Publications

Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems
Heiko Hoppe
Tobias Enders
Quentin Cappart
Maximilian Schiffer
2023-12-14
ArXiv (preprint)
doi.org
arxiv.org
A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captions
Jack Urbanek
Florian Bordes
Pietro Astolfi
Mary Williamson
Vasu Sharma
Adriana Romero Soriano
2023-12-14
ArXiv (preprint)
doi.org
arxiv.org
Asymmetric Actor-Critic with Approximate Information State
Amit Sinha
Aditya Mahajan
Reinforcement learning (RL) for partially observable Markov decision processes (POMDPs) is a challenging problem because decisions need to b… (see more)e made based on the entire history of observations and actions. However, in several scenarios, state information is available during the training phase. We are interested in exploiting the availability of this state information during the training phase to efficiently learn a history-based policy using RL. Specifically, we consider actor-critic algorithms, where the actor uses only the history information but the critic uses both history and state. Such algorithms are called asymmetric actor-critic, to highlight the fact that the actor and critic have asymmetric information. Motivated by the recent success of using representation losses in RL for POMDPs [1], we derive similar theoretical results for the asymmetric actor-critic case and evaluate the effectiveness of adding such auxiliary losses in experiments. In particular, we learn a history representation-called an approximate information state (AIS)-and bound the performance loss when acting using AIS.
2023-12-13
IEEE Conference on Decision and Control (published)
doi.org
Current Practices in Voice Data Collection and Limitations to Voice AI Research: A National Survey.
Emily Evangelista
Rohan Kale
Desiree McCutcheon
Anais Rameau
Alexander H. Gelbard
Maria Powell
Michael Johns
Anthony Law
Phillip C Song
M. Naunheim
Stephanie Watts
Paul C. Bryson
Matthew G. Crowson
Jeremy M. Pinto
Yael Bensoussan
INTRODUCTION Accuracy and validity of voice AI algorithms rely on substantial quality voice data. Although commensurable amounts of voice da… (see more)ta are captured daily in voice centers across North America, there is no standardized protocol for acoustic data management, which limits the usability of these datasets for voice artificial intelligence (AI) research. OBJECTIVE The aim was to capture current practices of voice data collection, storage, analysis, and perceived limitations to collaborative voice research. METHODS A 30-question online survey was developed with expert guidance from the voicecollab.ai members, an international collaborative of voice AI researchers. The survey was disseminated via REDCap to an estimated 200 practitioners at North American voice centers. Survey questions assessed respondents' current practices in terms of acoustic data collection, storage, and retrieval as well as limitations to collaborative voice research. RESULTS Seventy-two respondents completed the survey of which 81.7% were laryngologists and 18.3% were speech language pathologists (SLPs). Eighteen percent of respondents reported seeing 40%-60% and 55% reported seeing >60 patients with voice disorders weekly (conservative estimate of over 4000 patients/week). Only 28% of respondents reported utilizing standardized protocols for collection and storage of acoustic data. Although, 87% of respondents conduct voice research, only 38% of respondents report doing so on a multi-institutional level. Perceived limitations to conducting collaborative voice research include lack of standardized methodology for collection (30%) and lack of human resources to prepare and label voice data adequately (55%). CONCLUSION To conduct large-scale multi-institutional voice research with AI, there is a pertinent need for standardization of acoustic data management, as well as an infrastructure for secure and efficient data sharing. LEVEL OF EVIDENCE Level 5 Laryngoscope, 2023.
2023-12-13
The Laryngoscope (published)
doi.org
Current Practices in Voice Data Collection and Limitations to Voice AI Research: A National Survey.
