- Zhou, Tingtao; Wan, Xuan; et el. (2024) AI-aided
geometric design of anti-infection catheters; Science Advances; Vol.
10; No. 1; eadj1741; PMCID PMC10776022; 10.1126/sciadv.adj1741
- Zheng, Zhiling; Alawadhi, Ali H.; et el. (2023) Shaping
the Water-Harvesting Behavior of Metal–Organic Frameworks Aided by
Fine-Tuned GPT Models; Journal of the American Chemical Society; 10.1021/jacs.3c12086
- Liu, Shengchao; Nie, Weili; et el. (2023) Multi-modal
molecule structure–text model for text-based retrieval and editing;
Nature Machine Intelligence; Vol. 5; No. 12; 1447-1457; 10.1038/s42256-023-00759-6
- Kiyasseh, Dani; Ma, Runzhuo; et el. (2023) A
vision transformer for decoding surgeon activity from surgical
videos; Nature Biomedical Engineering; Vol. 7; No. 6; 780-796; PMCID
PMC10307635; 10.1038/s41551-023-01010-8
- Kiyasseh, Dani; Laca, Jasper; et el. (2023) Human
visual explanations mitigate bias in AI-based assessment of surgeon
skills; npj Digital Medicine; Vol. 6; Art. No. 54; PMCID
PMC10063676; 10.1038/s41746-023-00766-2
- Inouye, Daniel A.; Ma, Runzhuo; et el. (2023) Assessing
the efficacy of dissection gestures in robotic surgery; Journal of
Robotic Surgery; Vol. 17; No. 2; 597-603; 10.1007/s11701-022-01458-x
- Wen, Gege; Li, Zongyi; et el. (2023) Real-time
high-resolution CO₂ geological storage prediction using nested Fourier
neural operators; Energy and Environmental Science; Vol. 16; No. 4;
1732-1741; 10.1039/d2ee04204e
- Kiyasseh, Dani; Laca, Jasper; et el. (2023) A
multi-institutional study using artificial intelligence to provide
reliable and fair feedback to surgeons; Communications Medicine;
Vol. 3; Art. No. 42; PMCID PMC10063640; 10.1038/s43856-023-00263-3
- Hung, Andrew J.; Bao, Richard; et el. (2023) Capturing
fine-grained details for video-based automation of suturing skills
assessment; International Journal of Computer Assisted Radiology and
Surgery; Vol. 18; No. 3; 545-552; PMCID PMC9975072; 10.1007/s11548-022-02778-x
- Dommer, Abigail; Casalino, Lorenzo; et el. (2023) #COVIDisAirborne:
AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a
respiratory aerosol; International Journal of High Performance
Computing Applications; Vol. 37; No. 1; 28-44; PMCID PMC9527558; 10.1177/10943420221128233
- Ma, Runzhuo; Ramaswamy, Ashwin; et el. (2022) Surgical
gestures as a method to quantify surgical performance and predict
patient outcomes; npj Digital Medicine; Vol. 5; Art. No. 187; PMCID
PMC9780308; 10.1038/s41746-022-00738-y
- Zhao, Jiawei; Dai, Steve; et el. (2022) LNS-Madam:
Low-Precision Training in Logarithmic Number System using Multiplicative
Weight Update; IEEE Transactions on Computers; Vol. 71; No. 12;
3179-3190; 10.1109/tc.2022.3202747
- Laca, Jasper A.; Kocielnik, Rafal; et el. (2022) Using
Real-time Feedback To Improve Surgical Performance on a Robotic Tissue
Dissection Task; European Urology Open Science; Vol. 46; 15-21;
PMCID PMC9732447; 10.1016/j.euros.2022.09.015
- Trifan, Anda; Gorgun, Defne; et el. (2022) Intelligent
resolution: Integrating Cryo-EM with AI-driven multi-resolution
