Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDAndrew S. Lan
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[c79]Nigel Fernandez, Alexander Scarlatos, Andrew S. Lan:
SyllabusQA: A Course Logistics Question Answering Dataset. ACL (1) 2024: 10344-10369- [c78]Alexander Scarlatos, Digory Smith, Simon Woodhead, Andrew S. Lan:
Improving the Validity of Automatically Generated Feedback via Reinforcement Learning. AIED (1) 2024: 280-294 - [c77]Michael Smalenberger
, Elham Sohrabi, Mengxue Zhang, Sami Baral, Kelly Smalenberger, Andrew S. Lan, Neil T. Heffernan:
Automatic Short Answer Grading in College Mathematics Using In-Context Meta-learning: An Evaluation of the Transferability of Findings. AIED Companion (1) 2024: 409-417 - [i54]Nischal Ashok Kumar, Andrew S. Lan:
Using Large Language Models for Student-Code Guided Test Case Generation in Computer Science Education. CoRR abs/2402.07081 (2024) - [i53]Nischal Ashok Kumar, Andrew S. Lan:
Improving Socratic Question Generation using Data Augmentation and Preference Optimization. CoRR abs/2403.00199 (2024) - [i52]Alexander Scarlatos, Digory Smith, Simon Woodhead, Andrew S. Lan:
Improving the Validity of Automatically Generated Feedback via Reinforcement Learning. CoRR abs/2403.01304 (2024) - [i51]Nigel Fernandez, Alexander Scarlatos, Andrew S. Lan:
SyllabusQA: A Course Logistics Question Answering Dataset. CoRR abs/2403.14666 (2024) - [i50]Wanyong Feng, Jaewook Lee, Hunter McNichols, Alexander Scarlatos, Digory Smith, Simon Woodhead, Nancy Otero Ornelas, Andrew S. Lan:
Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models. CoRR abs/2404.02124 (2024) - [i49]Jaewook Lee, Digory Smith, Simon Woodhead, Andrew S. Lan:
Math Multiple Choice Question Generation via Human-Large Language Model Collaboration. CoRR abs/2405.00864 (2024) - [i48]Alexander Scarlatos, Wanyong Feng, Digory Smith, Simon Woodhead, Andrew S. Lan:
Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank. CoRR abs/2405.05144 (2024) - [i47]Hunter McNichols, Jaewook Lee, Stephen Fancsali, Steven Ritter, Andrew S. Lan:
Can Large Language Models Replicate ITS Feedback on Open-Ended Math Questions? CoRR abs/2405.06414 (2024) - [i46]Nigel Fernandez, Andrew S. Lan:
Interpreting Latent Student Knowledge Representations in Programming Assignments. CoRR abs/2405.08213 (2024) - [i45]Nigel Fernandez, Alexander Scarlatos, Simon Woodhead, Andrew S. Lan:
DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice Questions. CoRR abs/2406.19356 (2024) - 2023
- [c76]Aritra Ghosh, Andrew S. Lan:
DiFA: Differentiable Feature Acquisition. AAAI 2023: 7705-7713 - [c75]Andres Felipe Zambrano, Ryan S. Baker, Andrew S. Lan:
Active Learning for a Classroom Observer who Can't Time Travel. ACIIW 2023: 1-8 - [c74]Alexander Scarlatos, Andrew S. Lan:
Tree-Based Representation and Generation of Natural and Mathematical Language. ACL (1) 2023: 3714-3730 - [c73]Mengxue Zhang, Zichao Wang, Zhichao Yang, Weiqi Feng, Andrew S. Lan:
Interpretable Math Word Problem Solution Generation via Step-by-step Planning. ACL (1) 2023: 6858-6877 - [c72]Jaewook Lee, Andrew S. Lan:
SmartPhone: Exploring Keyword Mnemonic with Auto-generated Verbal and Visual Cues. AIED 2023: 16-27 - [c71]Sergey A. Sosnovsky, Peter Brusilovsky, Andrew S. Lan:
Intelligent Textbooks: The Fifth International Workshop. AIED (Posters/Late Breaking Results/...) 2023: 97-102 - [c70]Hunter McNichols, Mengxue Zhang, Andrew S. Lan:
Algebra Error Classification with Large Language Models. AIED 2023: 365-376 - [c69]Wanyong Feng, Aritra Ghosh, Stephen Sireci, Andrew S. Lan:
Balancing Test Accuracy and Security in Computerized Adaptive Testing. AIED 2023: 708-713 - [c68]Nischal Ashok Kumar, Nigel Fernandez, Zichao Wang, Andrew S. Lan:
Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank. BEA@ACL 2023: 247-259 - [c67]Nischal Ashok Kumar, Wanyong Feng, Jaewook Lee, Hunter McNichols, Aritra Ghosh, Andrew S. Lan:
A Conceptual Model for End-to-End Causal Discovery in Knowledge Tracing. EDM 2023 - [c66]Mengxue Zhang, Neil T. Heffernan, Andrew S. Lan:
Modeling and Analyzing Scorer Preferences in Short-Answer Math Questions. EDM 2023 - [e5]Sergey A. Sosnovsky, Peter Brusilovsky, Andrew S. Lan:
Proceedings of the Fifth International Workshop on Intelligent Textbooks 2023 co-located with the 24th International Conference on Artificial Intelligence in Education (AIED 2023), Tokyo, Japan, July 3, 2023. CEUR Workshop Proceedings 3444, CEUR-WS.org 2023 [contents] - [i44]Alexander Scarlatos, Andrew S. Lan:
Tree-Based Representation and Generation of Natural and Mathematical Language. CoRR abs/2302.07974 (2023) - [i43]Hunter McNichols, Mengxue Zhang, Andrew S. Lan:
Algebra Error Classification with Large Language Models. CoRR abs/2305.06163 (2023) - [i42]Jaewook Lee, Andrew S. Lan:
SmartPhone: Exploring Keyword Mnemonic with Auto-generated Verbal and Visual Cues. CoRR abs/2305.10436 (2023) - [i41]Alexander Scarlatos, Andrew S. Lan:
RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning. CoRR abs/2305.14502 (2023) - [i40]Nischal Ashok Kumar, Wanyong Feng, Jaewook Lee, Hunter McNichols, Aritra Ghosh, Andrew S. Lan:
A Conceptual Model for End-to-End Causal Discovery in Knowledge Tracing. CoRR abs/2305.16165 (2023) - [i39]Wanyong Feng, Aritra Ghosh, Stephen Sireci, Andrew S. Lan:
Balancing Test Accuracy and Security in Computerized Adaptive Testing. CoRR abs/2305.18312 (2023) - [i38]Mengxue Zhang, Zichao Wang, Zhichao Yang, Weiqi Feng, Andrew S. Lan:
Interpretable Math Word Problem Solution Generation Via Step-by-step Planning. CoRR abs/2306.00784 (2023) - [i37]Mengxue Zhang, Neil T. Heffernan, Andrew S. Lan:
Modeling and Analyzing Scorer Preferences in Short-Answer Math Questions. CoRR abs/2306.00791 (2023) - [i36]Nischal Ashok Kumar, Nigel Fernandez, Zichao Wang, Andrew S. Lan:
Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank. CoRR abs/2306.08847 (2023) - [i35]Hunter McNichols, Wanyong Feng, Jaewook Lee, Alexander Scarlatos, Digory Smith, Simon Woodhead, Andrew S. Lan:
Exploring Automated Distractor and Feedback Generation for Math Multiple-choice Questions via In-context Learning. CoRR abs/2308.03234 (2023) - 2022
- [c65]Aritra Ghosh, Saayan Mitra, Andrew S. Lan:
DiPS: Differentiable Policy for Sketching in Recommender Systems. AAAI 2022: 6703-6712 - [c64]Sergey A. Sosnovsky, Peter Brusilovsky, Andrew S. Lan:
Intelligent Textbooks: Themes and Topics. AIED (2) 2022: 111-114 - [c63]Nigel Fernandez, Aritra Ghosh, Naiming Liu, Zichao Wang, Benoît Choffin, Richard G. Baraniuk, Andrew S. Lan:
Automated Scoring for Reading Comprehension via In-context BERT Tuning. AIED (1) 2022: 691-697 - [c62]Yun-Wei Chu, Seyyedali Hosseinalipour, Elizabeth Tenorio, Laura M. Cruz Castro, Kerrie A. Douglas, Andrew S. Lan, Christopher G. Brinton:
Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning. CIKM 2022: 3033-3042 - [c61]Alexander Scarlatos, Christopher Brinton, Andrew S. Lan:
Process-BERT: A Framework for Representation Learning on Educational Process Data. EDM 2022 - [c60]Mengxue Zhang, Sami Baral, Neil T. Heffernan, Andrew S. Lan:
Automatic Short Math Answer Grading via In-context Meta-learning. EDM 2022 - [c59]Naiming Liu, Zichao Wang, Richard G. Baraniuk, Andrew S. Lan:
Open-ended Knowledge Tracing for Computer Science Education. EMNLP 2022: 3849-3862 - [e4]Sergey A. Sosnovsky, Peter Brusilovsky, Andrew S. Lan:
Proceedings of the Fourth International Workshop on Intelligent Textbooks 2022 co-located with 23d International Conference on Artificial Intelligence in Education (AIED 2022), Durham, UK, July 27, 2022. CEUR Workshop Proceedings 3192, CEUR-WS.org 2022 [contents] - [i34]Naiming Liu, Zichao Wang, Richard G. Baraniuk, Andrew S. Lan:
Open-Ended Knowledge Tracing. CoRR abs/2203.03716 (2022) - [i33]Alexander Scarlatos, Christopher G. Brinton, Andrew S. Lan:
Process-BERT: A Framework for Representation Learning on Educational Process Data. CoRR abs/2204.13607 (2022) - [i32]Nigel Fernandez, Aritra Ghosh, Naiming Liu, Zichao Wang, Benoît Choffin, Richard G. Baraniuk, Andrew S. Lan:
Automated Scoring for Reading Comprehension via In-context BERT Tuning. CoRR abs/2205.09864 (2022) - [i31]Mengxue Zhang, Sami Baral, Neil T. Heffernan, Andrew S. Lan:
Automatic Short Math Answer Grading via In-context Meta-learning. CoRR abs/2205.15219 (2022) - [i30]Yun-Wei Chu, Seyyedali Hosseinalipour, Elizabeth Tenorio, Laura M. Cruz Castro, Kerrie A. Douglas, Andrew S. Lan, Christopher G. Brinton:
Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning. CoRR abs/2208.01182 (2022) - [i29]Yun-Wei Chu, Seyyedali Hosseinalipour, Elizabeth Tenorio, Laura M. Cruz Castro, Kerrie A. Douglas, Andrew S. Lan, Christopher G. Brinton:
Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics. CoRR abs/2212.02985 (2022) - 2021
- [j8]Guohao Lan, Mohammadreza F. Imani, Zida Liu, José Manjarrés, Wenjun Hu, Andrew S. Lan, David R. Smith, Maria Gorlatova:
MetaSense: Boosting RF Sensing Accuracy Using Dynamic Metasurface Antenna. IEEE Internet Things J. 8(18): 14110-14126 (2021) - [j7]Setareh Maghsudi, Andrew S. Lan, Jie Xu, Mihaela van der Schaar:
Personalized Education in the Artificial Intelligence Era: What to Expect Next. IEEE Signal Process. Mag. 38(3): 37-50 (2021) - [c58]Zichao Wang, Andrew S. Lan, Richard G. Baraniuk:
Mathematical Formula Representation via Tree Embeddings. iTextbooks@AIED 2021: 121-133 - [c57]Aritra Ghosh, Jay Raspat, Andrew S. Lan:
Option Tracing: Beyond Correctness Analysis in Knowledge Tracing. AIED (1) 2021: 137-149 - [c56]Yun-Wei Chu, Elizabeth Tenorio, Laura M. Cruz Castro, Kerrie A. Douglas, Andrew S. Lan, Christopher G. Brinton:
Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach. IEEE BigData 2021: 1389-1398 - [c55]Zichao Wang, Mengxue Zhang, Richard G. Baraniuk, Andrew S. Lan:
Scientific Formula Retrieval via Tree Embeddings. IEEE BigData 2021: 1493-1503 - [c54]Aritra Ghosh, Andrew S. Lan:
Contrastive Learning Improves Model Robustness Under Label Noise. CVPR Workshops 2021: 2703-2708 - [c53]Mengxue Zhang, Zichao Wang, Richard G. Baraniuk, Andrew S. Lan:
Math Operation Embeddings for Open-ended Solution Analysis and Feedback. EDM 2021 - [c52]Zichao Wang, Andrew S. Lan, Richard G. Baraniuk:
Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints. EMNLP (1) 2021: 5986-5999 - [c51]Aritra Ghosh, Andrew S. Lan:
BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing. IJCAI 2021: 2410-2417 - [c50]Brian Zylich, Andrew S. Lan:
Linguistic Skill Modeling for Second Language Acquisition. LAK 2021: 141-150 - [c49]Shamya Karumbaiah, Andrew S. Lan, Sachit Nagpal, Ryan S. Baker, Anthony Botelho, Neil T. Heffernan:
Using Past Data to Warm Start Active Machine Learning: Does Context Matter? LAK 2021: 151-160 - [c48]Aritra Ghosh, Andrew S. Lan:
Do We Really Need Gold Samples for Sample Weighting under Label Noise? WACV 2021: 3921-3930 - [e3]Sergey A. Sosnovsky, Peter Brusilovsky, Richard G. Baraniuk, Andrew S. Lan:
Proceedings of the Third International Workshop on Inteligent Textbooks 2021 Co-located with 22nd International Conference on Artificial Intelligence in Education (AIED 2021), Online, June 15, 2021. CEUR Workshop Proceedings 2895, CEUR-WS.org 2021 [contents] - [i28]Shalini Pandey, Andrew S. Lan, George Karypis, Jaideep Srivastava:
Learning Student Interest Trajectory for MOOCThread Recommendation. CoRR abs/2101.05625 (2021) - [i27]Setareh Maghsudi, Andrew S. Lan, Jie Xu, Mihaela van der Schaar:
Personalized Education in the AI Era: What to Expect Next? CoRR abs/2101.10074 (2021) - [i26]Aritra Ghosh, Andrew S. Lan:
Contrastive Learning Improves Model Robustness Under Label Noise. CoRR abs/2104.08984 (2021) - [i25]Aritra Ghosh, Jay Raspat, Andrew S. Lan:
Option Tracing: Beyond Correctness Analysis in Knowledge Tracing. CoRR abs/2104.09043 (2021) - [i24]Aritra Ghosh, Andrew S. Lan:
Do We Really Need Gold Samples for Sample Weighting Under Label Noise? CoRR abs/2104.09045 (2021) - [i23]Mengxue Zhang, Zichao Wang, Richard G. Baraniuk, Andrew S. Lan:
Math Operation Embeddings for Open-ended Solution Analysis and Feedback. CoRR abs/2104.12047 (2021) - [i22]Aritra Ghosh, Andrew S. Lan:
BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing. CoRR abs/2108.07386 (2021) - [i21]Zichao Wang, Andrew S. Lan, Richard G. Baraniuk:
Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints. CoRR abs/2109.04546 (2021) - [i20]Yun-Wei Chu, Elizabeth Tenorio, Laura Melissa Cruz Castro, Kerrie A. Douglas, Andrew S. Lan, Christopher G. Brinton:
Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach. CoRR abs/2111.00901 (2021) - [i19]Aritra Ghosh, Saayan Mitra, Andrew S. Lan:
DiPS: Differentiable Policy for Sketching in Recommender Systems. CoRR abs/2112.07616 (2021) - 2020
- [j6]Adam Winchell, Andrew S. Lan, Michael Mozer:
Highlights as an Early Predictor of Student Comprehension and Interests. Cogn. Sci. 44(11) (2020) - [j5]Yupei Zhang, Huan Dai, Yue Yun, Shuhui Liu, Andrew S. Lan, Xuequn Shang:
Meta-knowledge dictionary learning on 1-bit response data for student knowledge diagnosis. Knowl. Based Syst. 205: 106290 (2020) - [c47]Brian Zylich, Adam Viola, Brokk Toggerson, Lara Al-Hariri, Andrew S. Lan:
Exploring Automated Question Answering Methods for Teaching Assistance. AIED (1) 2020: 610-622 - [c46]Aritra Ghosh, Beverly P. Woolf, Shlomo Zilberstein, Andrew S. Lan:
Skill-based Career Path Modeling and Recommendation. IEEE BigData 2020: 1156-1165 - [c45]Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer:
MSE-Optimal Neural Network Initialization via Layer Fusion. CISS 2020: 1-6 - [c44]Zichao Wang, Yi Gu, Andrew S. Lan, Richard G. Baraniuk:
VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics. EDM 2020 - [c43]Shashank Sonkar, Andrew S. Lan, Andrew E. Waters, Phillip Grimaldi, Richard G. Baraniuk:
qDKT: Question-centric Deep Knowledge Tracing. EDM 2020 - [c42]Tsung-Yen Yang, Andrew S. Lan, Karthik Narasimhan:
Robust and Interpretable Grounding of Spatial References with Relation Networks. EMNLP (Findings) 2020: 1908-1923 - [c41]Shalini Pandey, Andrew S. Lan, George Karypis, Jaideep Srivastava:
Learning Student Interest Trajectory for MOOC Thread Recommendation. ICDM (Workshops) 2020: 400-407 - [c40]Aritra Ghosh, Neil T. Heffernan, Andrew S. Lan:
Context-Aware Attentive Knowledge Tracing. KDD 2020: 2330-2339 - [e2]Sergey A. Sosnovsky, Peter Brusilovsky, Richard G. Baraniuk, Andrew S. Lan:
Proceedings of the Second International Workshop on Intelligent Textbooks 2020 co-located with 21st International Conference on Artificial Intelligence in Education (AIED 2020), Online, July 06, 2020. CEUR Workshop Proceedings 2674, CEUR-WS.org 2020 [contents] - [i18]Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer:
MSE-Optimal Neural Network Initialization via Layer Fusion. CoRR abs/2001.10509 (2020) - [i17]Shashank Sonkar, Andrew E. Waters, Andrew S. Lan, Phillip J. Grimaldi, Richard G. Baraniuk:
qDKT: Question-centric Deep Knowledge Tracing. CoRR abs/2005.12442 (2020) - [i16]Jack Zichao Wang, Yi Gu, Andrew S. Lan, Richard G. Baraniuk:
VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics. CoRR abs/2005.13107 (2020) - [i15]Aritra Ghosh, Neil T. Heffernan, Andrew S. Lan:
Context-Aware Attentive Knowledge Tracing. CoRR abs/2007.12324 (2020)
2010 – 2019
- 2019
- [c39]Indu Manickam, Andrew S. Lan, Gautam Dasarathy, Richard G. Baraniuk:
IdeoTrace: a framework for ideology tracing with a case study on the 2016 U.S. presidential election. ASONAM 2019: 274-281 - [c38]Zhiyun Ren, Xia Ning, Andrew S. Lan, Huzefa Rangwala:
Grade Prediction with Neural Collaborative Filtering. DSAA 2019: 1-10 - [c37]Zhiyun Ren, Xia Ning, Andrew S. Lan, Huzefa Rangwala:
Grade Prediction Based on Cumulative Knowledge and Co-taken Courses. EDM 2019 - [c36]Zichao Wang, Andrew S. Lan, Andrew E. Waters, Phillip Grimaldi, Richard G. Baraniuk:
A Meta-Learning Augmented Bidirectional Transformer Model for Automatic Short Answer Grading. EDM 2019 - [c35]Tsung-Yen Yang, Ryan S. Baker, Christoph Studer, Neil T. Heffernan, Andrew S. Lan:
Active Learning for Student Affect Detection. EDM 2019 - [c34]Yuwei Tu, Christopher G. Brinton, Andrew S. Lan, Mung Chiang:
Adaptive Remediation with Multi-modal Content. HCI (32) 2019: 455-468 - [c33]Patrick Hansen, Richard Junior Bustamante, Tsung-Yen Yang, Elizabeth Tenorio, Christopher G. Brinton, Mung Chiang, Andrew S. Lan:
Predicting the Timing and Quality of Responses in Online Discussion Forums. ICDCS 2019: 1931-1940 - [c32]Parinaz Naghizadeh, Maria Gorlatova, Andrew S. Lan, Mung Chiang:
Hurts to Be Too Early: Benefits and Drawbacks of Communication in Multi-Agent Learning. INFOCOM 2019: 622-630 - [e1]Sergey A. Sosnovsky, Peter Brusilovsky, Richard G. Baraniuk, Rakesh Agrawal, Andrew S. Lan:
Proceedings of the First Workshop on Intelligent Textbooks co-located with 20th International Conference on Artificial Intelligence in Education (AIED 2019), Chicago, IL, USA, June 25, 2019. CEUR Workshop Proceedings 2384, CEUR-WS.org 2019 [contents] - [i14]Indu Manickam, Andrew S. Lan, Gautam Dasarathy, Richard G. Baraniuk:
IdeoTrace: A Framework for Ideology Tracing with a Case Study on the 2016 U.S. Presidential Election. CoRR abs/1905.08831 (2019) - 2018
- [j4]Amirali Aghazadeh, Mohammad Golbabaee, Andrew S. Lan, Richard G. Baraniuk:
Insense: Incoherent sensor selection for sparse signals. Signal Process. 150: 57-65 (2018) - [j3]Christopher G. Brinton, Swapna Buccapatnam, Liang Zheng, Da Cao, Andrew S. Lan, Felix Ming Fai Wong, Sangtae Ha, Mung Chiang, H. Vincent Poor:
On the Efficiency of Online Social Learning Networks. IEEE/ACM Trans. Netw. 26(5): 2076-2089 (2018) - [c31]Da Cao, Andrew S. Lan, Weiyu Chen, Christopher G. Brinton, Mung Chiang:
Learner Behavioral Feature Refinement and Augmentation Using GANs. AIED (2) 2018: 41-46 - [c30]Tsung-Yen Yang, Christopher G. Brinton, Prateek Mittal, Mung Chiang, Andrew S. Lan:
Learning Informative and Private Representations via Generative Adversarial Networks. IEEE BigData 2018: 1534-1543 - [c29]Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer:
PhaseLin: Linear phase retrieval. CISS 2018: 1-6 - [c28]Andrew S. Lan, Mung Chiang, Christoph Studer:
Linearized binary regression. CISS 2018: 1-6 - [c27]Weiyu Chen, Andrew S. Lan, Da Cao, Christopher G. Brinton, Mung Chiang:
Behavioral Analysis at Scale: Learning Course Prerequisite Structures from Learner Clickstreams. EDM 2018 - [c26]Adam Winchell, Michael Mozer, Andrew S. Lan, Phillip Grimaldi, Harold Pashler:
Textbook annotations as an early predictor of student learning. EDM 2018 - [c25]Amirali Aghazadeh, Mohammad Golbabaee, Andrew S. Lan, Richard G. Baraniuk:
Insense: Incoherent Sensor Selection for Sparse Signals. ICASSP 2018: 4689-4693 - [c24]Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer:
Linear Spectral Estimators and an Application to Phase Retrieval. ICML 2018: 1729-1738 - [c23]Andrew S. Lan, Mung Chiang, Christoph Studer:
An Estimation and Analysis Framework for the Rasch Model. ICML 2018: 2889-2897 - [c22]Mikhail Khodak, Liang Zheng, Andrew S. Lan, Carlee Joe-Wong, Mung Chiang:
Learning Cloud Dynamics to Optimize Spot Instance Bidding Strategies. INFOCOM 2018: 2762-2770 - [c21]Zichao Wang, Andrew S. Lan, Weili Nie, Andrew E. Waters, Phillip J. Grimaldi, Richard G. Baraniuk:
QG-net: a data-driven question generation model for educational content. L@S 2018: 7:1-7:10 - [c20]Andrew S. Lan, Jonathan C. Spencer, Ziqi Chen, Christopher G. Brinton, Mung Chiang:
Personalized Thread Recommendation for MOOC Discussion Forums. ECML/PKDD (2) 2018: 725-740 - [i13]Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer:
PhaseLin: Linear Phase Retrieval. CoRR abs/1802.00432 (2018) - [i12]Ramina Ghods, Andrew S. Lan, Tom Goldstein, Christoph Studer:
Linear Spectral Estimators and an Application to Phase Retrieval. CoRR abs/1806.03547 (2018) - [i11]Andrew S. Lan, Mung Chiang, Christoph Studer:
An Estimation and Analysis Framework for the Rasch Model. CoRR abs/1806.03551 (2018) - [i10]Andrew S. Lan, Jonathan C. Spencer, Ziqi Chen, Christopher G. Brinton, Mung Chiang:
Personalized Thread Recommendation for MOOC Discussion Forums. CoRR abs/1806.08468 (2018) - 2017
- [j2]Andrew S. Lan, Andrew E. Waters, Christoph Studer, Richard G. Baraniuk:
BLAh: Boolean Logic Analysis for Graded Student Response Data. IEEE J. Sel. Top. Signal Process. 11(5): 754-764 (2017) - [c19]