Emily Evangelista
Rohan Kale
Desiree McCutcheon
Anais Rameau
Alexander Gelbard
Maria Powell
Michael Johns
Anthony Law
Phillip Song
Matthew Naunheim
Stephanie Watts
Paul C. Bryson
Matthew G. Crowson
Jeremy Pinto
Yael Bensoussan
INTRODUCTION Accuracy and validity of voice AI algorithms rely on substantial quality voice data. Although commensurable amounts of voice da… (see more)ta are captured daily in voice centers across North America, there is no standardized protocol for acoustic data management, which limits the usability of these datasets for voice artificial intelligence (AI) research. OBJECTIVE The aim was to capture current practices of voice data collection, storage, analysis, and perceived limitations to collaborative voice research. METHODS A 30-question online survey was developed with expert guidance from the voicecollab.ai members, an international collaborative of voice AI researchers. The survey was disseminated via REDCap to an estimated 200 practitioners at North American voice centers. Survey questions assessed respondents' current practices in terms of acoustic data collection, storage, and retrieval as well as limitations to collaborative voice research. RESULTS Seventy-two respondents completed the survey of which 81.7% were laryngologists and 18.3% were speech language pathologists (SLPs). Eighteen percent of respondents reported seeing 40%-60% and 55% reported seeing >60 patients with voice disorders weekly (conservative estimate of over 4000 patients/week). Only 28% of respondents reported utilizing standardized protocols for collection and storage of acoustic data. Although, 87% of respondents conduct voice research, only 38% of respondents report doing so on a multi-institutional level. Perceived limitations to conducting collaborative voice research include lack of standardized methodology for collection (30%) and lack of human resources to prepare and label voice data adequately (55%). CONCLUSION To conduct large-scale multi-institutional voice research with AI, there is a pertinent need for standardization of acoustic data management, as well as an infrastructure for secure and efficient data sharing. LEVEL OF EVIDENCE Level 5 Laryngoscope, 2023.
2023-12-13
The Laryngoscope (published)
doi.org
A deep learning benchmark for first break detection from hardrock seismic reflection data
Pierre-Luc St-Charles
Bruno Rousseau
Joumana Ghosn
Gilles Bellefleur
Ernst Schetselaar
2023-12-13
GEOPHYSICS (published)
doi.org
Privacy-preserving analysis of time-to-event data under nested case-control sampling
Lamin Juwara
Archer Yang
Ana M Velly
Paramita Saha-Chaudhuri
2023-12-13
Statistical Methods in Medical Research (published)
doi.org
Q-learners Can Provably Collude in the Iterated Prisoner's Dilemma
Quentin Bertrand
Juan Duque
Emilio Calvano
Gauthier Gidel
The deployment of machine learning systems in the market economy has triggered academic and institutional fears over potential tacit collusi… (see more)on between fully automated agents. Multiple recent economics studies have empirically shown the emergence of collusive strategies from agents guided by machine learning algorithms. In this work, we prove that multi-agent Q-learners playing the iterated prisoner's dilemma can learn to collude. The complexity of the cooperative multi-agent setting yields multiple fixed-point policies for
2023-12-13
ArXiv (preprint)
doi.org
arxiv.org
Relative Almost Sure Regret Bounds for Certainty Equivalence Control of Markov Jump Systems
Borna Sayedana
Mohammad Afshari
Peter E. Caines
Aditya Mahajan
In this paper, we consider learning and control problem in an unknown Markov jump linear system (MJLS) with perfect state observations. We f… (see more)irst establish a generic upper bound on regret for any learning based algorithm. We then propose a certainty equivalence-based learning alagrithm and show that this algorithm achieves a regret of
2023-12-13
2023 62nd IEEE Conference on Decision and Control (CDC) (published)
doi.org
scHiCyclePred: a deep learning framework for predicting cell cycle phases from single-cell Hi-C data using multi-scale interaction information
Yingfu Wu
Zhenqi Shi
Xiangfei Zhou
Pengyu Zhang
Xiuhui Yang
Jun Ding
Hao Wu
2023-12-13
bioRxiv (preprint)
doi.org
Weighted-Norm Bounds on Model Approximation in MDPs with Unbounded Per-Step Cost
Berk Bozkurt
Aditya Mahajan
Ashutosh Nayyar
Yi Ouyang
We consider the problem of designing a control policy for an infinite-horizon discounted cost Markov Decision Process (MDP) …
2023-12-13
IEEE Conference on Decision and Control (published)
doi.org
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems
Alexandre AGM Duval
Simon V. Mathis
Chaitanya K. Joshi
Victor Schmidt
Santiago Miret
Fragkiskos D. Malliaros
Taco Cohen
Pietro Lio’
Yoshua Bengio
Michael M. Bronstein
2023-12-12
ArXiv (preprint)
doi.org
arxiv.org