simulations to observe the severe acute respiratory syndrome
coronavirus-2 replication-transcription machinery in action;
International Journal of High Performance Computing Applications; 10.1177/10943420221113513
- Hoeller, David; Rudin, Nikita; et el. (2022) Neural
Scene Representation for Locomotion on Structured Terrain; IEEE
Robotics and Automation Letters; Vol. 7; No. 4; 8667-8674; 10.1109/LRA.2022.3184779
- Pangal, Dhiraj J.; Kugener, Guillaume; et el. (2022) Use
of surgical video–based automated performance metrics to predict blood
loss and success of simulated vascular injury control in neurosurgery: a
pilot study; Journal of Neurosurgery; Vol. 137; No. 3; 840-849; 10.3171/2021.10.jns211064
- Markarian, Nicholas; Kugener, Guillaume; et el. (2022) Validation
of Machine Learning-Based Automated Surgical Instrument Annotation Using
Publicly Available Intraoperative Video; Operative Neurosurgery;
Vol. 23; No. 3; 235-240; 10.1227/ons.0000000000000274
- Patti, Taylor L.; Kossaifi, Jean; et el. (2022) Variational
quantum optimization with multibasis encodings; Physical Review
Research; Vol. 4; No. 3; Art. No. 4.033142; 10.1103/physrevresearch.4.033142
- Qiao, Zhuoran; Christensen, Anders S.; et el. (2022) Informing
geometric deep learning with electronic interactions to accelerate
quantum chemistry; Proceedings of the National Academy of Sciences;
Vol. 119; No. 31; Art. No. e2205221119; PMCID PMC9351474; 10.1073/pnas.2205221119
- Xu, Pan; Zheng, Hongkai; et el. (2022) Langevin
Monte Carlo for Contextual Bandits; Proceedings of Machine Learning
Research; Vol. 162; 24830-24850; 10.48550/arXiv.arXiv.2206.11254
- Kargin, Taylan; Lale, Sahin; et el. (2022) Thompson
Sampling Achieves Õ(√T) Regret in Linear Quadratic Control;
Proceedings of Machine Learning Research; Vol. 178; 3235-3284; 10.48550/arXiv.2206.08520
- Kugener, Guillaume; Zhu, Yichao; et el. (2022) Deep
Neural Networks Can Accurately Detect Blood Loss and Hemorrhage Control
Task Success From Video; Neurosurgery; Vol. 90; No. 6; 823-829; 10.1227/neu.0000000000001906
- Pangal, Dhiraj J.; Kugener, Guillaume; et el. (2022) Expert
surgeons and deep learning models can predict the outcome of surgical
hemorrhage from 1 min of video; Scientific Reports; Vol. 12; Art.
No. 8137; PMCID PMC9114003; 10.1038/s41598-022-11549-2
- Nie, Weili; Guo, Brandon; et el. (2022) Diffusion
Models for Adversarial Purification; Proceedings of Machine Learning
Research; Vol. 162; 16805-16827; 10.48550/arXiv.2205.07460
- O’Connell, Michael; Shi, Guanya; et el. (2022) Neural-Fly
enables rapid learning for agile flight in strong winds; Science
Robotics; Vol. 7; No. 66; Art. No. eabm6597; 10.1126/scirobotics.abm6597
- Roberts, Sidney I.; Cen, Steven Y.; et el. (2022) The
Relationship Between Technical Skills, Cognitive Workload, and Errors
During Robotic Surgical Exercises; Journal of Endourology; Vol. 36;
No. 5; 712-720; PMCID PMC9145254; 10.1089/end.2021.0790
- Wen, Gege; Li, Zongyi; et el. (2022) U-FNO—An
enhanced Fourier neural operator-based deep-learning model for
multiphase flow; Advances in Water Resources; Vol. 163; Art.
