default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDAmbuj Tewari
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
[j25]Aditya Modi, Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis:
Joint learning of linear time-invariant dynamical systems. Autom. 164: 111635 (2024)- [c101]Kihyuk Hong, Yuhang Li, Ambuj Tewari:
A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning. AISTATS 2024: 280-288 - [c100]Jacob Trauger, Ambuj Tewari:
Sequence Length Independent Norm-Based Generalization Bounds for Transformers. AISTATS 2024: 1405-1413 - [c99]Chinmaya Kausik, Yangyi Lu, Kevin Tan
, Maggie Makar, Yixin Wang, Ambuj Tewari:
Offline Policy Evaluation and Optimization Under Confounding. AISTATS 2024: 1459-1467 - [c98]Yash P. Patel, Sahana Rayan, Ambuj Tewari:
Conformal Contextual Robust Optimization. AISTATS 2024: 2485-2493 - [c97]Ananth Raman, Vinod Raman, Unique Subedi, Idan Mehalel, Ambuj Tewari:
Multiclass Online Learnability under Bandit Feedback. ALT 2024: 997-1012 - [c96]Unique Subedi, Vinod Raman, Ambuj Tewari:
Online Infinite-Dimensional Regression: Learning Linear Operators. ALT 2024: 1113-1133 - [c95]Vinod Raman, Unique Subedi, Ananth Raman, Ambuj Tewari:
Apple Tasting: Combinatorial Dimensions and Minimax Rates. COLT 2024: 4358-4380 - [c94]Vinod Raman, Unique Subedi, Ambuj Tewari:
Online Learning with Set-valued Feedback. COLT 2024: 4381-4412 - [c93]Ziping Xu, Zifan Xu, Runxuan Jiang, Peter Stone, Ambuj Tewari:
Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks. ICLR 2024 - [c92]Kihyuk Hong, Ambuj Tewari:
A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Linear MDPs. ICML 2024 - [c91]Yash P. Patel, Declan McNamara, Jackson Loper, Jeffrey Regier, Ambuj Tewari:
Variational Inference with Coverage Guarantees in Simulation-Based Inference. ICML 2024 - [i107]Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano, Ambuj Tewari:
A Framework for Partially Observed Reward-States in RLHF. CoRR abs/2402.03282 (2024) - [i106]Kihyuk Hong, Ambuj Tewari:
A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Low-Rank MDPs. CoRR abs/2402.04493 (2024) - [i105]Vinod Raman, Unique Subedi, Ambuj Tewari:
The Complexity of Sequential Prediction in Dynamical Systems. CoRR abs/2402.06614 (2024) - [i104]Eduardo Ochoa Rivera, Ambuj Tewari:
Optimal Thresholding Linear Bandit. CoRR abs/2402.09467 (2024) - [i103]Ziping Xu, Zifan Xu, Runxuan Jiang, Peter Stone, Ambuj Tewari:
Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks. CoRR abs/2403.01636 (2024) - [i102]Vinod Raman, Ambuj Tewari:
Online Classification with Predictions. CoRR abs/2405.14066 (2024) - [i101]Kihyuk Hong, Yufan Zhang, Ambuj Tewari:
Provably Efficient Reinforcement Learning for Infinite-Horizon Average-Reward Linear MDPs. CoRR abs/2405.15050 (2024) - [i100]Vinod Raman, Unique Subedi, Ambuj Tewari:
Smoothed Online Classification can be Harder than Batch Classification. CoRR abs/2405.15424 (2024) - [i99]Yash P. Patel, Sahana Rayan, Ambuj Tewari:
Conformal Robust Control of Linear Systems. CoRR abs/2405.16250 (2024) - [i98]Chinmaya Kausik, Kevin Tan, Ambuj Tewari:
Leveraging Offline Data in Linear Latent Bandits. CoRR abs/2405.17324 (2024) - [i97]Unique Subedi, Ambuj Tewari:
Error Bounds for Learning Fourier Linear Operators. CoRR abs/2408.09004 (2024) - 2023
- [j24]Eunjae Shim, Ambuj Tewari, Tim Cernak, Paul M. Zimmerman:
Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit. J. Chem. Inf. Model. 63(12): 3659-3668 (2023) - [j23]Othman El Balghiti, Adam N. Elmachtoub, Paul Grigas, Ambuj Tewari:
Generalization Bounds in the Predict-Then-Optimize Framework. Math. Oper. Res. 48(4): 2043-2065 (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) - [c90]Kihyuk Hong, Yuhang Li, Ambuj Tewari:
An Optimization-based Algorithm for Non-stationary Kernel Bandits without Prior Knowledge. AISTATS 2023: 3048-3085 - [c89]Steve Hanneke, Shay Moran, Vinod Raman, Unique Subedi, Ambuj Tewari:
Multiclass Online Learning and Uniform Convergence. COLT 2023: 5682-5696 - [c88]Sunrit Chakraborty, Saptarshi Roy, Ambuj Tewari:
Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits. ICML 2023: 3979-4008 - [c87]Chinmaya Kausik, Kevin Tan, Ambuj Tewari:
Learning Mixtures of Markov Chains and MDPs. ICML 2023: 15970-16017 - [c86]Vinod Raman, Unique Subedi, Ambuj Tewari:
On the Learnability of Multilabel Ranking. NeurIPS 2023 - [c85]Vinod Raman, Unique Subedi, Ambuj Tewari:
On Proper Learnability between Average- and Worst-case Robustness. NeurIPS 2023 - [c84]Gautam Chandrasekaran, Ambuj Tewari:
Learning in online MDPs: is there a price for handling the communicating case? UAI 2023: 293-302 - [i96]Vinod Raman, Unique Subedi, Ambuj Tewari:
A Characterization of Multilabel Learnability. CoRR abs/2301.02729 (2023) - [i95]Gang Qiao, Ambuj Tewari:
An Asymptotically Optimal Algorithm for the One-Dimensional Convex Hull Feasibility Problem. CoRR abs/2302.02033 (2023) - [i94]Preetham Mohan, Ambuj Tewari:
Quantum Learning Theory Beyond Batch Binary Classification. CoRR abs/2302.07409 (2023) - [i93]Vinod Raman, Unique Subedi, Ambuj Tewari:
A Characterization of Online Multiclass Learnability. CoRR abs/2303.17716 (2023) - [i92]Vinod Raman, Unique Subedi, Ambuj Tewari:
On the Learnability of Multilabel Ranking. CoRR abs/2304.03337 (2023) - [i91]Yash P. Patel, Declan McNamara, Jackson Loper, Jeffrey Regier, Ambuj Tewari:
Variational Inference with Coverage Guarantees. CoRR abs/2305.14275 (2023) - [i90]Vinod Raman, Unique Subedi, Ambuj Tewari:
Online Learning with Set-Valued Feedback. CoRR abs/2306.06247 (2023) - [i89]Kihyuk Hong, Yuhang Li, Ambuj Tewari:
A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning. CoRR abs/2306.07818 (2023) - [i88]Vinod Raman, Unique Subedi, Ambuj Tewari:
A Combinatorial Characterization of Online Learning Games with Bounded Losses. CoRR abs/2307.03816 (2023) - [i87]Ananth Raman, Vinod Raman, Unique Subedi, Ambuj Tewari:
Multiclass Online Learnability under Bandit Feedback. CoRR abs/2308.04620 (2023) - [i86]Mingyuan Zhang, Ambuj Tewari:
On the Minimax Regret in Online Ranking with Top-k Feedback. CoRR abs/2309.02425 (2023) - [i85]Vinod Raman, Unique Subedi, Ambuj Tewari:
Online Infinite-Dimensional Regression: Learning Linear Operators. CoRR abs/2309.06548 (2023) - [i84]Saptarshi Roy, Ambuj Tewari:
On the Computational Complexity of Private High-dimensional Model Selection via the Exponential Mechanism. CoRR abs/2310.07852 (2023) - [i83]Yash P. Patel, Sahana Rayan, Ambuj Tewari:
Conformal Contextual Robust Optimization. CoRR abs/2310.10003 (2023) - [i82]Jacob Trauger, Ambuj Tewari:
Sequence Length Independent Norm-Based Generalization Bounds for Transformers. CoRR abs/2310.13088 (2023) - [i81]Vinod Raman, Unique Subedi, Ananth Raman, Ambuj Tewari:
Revisiting the Learnability of Apple Tasting. CoRR abs/2310.19064 (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]Sougata Chaudhuri, Ambuj Tewari:
Perceptron like Algorithms for Online Learning to Rank. CoRR abs/1508.00842 (2015) - [i19]Jacob D. Abernethy, Chansoo Lee, Ambuj Tewari:
Fighting Bandits with a New Kind of Smoothness. CoRR abs/1512.04152 (2015) - 2014
- [j6]Kai-Yang Chiang, Cho-Jui Hsieh, Nagarajan Natarajan, Inderjit S. Dhillon, Ambuj Tewari:
Prediction and clustering in signed networks: a local to global perspective. J. Mach. Learn. Res. 15(1): 1177-1213 (2014) - [c40]Jacob D. Abernethy, Chansoo Lee, Abhinav Sinha, Ambuj Tewari:
Online Linear Optimization via Smoothing. COLT 2014: 807-823 - [c39]Prateek Jain, Ambuj Tewari, Purushottam Kar:
On Iterative Hard Thresholding Methods for High-dimensional M-Estimation. NIPS 2014: 685-693 - [i18]Sougata Chaudhuri, Ambuj Tewari:
Perceptron-like Algorithms and Generalization Bounds for Learning to Rank. CoRR abs/1405.0591 (2014) - [i17]Jacob D. Abernethy, Chansoo Lee, Abhinav Sinha, Ambuj Tewari:
Online Linear Optimization via Smoothing. CoRR abs/1405.6076 (2014) - [i16]Sougata Chaudhuri, Ambuj Tewari:
Online Ranking with Top-1 Feedback. CoRR abs/1410.1103 (2014) - [i15]Prateek Jain, Ambuj Tewari, Purushottam Kar:
On Iterative Hard Thresholding Methods for High-dimensional M-Estimation. CoRR abs/1410.5137 (2014) - 2013
- [j5]Ankan Saha, Ambuj Tewari:
On the Nonasymptotic Convergence of Cyclic Coordinate Descent Methods. SIAM J. Optim. 23(1): 576-601 (2013) - [c38]Eunho Yang, Ambuj Tewari, Pradeep Ravikumar:
On Robust Estimation of High Dimensional Generalized Linear Models. IJCAI 2013: 1834-1840 - [c37]Nagarajan Natarajan, Inderjit S. Dhillon, Pradeep Ravikumar, Ambuj Tewari:
Learning with Noisy Labels. NIPS 2013: 1196-1204 - [c36]Harish G. Ramaswamy, Shivani Agarwal, Ambuj Tewari:
Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses. NIPS 2013: 1475-1483 - [i14]Kai-Yang Chiang, Cho-Jui Hsieh, Nagarajan Natarajan, Ambuj Tewari, Inderjit S. Dhillon:
Prediction and Clustering in Signed Networks: A Local to Global Perspective. CoRR abs/1302.5145 (2013) - 2012
- [j4]Sham M. Kakade, Shai Shalev-Shwartz, Ambuj Tewari:
Regularization Techniques for Learning with Matrices. J. Mach. Learn. Res. 13: 1865-1890 (2012) - [c35]Ofer Dekel, Ambuj Tewari, Raman Arora:
Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret. ICML 2012 - [c34]Shivaram Kalyanakrishnan, Ambuj Tewari, Peter Auer, Peter Stone:
