectiveness on a set of continuous control tasks.
2024-04-17
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (publié)
doi.org
proceedings.mlr.press
Length Independent PAC-Bayes Bounds for Simple RNNs
Volodimir Mitarchuk
Clara Lacroce
Rémi Eyraud
Rémi Emonet
Amaury Habrard
Guillaume Rabusseau
2024-04-17
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (publié)
proceedings.mlr.press
proceedings.mlr.press
Multi-phase black-hole feedback and a bright [CII] halo in a Lo-BAL quasar at $z\sim6.6$
Manuela Bischetti
Hyunseop 현섭 Choi 최
Fabrizio Fiore
Chiara Feruglio
Stefano Carniani
Valentina D'Odorico
Eduardo Banados
Huanqing Chen
Roberto Decarli
Simona Gallerani
J. Hlavacek-Larrondo
Samuel Lai
Karen M. Leighly
Chiara Mazzucchelli
Laurence Perreault-Levasseur
Roberta Tripodi
Fabian Walter
Feige Wang
Jinyi Yang
Maria Vittoria Zanchettin … (voir 1 de plus)
Yongda Zhu
2024-04-17
ArXiv (prépublication)
doi.org
arxiv.org
Multi-resolution Time-Series Transformer for Long-term Forecasting
Yitian Zhang
Liheng Ma
Soumyasundar Pal
Yingxue Zhang
Mark J. Coates
2024-04-17
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (publié)
doi.org
proceedings.mlr.press
Simulating Weighted Automata over Sequences and Trees with Transformers
Michael Rizvi
Maude Lizaire
Clara Lacroce
Guillaume Rabusseau
Transformers are ubiquitous models in the natural language processing (NLP) community and have shown impressive empirical successes in the p… (voir plus)ast few years. However, little is understood about how they reason and the limits of their computational capabilities. These models do not process data sequentially, and yet outperform sequential neural models such as RNNs. Recent work has shown that these models can compactly simulate the sequential reasoning abilities of deterministic finite automata (DFAs). This leads to the following question: can transformers simulate the reasoning of more complex finite state machines? In this work, we show that transformers can simulate weighted finite automata (WFAs), a class of models which subsumes DFAs, as well as weighted tree automata (WTA), a generalization of weighted automata to tree structured inputs. We prove these claims formally and provide upper bounds on the sizes of the transformer models needed as a function of the number of states the target automata. Empirically, we perform synthetic experiments showing that transformers are able to learn these compact solutions via standard gradient-based training.
2024-04-17
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (publié)
doi.org
proceedings.mlr.press