No. 104180; 10.1016/j.advwatres.2022.104180
- Zhou, Daquan; Yu, Zhiding; et el. (2022) Understanding
The Robustness in Vision Transformers; Proceedings of Machine
Learning Research; Vol. 162; 27378-27394; 10.48550/arXiv.2204.12451
- Kugener, Guillaume; Pangal, Dhiraj J.; et el. (2022) Utility
of the Simulated Outcomes Following Carotid Artery Laceration Video Data
Set for Machine Learning Applications; JAMA Network Open; Vol. 5;
No. 3; Art. No. e223177; PMCID PMC8938712; 10.1001/jamanetworkopen.2022.3177
- Liu, Burigede; Kovachki, Nikola; et el. (2022) A
learning-based multiscale method and its application to inelastic impact
problems; Journal of the Mechanics and Physics of Solids; Vol. 158;
Art. No. 104668; 10.1016/j.jmps.2021.104668
- Christensen, Anders S.; Sirumalla, Sai Krishna; et el. (2021) OrbNet
Denali: A machine learning potential for biological and organic
chemistry with semi-empirical cost and DFT accuracy; Journal of
Chemical Physics; Vol. 155; No. 20; Art. No. 204103; 10.1063/5.0061990
- Lee, Youngwoon; Lim, Joseph J.; et el. (2021) Adversarial
Skill Chaining for Long-Horizon Robot Manipulation via Terminal State
Regularization; Proceedings of Machine Learning Research; Vol. 164;
406-416; 10.48550/arXiv.arXiv.2111.07999
- Hung, Andrew J.; Liu, Yan; et el. (2021) Deep
Learning to Automate Technical Skills Assessment in Robotic Surgery;
JAMA Surgery; Vol. 156; No. 11; 1059-1060; 10.1001/jamasurg.2021.3651
- Chan, Justin; Pangal, Dhiraj J.; et el. (2021) A
systematic review of virtual reality for the assessment of technical
skills in neurosurgery; Neurosurgical Focus; Vol. 51; No. 2; Art.
No. E15; 10.3171/2021.5.focus21210
- Liu, Bo; Liu, Qiang; et el. (2021) Coach-Player
Multi-agent Reinforcement Learning for Dynamic Team Composition;
Proceedings of Machine Learning Research; Vol. 139; 6860-6870; 10.48550/arXiv.2105.08692
- Chang, Nadine; Yu, Zhiding; et el. (2021) Image-Level
or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed
Detection; Proceedings of Machine Learning Research; Vol. 139;
1463-1472; 10.48550/arXiv.2104.05702
- Fan, Linxi; Wang, Guanzhi; et el. (2021) SECANT:
Self-Expert Cloning for Zero-Shot Generalization of Visual Policies;
Proceedings of Machine Learning Research; Vol. 139; 3088-3099; 10.48550/arXiv.2106.09678
- Mahajan, Anuj; Samvelyan, Mikayel; et el. (2021) Tesseract:
Tensorised Actors for Multi-Agent Reinforcement Learning;
Proceedings of Machine Learning Research; Vol. 139; 7301-7312; 10.48550/arXiv.2106.00136
- Lale, Sahin; Azizzadenesheli, Kamyar; et el. (2021) Finite-time
System Identification and Adaptive Control in Autoregressive Exogenous
Systems; Proceedings of Machine Learning Research; Vol. 144;
967-979
- Yu, Jing; Gehring, Clement; et el. (2021) Robust
Reinforcement Learning: A Constrained Game-theoretic Approach;
Proceedings of Machine Learning Research; Vol. 144; 1242-1254
- Lale, Sahin; Teke, Oguzhan; et el. (2021) Stability
and Identification of Random Asynchronous Linear Time-Invariant
Systems; Proceedings of Machine Learning Research; Vol. 144;
651-663; 10.48550/arXiv.2012.04160
- Qu, Guannan; Shi, Yuanyuan; et el. (2021) Stable
Online Control of Linear Time-Varying Systems; Proceedings of
Machine Learning Research; Vol. 144; 742-753; 10.48550/arXiv.2104.14134
- Luongo, Francisco; Hakim, Ryan; et el. (2021) Deep
learning-based computer vision to recognize and classify suturing
gestures in robot-assisted surgery; Surgery; Vol. 169; No. 5;
