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2020 – today
- 2023
[j22]Jitao Wang
, Yu Fang, Elena Frank, Maureen A. Walton, Margit Burmeister, Ambuj Tewari, Walter H. Dempsey, Timothy NeCamp, Srijan Sen, Zhenke Wu:
Effectiveness of gamified team competition as mHealth intervention for medical interns: a cluster micro-randomized trial. npj Digit. Medicine 6 (2023)- [i86]Vinod Raman, Unique Subedi, Ambuj Tewari:
A Characterization of Multilabel Learnability. CoRR abs/2301.02729 (2023) - [i85]Gang Qiao, Ambuj Tewari:
An Asymptotically Optimal Algorithm for the One-Dimensional Convex Hull Feasibility Problem. CoRR abs/2302.02033 (2023) - [i84]Preetham Mohan, Ambuj Tewari:
Quantum Learning Theory Beyond Batch Binary Classification. CoRR abs/2302.07409 (2023) - [i83]Vinod Raman, Unique Subedi, Ambuj Tewari:
A Characterization of Online Multiclass Learnability. CoRR abs/2303.17716 (2023) - [i82]Vinod Raman, Unique Subedi, Ambuj Tewari:
On the Learnability of Multilabel Ranking. CoRR abs/2304.03337 (2023) - [i81]Yash Patel, Declan McNamara, Jackson Loper, Jeffrey Regier, Ambuj Tewari:
Variational Inference with Coverage Guarantees. CoRR abs/2305.14275 (2023) - 2022
- [j21]Runxuan Jiang, Tarun Gogineni, Joshua Kammeraad, Yifei He, Ambuj Tewari, Paul M. Zimmerman:
Conformer-RL: A deep reinforcement learning library for conformer generation. J. Comput. Chem. 43(27): 1880-1886 (2022) - [c83]Yuntian Deng, Xingyu Zhou, Baekjin Kim, Ambuj Tewari, Abhishek Gupta, Ness B. Shroff:
Weighted Gaussian Process Bandits for Non-stationary Environments. AISTATS 2022: 6909-6932 - [c82]Yangyi Lu, Amirhossein Meisami, Ambuj Tewari:
Efficient Reinforcement Learning with Prior Causal Knowledge. CLeaR 2022: 526-541 - [c81]Ziping Xu, Ambuj Tewari:
On the Statistical Benefits of Curriculum Learning. ICML 2022: 24663-24682 - [c80]Vinod Raman, Ambuj Tewari:
Online Agnostic Multiclass Boosting. NeurIPS 2022 - [c79]Ziping Xu, Eunjae Shim, Ambuj Tewari, Paul M. Zimmerman:
Adaptive Sampling for Discovery. NeurIPS 2022 - [c78]Anthony DiGiovanni, Ambuj Tewari:
Balancing adaptability and non-exploitability in repeated games. UAI 2022: 559-568 - [i80]Laura Niss, Yuekai Sun, Ambuj Tewari:
Achieving Representative Data via Convex Hull Feasibility Sampling Algorithms. CoRR abs/2204.06664 (2022) - [i79]Kihyuk Hong, Yuhang Li, Ambuj Tewari:
An Optimization-based Algorithm for Non-stationary Kernel Bandits without Prior Knowledge. CoRR abs/2205.14775 (2022) - [i78]Ziping Xu, Eunjae Shim, Ambuj Tewari, Paul M. Zimmerman:
Adaptive Learning for Discovery. CoRR abs/2205.14829 (2022) - [i77]Vinod Raman, Ambuj Tewari:
Online Agnostic Multiclass Boosting. CoRR abs/2205.15113 (2022) - [i76]Vinod Raman, Unique Subedi, Ambuj Tewari:
Probabilistically Robust PAC Learning. CoRR abs/2211.05656 (2022) - [i75]Sunrit Chakraborty, Saptarshi Roy, Ambuj Tewari:
Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits. CoRR abs/2211.05964 (2022) - [i74]Chinmaya Kausik, Kevin Tan, Ambuj Tewari:
Learning Mixtures of Markov Chains and MDPs. CoRR abs/2211.09403 (2022) - [i73]Kevin Tan, Yangyi Lu, Chinmaya Kausik, Yixin Wang, Ambuj Tewari:
Offline Policy Evaluation and Optimization under Confounding. CoRR abs/2211.16583 (2022) - 2021
- [j20]Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis:
Optimism-Based Adaptive Regulation of Linear-Quadratic Systems. IEEE Trans. Autom. Control. 66(4): 1802-1808 (2021) - [c77]Ziping Xu, Amirhossein Meisami, Ambuj Tewari:
Decision Making Problems with Funnel Structure: A Multi-Task Learning Approach with Application to Email Marketing Campaigns. AISTATS 2021: 127-135 - [c76]Yangyi Lu, Amirhossein Meisami, Ambuj Tewari:
Low-Rank Generalized Linear Bandit Problems. AISTATS 2021: 460-468 - [c75]Ziping Xu, Ambuj Tewari:
Representation Learning Beyond Linear Prediction Functions. NeurIPS 2021: 4792-4804 - [c74]Yangyi Lu, Amirhossein Meisami, Ambuj Tewari:
Causal Bandits with Unknown Graph Structure. NeurIPS 2021: 24817-24828 - [c73]Anthony DiGiovanni, Ambuj Tewari:
Thompson sampling for Markov games with piecewise stationary opponent policies. UAI 2021: 738-748 - [i72]Yangyi Lu, Amirhossein Meisami, Ambuj Tewari:
Causal Markov Decision Processes: Learning Good Interventions Efficiently. CoRR abs/2102.07663 (2021) - [i71]Ziping Xu, Ambuj Tewari:
Representation Learning Beyond Linear Prediction Functions. CoRR abs/2105.14989 (2021) - [i70]Yangyi Lu, Amirhossein Meisami, Ambuj Tewari:
Causal Bandits with Unknown Graph Structure. CoRR abs/2106.02988 (2021) - [i69]Yuntian Deng, Xingyu Zhou, Baekjin Kim, Ambuj Tewari, Abhishek Gupta, Ness B. Shroff:
Weighted Gaussian Process Bandits for Non-stationary Environments. CoRR abs/2107.02371 (2021) - [i68]Yangyi Lu, Ziping Xu, Ambuj Tewari:
Bandit Algorithms for Precision Medicine. CoRR abs/2108.04782 (2021) - [i67]Gautam Chandrasekaran, Ambuj Tewari:
Online Learning in Adversarial MDPs: Is the Communicating Case Harder than Ergodic? CoRR abs/2111.02024 (2021) - [i66]Ziping Xu, Ambuj Tewari:
On the Statistical Benefits of Curriculum Learning. CoRR abs/2111.07126 (2021) - [i65]Anthony DiGiovanni, Ambuj Tewari:
Balancing Adaptability and Non-exploitability in Repeated Games. CoRR abs/2112.10314 (2021) - [i64]Aditya Modi, Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis:
Joint Learning of Linear Time-Invariant Dynamical Systems. CoRR abs/2112.10955 (2021) - 2020
- [j19]Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis:
Input perturbations for adaptive control and learning. Autom. 117: 108950 (2020) - [j18]Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis:
On adaptive Linear-Quadratic regulators. Autom. 117: 108982 (2020) - [j17]Joshua Kammeraad, Jack Goetz, Eric Walker, Ambuj Tewari, Paul M. Zimmerman:
What Does the Machine Learn? Knowledge Representations of Chemical Reactivity. J. Chem. Inf. Model. 60(3): 1290-1301 (2020) - [c72]Aditya Modi, Nan Jiang, Ambuj Tewari, Satinder Singh:
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles. AISTATS 2020: 2010-2020 - [c71]Tarun Gogineni, Ziping Xu, Exequiel Punzalan, Runxuan Jiang, Joshua Kammeraad, Ambuj Tewari, Paul M. Zimmerman:
TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search. NeurIPS 2020 - [c70]Young Hun Jung, Baekjin Kim, Ambuj Tewari:
On the Equivalence between Online and Private Learnability beyond Binary Classification. NeurIPS 2020 - [c69]Ziping Xu, Ambuj Tewari:
Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting. NeurIPS 2020 - [c68]Baekjin Kim, Ambuj Tewari:
Randomized Exploration for Non-Stationary Stochastic Linear Bandits. UAI 2020: 71-80 - [c67]Yangyi Lu, Amirhossein Meisami, Ambuj Tewari, William Yan:
Regret Analysis of Bandit Problems with Causal Background Knowledge. UAI 2020: 141-150 - [c66]Laura Niss, Ambuj Tewari:
What You See May Not Be What You Get: UCB Bandit Algorithms Robust to ε-Contamination. UAI 2020: 450-459 - [c65]Aditya Modi, Ambuj Tewari:
No-regret Exploration in Contextual Reinforcement Learning. UAI 2020: 829-838 - [i63]Ziping Xu, Ambuj Tewari:
Near-optimal Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms for the Non-episodic Setting. CoRR abs/2002.02302 (2020) - [i62]A. Philip Dawid, Ambuj Tewari:
On Learnability under General Stochastic Processes. CoRR abs/2005.07605 (2020) - [i61]Young Hun Jung, Baekjin Kim, Ambuj Tewari:
On the Equivalence between Online and Private Learnability beyond Binary Classification. CoRR abs/2006.01980 (2020) - [i60]Yangyi Lu, Amirhossein Meisami, Ambuj Tewari:
Low-Rank Generalized Linear Bandit Problems. CoRR abs/2006.02948 (2020) - [i59]Tarun Gogineni, Ziping Xu, Exequiel Punzalan, Runxuan Jiang, Joshua Kammeraad, Ambuj Tewari, Paul M. Zimmerman:
TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search. CoRR abs/2006.07078 (2020) - [i58]Jack Goetz, Ambuj Tewari:
Federated Learning via Synthetic Data. CoRR abs/2008.04489 (2020) - [i57]Ziping Xu, Amir Meisami, Ambuj Tewari:
Decision Making Problems with Funnel Structure: A Multi-Task Learning Approach with Application to Email Marketing Campaigns. CoRR abs/2010.08048 (2020)
2010 – 2019
- 2019
- [j16]Eric Walker, Joshua Kammeraad, Jonathan Goetz, Michael T. Robo, Ambuj Tewari, Paul M. Zimmerman:
Learning To Predict Reaction Conditions: Relationships between Solvent, Molecular Structure, and Catalyst. J. Chem. Inf. Model. 59(9): 3645-3654 (2019) - [j15]Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis:
Finite-Time Adaptive Stabilization of Linear Systems. IEEE Trans. Autom. Control. 64(8): 3498-3505 (2019) - [c64]Daniel T. Zhang, Young Hun Jung, Ambuj Tewari:
Online Multiclass Boosting with Bandit Feedback. AISTATS 2019: 1148-1156 - [c63]Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis:
On Applications of Bootstrap in Continuous Space Reinforcement Learning. CDC 2019: 1977-1984 - [c62]Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis:
Randomized Algorithms for Data-Driven Stabilization of Stochastic Linear Systems. DSW 2019: 170-174 - [c61]Baekjin Kim, Ambuj Tewari:
On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems. NeurIPS 2019: 2691-2700 - [c60]Jacob D. Abernethy, Young Hun Jung, Chansoo Lee, Audra McMillan, Ambuj Tewari:
Online Learning via the Differential Privacy Lens. NeurIPS 2019: 8892-8902 - [c59]Young Hun Jung, Ambuj Tewari:
Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems. NeurIPS 2019: 9005-9014 - [c58]Othman El Balghiti, Adam N. Elmachtoub, Paul Grigas, Ambuj Tewari:
Generalization Bounds in the Predict-then-Optimize Framework. NeurIPS 2019: 14389-14398 - [i56]Baekjin Kim, Ambuj Tewari:
On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems. CoRR abs/1902.00610 (2019) - [i55]Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis:
On Applications of Bootstrap in Continuous Space Reinforcement Learning. CoRR abs/1903.05803 (2019) - [i54]Aditya Modi, Ambuj Tewari:
Contextual Markov Decision Processes using Generalized Linear Models. CoRR abs/1903.06187 (2019) - [i53]Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis:
Randomized Algorithms for Data-Driven Stabilization of Stochastic Linear Systems. CoRR abs/1905.06978 (2019) - [i52]Othman El Balghiti, Adam N. Elmachtoub, Paul Grigas, Ambuj Tewari:
Generalization Bounds in the Predict-then-Optimize Framework. CoRR abs/1905.11488 (2019) - [i51]Young Hun Jung, Ambuj Tewari:
Regret Bounds for Thompson Sampling in Restless Bandit Problems. CoRR abs/1905.12673 (2019) - [i50]Yangyi Lu, Amirhossein Meisami, Ambuj Tewari, Zhenyu Yan:
Regret Analysis of Causal Bandit Problems. CoRR abs/1910.04938 (2019) - [i49]Jack Goetz, Ambuj Tewari:
Not All are Made Equal: Consistency of Weighted Averaging Estimators Under Active Learning. CoRR abs/1910.05321 (2019) - [i48]Laura Niss, Ambuj Tewari:
What You See May Not Be What You Get: UCB Bandit Algorithms Robust to ε-Contamination. CoRR abs/1910.05625 (2019) - [i47]Young Hun Jung, Marc Abeille, Ambuj Tewari:
Thompson Sampling in Non-Episodic Restless Bandits. CoRR abs/1910.05654 (2019) - [i46]Aditya Modi, Nan Jiang, Ambuj Tewari, Satinder Singh:
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles. CoRR abs/1910.10597 (2019) - [i45]Daniel T. Zhang, Young Hun Jung, Ambuj Tewari:
Online Boosting for Multilabel Ranking with Top-k Feedback. CoRR abs/1910.10937 (2019) - [i44]Baekjin Kim, Ambuj Tewari:
Near-optimal Oracle-efficient Algorithms for Stationary and Non-Stationary Stochastic Linear Bandits. CoRR abs/1912.05695 (2019) - 2018
- [j14]Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis:
Finite time identification in unstable linear systems. Autom. 96: 342-353 (2018) - [j13]Zifan Li, Ambuj Tewari:
Sampled fictitious play is Hannan consistent. Games Econ. Behav. 109: 401-412 (2018) - [c57]Young Hun Jung, Ambuj Tewari:
Online Boosting Algorithms for Multi-label Ranking. AISTATS 2018: 279-287 - [c56]Aditya Modi, Nan Jiang, Satinder Singh, Ambuj Tewari:
Markov Decision Processes with Continuous Side Information. ALT 2018: 597-618 - [c55]Jack Goetz, Ambuj Tewari, Paul M. Zimmerman:
Active Learning for Non-Parametric Regression Using Purely Random Trees. NeurIPS 2018: 2542-2551 - [c54]Yitong Sun, Anna C. Gilbert, Ambuj Tewari:
But How Does It Work in Theory? Linear SVM with Random Features. NeurIPS 2018: 3383-3392 - [i43]Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis:
On Optimality of Adaptive Linear-Quadratic Regulators. CoRR abs/1806.10749 (2018) - [i42]Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis:
Finite Time Adaptive Stabilization of LQ Systems. CoRR abs/1807.09120 (2018) - [i41]Anna C. Gilbert, Ambuj Tewari, Yitong Sun:
But How Does It Work in Theory? Linear SVM with Random Features. CoRR abs/1809.04481 (2018) - [i40]Yitong Sun, Anna C. Gilbert, Ambuj Tewari:
Random ReLU Features: Universality, Approximation, and Composition. CoRR abs/1810.04374 (2018) - [i39]Daniel T. Zhang, Young Hun Jung, Ambuj Tewari:
Online Multiclass Boosting with Bandit Feedback. CoRR abs/1810.05290 (2018) - [i38]Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis:
Input Perturbations for Adaptive Regulation and Learning. CoRR abs/1811.04258 (2018) - 2017
- [j12]Sougata Chaudhuri, Ambuj Tewari:
Online Learning to Rank with Top-k Feedback. J. Mach. Learn. Res. 18: 103:1-103:50 (2017) - [j11]Nagarajan Natarajan, Inderjit S. Dhillon, Pradeep Ravikumar, Ambuj Tewari:
Cost-Sensitive Learning with Noisy Labels. J. Mach. Learn. Res. 18: 155:1-155:33 (2017) - [j10]Zifan Li, Ambuj Tewari:
Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multi-armed Bandits. J. Mach. Learn. Res. 18: 183:1-183:24 (2017) - [j9]Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis:
Optimality of Fast-Matching Algorithms for Random Networks With Applications to Structural Controllability. IEEE Trans. Control. Netw. Syst. 4(4): 770-780 (2017) - [j8]Prateek Jain, Ambuj Tewari, Inderjit S. Dhillon:
Partial Hard Thresholding. IEEE Trans. Inf. Theory 63(5): 3029-3038 (2017) - [c53]Young Hun Jung, Jack Goetz, Ambuj Tewari:
Online multiclass boosting. NIPS 2017: 919-928 - [c52]Kristjan H. Greenewald, Ambuj Tewari, Susan A. Murphy, Predrag V. Klasnja:
Action Centered Contextual Bandits. NIPS 2017: 5977-5985 - [p1]Ambuj Tewari, Susan A. Murphy:
From Ads to Interventions: Contextual Bandits in Mobile Health. Mobile Health - Sensors, Analytic Methods, and Applications 2017: 495-517 - [i37]Zifan Li, Ambuj Tewari:
Beyond the Hazard Rate: More Perturbation Algorithms for Adversarial Multi-armed Bandits. CoRR abs/1702.05536 (2017) - [i36]Young Hun Jung, Ambuj Tewari:
Online Multiclass Boosting. CoRR abs/1702.07305 (2017) - [i35]Huitian Lei, Ambuj Tewari, Susan A. Murphy:
An Actor-Critic Contextual Bandit Algorithm for Personalized Mobile Health Interventions. CoRR abs/1706.09090 (2017) - [i34]Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis:
Finite Time Identification in Unstable Linear Systems. CoRR abs/1710.01852 (2017) - [i33]Young Hun Jung, Ambuj Tewari:
Online Boosting Algorithms for Multi-label Ranking. CoRR abs/1710.08079 (2017) - [i32]Aditya Modi, Nan Jiang, Satinder Singh, Ambuj Tewari:
Markov Decision Processes with Continuous Side Information. CoRR abs/1711.05726 (2017) - [i31]Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis:
Finite Time Analysis of Optimal Adaptive Policies for Linear-Quadratic Systems. CoRR abs/1711.07230 (2017) - [i30]Jacob D. Abernethy, Chansoo Lee, Audra McMillan, Ambuj Tewari:
Online Learning via Differential Privacy. CoRR abs/1711.10019 (2017) - 2016
- [c51]Bopeng Li, Sougata Chaudhuri, Ambuj Tewari:
Handling Class Imbalance in Link Prediction Using Learning to Rank Techniques. AAAI 2016: 4226-4227 - [c50]Sougata Chaudhuri, Ambuj Tewari:
Online Learning to Rank with Feedback at the Top. AISTATS 2016: 277-285 - [c49]Harish G. Ramaswamy, Clayton Scott, Ambuj Tewari:
Mixture Proportion Estimation via Kernel Embeddings of Distributions. ICML 2016: 2052-2060 - [c48]Nan Jiang, Satinder Singh, Ambuj Tewari:
On Structural Properties of MDPs that Bound Loss Due to Shallow Planning. IJCAI 2016: 1640-1647 - [c47]Sougata Chaudhuri, Ambuj Tewari:
Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games. NIPS 2016: 2433-2441 - [i29]Kam Chung Wong, Ambuj Tewari, Zifan Li:
Regularized Estimation in High Dimensional Time Series under Mixing Conditions. CoRR abs/1602.04265 (2016) - [i28]Sougata Chaudhuri, Ambuj Tewari:
Online Learning to Rank with Feedback at the Top. CoRR abs/1603.01855 (2016) - [i27]Ambuj Tewari, Sougata Chaudhuri:
Generalization error bounds for learning to rank: Does the length of document lists matter? CoRR abs/1603.01860 (2016) - [i26]Harish G. Ramaswamy, Clayton Scott, Ambuj Tewari:
Mixture Proportion Estimation via Kernel Embedding of Distributions. CoRR abs/1603.02501 (2016) - [i25]Sougata Chaudhuri, Ambuj Tewari:
Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games. CoRR abs/1608.06403 (2016) - [i24]Sougata Chaudhuri, Ambuj Tewari:
Online Learning to Rank with Top-k Feedback. CoRR abs/1608.06408 (2016) - [i23]Zifan Li, Ambuj Tewari:
Sampled Fictitious Play is Hannan Consistent. CoRR abs/1610.01687 (2016) - 2015
- [j7]Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online learning via sequential complexities. J. Mach. Learn. Res. 16: 155-186 (2015) - [c46]Sougata Chaudhuri, Ambuj Tewari:
Online Ranking with Top-1 Feedback. AISTATS 2015 - [c45]Ambuj Tewari, Sougata Chaudhuri:
Generalization error bounds for learning to rank: Does the length of document lists matter? ICML 2015: 315-323 - [c44]Harish G. Ramaswamy, Ambuj Tewari, Shivani Agarwal:
Convex Calibrated Surrogates for Hierarchical Classification. ICML 2015: 1852-1860 - [c43]Prateek Jain, Nagarajan Natarajan, Ambuj Tewari:
Predtron: A Family of Online Algorithms for General Prediction Problems. NIPS 2015: 1009-1017 - [c42]Prateek Jain, Ambuj Tewari:
Alternating Minimization for Regression Problems with Vector-valued Outputs. NIPS 2015: 1126-1134 - [c41]Jacob D. Abernethy, Chansoo Lee, Ambuj Tewari:
Fighting Bandits with a New Kind of Smoothness. NIPS 2015: 2197-2205 - [i22]Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis:
Optimality of Fast Matching Algorithms for Random Networks with Applications to Structural Controllability. CoRR abs/1503.08019 (2015) - [i21]Harish G. Ramaswamy, Ambuj Tewari, Shivani Agarwal:
Consistent Algorithms for Multiclass Classification with a Reject Option. CoRR abs/1505.04137 (2015) - [i20]