1240-1244; PMCID PMC7994208; 10.1016/j.surg.2020.08.016
- Panagakis, Yannis; Kossaifi, Jean; et el. (2021) Tensor
Methods in Computer Vision and Deep Learning; Proceedings of the
IEEE; Vol. 109; No. 5; 863-890; 10.1109/jproc.2021.3074329
- Kashinath, K.; Mustafa, M.; et el. (2021) Physics-informed
machine learning: case studies for weather and climate modelling;
Philosophical Transactions A: Mathematical, Physical and Engineering
Sciences; Vol. 379; No. 2194; Art. No. 20200093; 10.1098/rsta.2020.0093
- Zhao, Eric; Liu, Anqi; et el. (2021) Active
Learning under Label Shift; Proceedings of Machine Learning
Research; Vol. 130; 3412-3420; 10.48550/arXiv.2007.08479
- Nakka, Yashwanth Kumar; Liu, Anqi; et el. (2021) Chance-Constrained
Trajectory Optimization for Safe Exploration and Learning of Nonlinear
Systems; IEEE Robotics and Automation Letters; Vol. 6; No. 2;
389-396; 10.1109/LRA.2020.3044033
- Chu, Linda C.; Anandkumar, Animashree; et el. (2020) The
Potential Dangers of Artificial Intelligence for Radiology and
Radiologists; Journal of the American College of Radiology; Vol. 17;
No. 10; 1309-1311; PMCID PMC7164850; 10.1016/j.jacr.2020.04.010
- Qiao, Zhuoran; Welborn, Matthew; et el. (2020) OrbNet:
Deep learning for quantum chemistry using symmetry-adapted
atomic-orbital features; Journal of Chemical Physics; Vol. 153;
No. 12; Art. No. 124111; 10.1063/5.0021955
- Ren, Hongyu; Zhu, Yuke; et el. (2020) OCEAN:
Online Task Inference for Compositional Tasks with Context
Adaptation; Proceedings of Machine Learning Research; Vol. 124;
1378-1387; 10.48550/arXiv.2008.07087
- Kossaifi, Jean; Lipton, Zachary C.; et el. (2020) Tensor
Regression Networks; Journal of Machine Learning Research; Vol. 21;
1-21; 10.48550/arXiv.1707.08308
- Chen, Wuyang; Yu, Zhiding; et el. (2020) Automated
Synthetic-to-Real Generalization; Proceedings of Machine Learning
Research; Vol. 119; 1746-1756; 10.48550/arXiv.2007.06965
- Chen, Beidi; Liu, Weiyang; et el. (2020) Angular
Visual Hardness; Proceedings of Machine Learning Research; Vol. 119;
1637-1648; 10.48550/arXiv.1912.02279
- Ross, Zachary E.; Trugman, Daniel T.; et el. (2020) Directivity
Modes of Earthquake Populations with Unsupervised Learning; Journal
of Geophysical Research. Solid Earth; Vol. 125; No. 2; Art.
No. e2019JB018299; 10.1029/2019JB018299
- Janzamin, Majid; Ge, Rong; et el. (2019) Spectral
Learning on Matrices and Tensors; Foundations and Trends in Machine
Learning; Vol. 12; No. 5-6; 393-536; 10.1561/2200000057
- Huang, Furong; Naresh, Niranjan Uma; et el. (2019) Guaranteed
Scalable Learning of Latent Tree Models; Proceedings of Machine
Learning Research; Vol. 115; 883-893; 10.48550/arXiv.1406.4566
- Cvitkovic, Milan; Singh, Badal; et el. (2019) Open
Vocabulary Learning on Source Code with a Graph-Structured Cache;
Proceedings of Machine Learning Research; Vol. 97; 1475-1485; 10.48550/arXiv.1810.08305
- Kwok, Roberta; Ranade, Gireeja; et el. (2019) Junior
AI researchers are in demand by universities and industry; Nature;
Vol. 568; No. 7753; 581-583; 10.1038/d41586-019-01248-w
- Kossaifi, Jean; Panagakis, Yannis; et el. (2019) TensorLy:
Tensor Learning in Python; Journal of Machine Learning Research;
Vol. 20; No. 26; 1-6; 10.48550/arXiv.1610.09555
- Furlanello, Tommaso; Lipton, Zachary C.; et el. (2018) Born
Again Neural Networks; Proceedings of Machine Learning Research;
Vol. 80; 1607-1616; 10.48550/arXiv.1805.04770
- Tschannen, Michael; Khanna, Aran; et el. (2018) StrassenNets:
Deep Learning with a Multiplication Budget; Proceedings of Machine
Learning Research; Vol. 80; 4985-4994; 10.48550/arXiv.1712.03942
- Bernstein, Jeremy; Wang, Yu-Xiang; et el. (2018) signSGD:
Compressed Optimisation for Non-Convex Problems; Proceedings of
Machine Learning Research; Vol. 80; 560-569; 10.48550/arXiv.1802.04434
- Anandkumar, Anima; Deng, Yuan; et el. (2017) Homotopy
Analysis for Tensor PCA; Proceedings of Machine Learning Research;
Vol. 65; 79-104; 10.48550/arXiv.1610.09322
- Agarwal, Alekh; Anandkumar, Animashree; et el. (2017) A
Clustering Approach to Learning Sparsely Used Overcomplete
Dictionaries; IEEE Transactions on Information Theory; Vol. 63;
No. 1; 575-592; 10.1109/TIT.2016.2614684
- Anandkumar, Animashree; Ge, Rong; et el. (2017) Analyzing
Tensor Power Method Dynamics in Overcomplete Regime; Journal of
Machine Learning Research; Vol. 18; No. 22; 1-40; 10.48550/arXiv.1411.1488
- Agarwal, Alekh; Anandkumar, Animashree; et el. (2016) Learning
Sparsely Used Overcomplete Dictionaries via Alternating
Minimization; SIAM Journal of Optimization; Vol. 26; No. 4;
2775-2799; 10.1137/140979861
- Azizzadenesheli, Kamyar; Lazaric, Alessandro; et el. (2016) Open
Problem: Approximate Planning of POMDPs in the class of Memoryless
Policies; Proceedings of Machine Learning Research; Vol. 49;
1639-1642; 10.48550/arXiv.1608.04996
- Azizzadenesheli, Kamyar; Lazaric, Alessandro; et el. (2016) Reinforcement
Learning of POMDPs using Spectral Methods; Proceedings of Machine
Learning Research; Vol. 49; 193-256; 10.48550/arXiv.1602.07764
- Huang, Furong; Niranjan, U. N.; et el. (2015) Online
Tensor Methods for Learning Latent Variable Models; Journal of
Machine Learning Research; Vol. 16; 2797-2835; 10.48550/arXiv.1309.0787
- Anandkumar, Animashree; Hsu, Daniel; et el. (2015) When
Are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker
Decompositions with Structured Sparsity; Journal of Machine Learning
Research; Vol. 16; 2643-2694; 10.48550/arXiv.1308.2853
- Anandkumar, Animashree; Foster, Dean P.; et el. (2015) A
Spectral Algorithm for Latent Dirichlet Allocation; Algorithmica;
Vol. 72; No. 1; 193-214; 10.1007/s00453-014-9909-1
- Sedghi, Hanie; Janzamin, Majid; et el. (2014) Provable
Tensor Methods for Learning Mixtures of Generalized Linear Models;
Proceedings of Machine Learning Research; Vol. 51; 1223-1231; 10.48550/arXiv.1412.3046
- Anandkumar, Animashree; Ge, Rong; et el. (2014) Tensor
Decompositions for Learning Latent Variable Models; Journal of
Machine Learning Research; Vol. 15; 2773-2832; 10.48550/arXiv.1210.7559
- Anandkumar, Animashree; Ge, Rong; et el. (2014) A
Tensor Approach to Learning Mixed Membership Community Models;
Journal of Machine Learning Research; Vol. 15; 2239-2312; 10.48550/arXiv.1302.2684
- Sattari, Pegah; Kurant, Maciej; et el. (2014) Active
Learning of Multiple Source Multiple Destination Topologies; IEEE
Transactions on Signal Processing; Vol. 62; No. 8; 1926-1937; 10.1109/TSP.2014.2304431
- Janzamin, Majid and Anandkumar, Animashree (2014) High-Dimensional
Covariance Decomposition into Sparse Markov and Independence Models;
Journal of Machine Learning Research; Vol. 15; 1549-1591; 10.48550/arXiv.1211.0919
- Anandkumar, Animashree; He, Ting; et el. (2013) Seeing
through black boxes: Tracking transactions through queues under
monitoring resource constraints; Performance Evaluation; Vol. 70;
No. 12; 1090-1110; 10.1016/j.peva.2013.08.003
- Anandkumar, Animashree; Hassidim, Avinatan; et el. (2013) Topology
discovery of sparse random graphs with few participants; Random
Structures & Algorithms; Vol. 43; No. 1; 16-48; 10.1002/rsa.20420
- Anandkumar, Animashree and Valluvan, Ragupathyraj (2013) Learning
loopy graphical models with latent variables: Efficient methods and
guarantees; Annals of Statistics; Vol. 41; No. 2; 401-435; 10.48550/arXiv.1203.3887
- Liu, Ying; Chandrasekaran, Venkat; et el. (2012) Feedback
Message Passing for Inference in Gaussian Graphical Models; IEEE
Transactions on Signal Processing; Vol. 60; No. 8; 4135-4150; 10.1109/TSP.2012.2195656
- Anandkumar, Animashree; Tan, Vincent Y. F.; et el. (2012) High-Dimensional
Gaussian Graphical Model Selection: Walk Summability and Local
Separation Criterion; Journal of Machine Learning Research; Vol. 13;
2293-2337; 10.48550/arXiv.1107.1270
- Anandkumar, Animashree; Tan, Vincent Y. F.; et el. (2012) High-dimensional
structure estimation in Ising models: Local separation criterion;
Annals of Statistics; Vol. 40; No. 3; 1346-1375; 10.48550/arXiv.1107.1736
- Anandkumar, Amod J. G.; Anandkumar, Animashree; et el. (2011) Robust
Rate Maximization Game Under Bounded Channel Uncertainty; IEEE
Transactions on Vehicular Technology; Vol. 60; No. 9; 4471-4486; 10.1109/TVT.2011.2171011
- Tan, Vincent Y. F.; Anandkumar, Animashree; et el. (2011) Learning
High-Dimensional Markov Forest Distributions: Analysis of Error
Rates; Journal of Machine Learning Research; Vol. 12; 1617-1653; 10.48550/arXiv.1005.0766
- Choi, Myung Jin; Tan, Vincent Y. F.; et el. (2011) Learning
Latent Tree Graphical Models; Journal of Machine Learning Research;
Vol. 12; 1771-1812; 10.48550/arXiv.1009.2722
- Anandkumar, Animashree; Michael, Nithin; et el. (2011) Distributed
Algorithms for Learning and Cognitive Medium Access with Logarithmic
Regret; IEEE Journal on Selected Areas in Communications; Vol. 29;
No. 4; 731-745; 10.1109/JSAC.2011.110406
- Tan, Vincent Y. F.; Anandkumar, Animashree; et el. (2011) A
Large-Deviation Analysis of the Maximum-Likelihood Learning of Markov
Tree Structures; IEEE Transactions on Information Theory; Vol. 57;
No. 3; 1714-1735; 10.1109/TIT.2011.2104513
- Tan, Vincent Y. F.; Anandkumar, Animashree; et el. (2010) Learning
Gaussian Tree Models: Analysis of Error Exponents and Extremal
Structures; IEEE Transactions on Signal Processing; Vol. 58; No. 5;
2701-2714; 10.1109/TSP.2010.2042478
- Anandkumar, Animashree; Yukich, Joseph E.; et el. (2009) Energy
scaling laws for distributed inference in random fusion networks;
IEEE Journal on Selected Areas in Communications; Vol. 27; No. 7;
1203-1217; 10.1109/JSAC.2009.090916
- Anandkumar, Animashree; Tong, Lang; et el. (2009) Detection
of Gauss-Markov Random Fields With Nearest-Neighbor Dependency; IEEE
Transactions on Information Theory; Vol. 55; No. 2; 816-827; 10.1109/TIT.2008.2009855
- Anandkumar, Animashree; Tong, Lang; et el. (2008) Optimal
Node Density for Detection in Energy-Constrained Random Networks;
IEEE Transactions on Signal Processing; Vol. 56; No. 10; 5232-5245; 10.1109/TSP.2008.928514
- Anandkumar, Animashree; Tong, Lang; et el. (2008) Distributed
Estimation Via Random Access; IEEE Transactions on Information
Theory; Vol. 54; No. 7; 3175-3181; 10.1109/TIT.2008.924652
- Anandkumar, Animashree and Tong, Lang (2007) Type-Based
Random Access for Distributed Detection Over Multiaccess Fading
Channels; IEEE Transactions on Signal Processing; Vol. 55; No. 10;
5032-5043; 10.1109/TSP.2007.896302