PAC Subset Selection in Stochastic Multi-armed Bandits. ICML 2012 - [c33]Chad Scherrer, Mahantesh Halappanavar, Ambuj Tewari, David Haglin:
Scaling Up Coordinate Descent Algorithms for Large ℓ1 Regularization Problems. ICML 2012 - [c32]Chad Scherrer, Ambuj Tewari, Mahantesh Halappanavar, David Haglin:
Feature Clustering for Accelerating Parallel Coordinate Descent. NIPS 2012: 28-36 - [c31]Shilpa Shukla, Matthew Lease, Ambuj Tewari:
Parallelizing ListNet training using spark. SIGIR 2012: 1127-1128 - [c30]Raman Arora, Ofer Dekel, Ambuj Tewari:
Deterministic MDPs with Adversarial Rewards and Bandit Feedback. UAI 2012: 93-101 - [c29]Eunho Yang, Ambuj Tewari, Pradeep Ravikumar:
Perturbation based Large Margin Approach for Ranking. AISTATS 2012: 1358-1366 - [i13]Peter L. Bartlett, Ambuj Tewari:
REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs. CoRR abs/1205.2661 (2012) - [i12]Raman Arora, Ofer Dekel, Ambuj Tewari:
Deterministic MDPs with Adversarial Rewards and Bandit Feedback. CoRR abs/1210.4843 (2012) - [i11]Ankan Saha, Prateek Jain, Ambuj Tewari:
The Interplay Between Stability and Regret in Online Learning. CoRR abs/1211.6158 (2012) - [i10]Chad Scherrer, Ambuj Tewari, Mahantesh Halappanavar, David J. Haglin:
Feature Clustering for Accelerating Parallel Coordinate Descent. CoRR abs/1212.4174 (2012) - 2011
- [j3]Shai Shalev-Shwartz, Ambuj Tewari:
Stochastic Methods for l1-regularized Loss Minimization. J. Mach. Learn. Res. 12: 1865-1892 (2011) - [c28]Kai-Yang Chiang, Nagarajan Natarajan, Ambuj Tewari, Inderjit S. Dhillon:
Exploiting longer cycles for link prediction in signed networks. CIKM 2011: 1157-1162 - [c27]Ambuj Tewari, Pradeep Ravikumar, Inderjit S. Dhillon:
Greedy Algorithms for Structurally Constrained High Dimensional Problems. NIPS 2011: 882-890 - [c26]Prateek Jain, Ambuj Tewari, Inderjit S. Dhillon:
Orthogonal Matching Pursuit with Replacement. NIPS 2011: 1215-1223 - [c25]Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online Learning: Stochastic, Constrained, and Smoothed Adversaries. NIPS 2011: 1764-1772 - [c24]Inderjit S. Dhillon, Pradeep Ravikumar, Ambuj Tewari:
Nearest Neighbor based Greedy Coordinate Descent. NIPS 2011: 2160-2168 - [c23]Nati Srebro, Karthik Sridharan, Ambuj Tewari:
On the Universality of Online Mirror Descent. NIPS 2011: 2645-2653 - [c22]Dean P. Foster, Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Complexity-Based Approach to Calibration with Checking Rules. COLT 2011: 293-314 - [c21]Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online Learning: Beyond Regret. COLT 2011: 559-594 - [c20]Pradeep Ravikumar, Ambuj Tewari, Eunho Yang:
On NDCG Consistency of Listwise Ranking Methods. AISTATS 2011: 618-626 - [c19]Ankan Saha, Ambuj Tewari:
Improved Regret Guarantees for Online Smooth Convex Optimization with Bandit Feedback. AISTATS 2011: 636-642 - [i9]Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online Learning: Stochastic and Constrained Adversaries. CoRR abs/1104.5070 (2011) - [i8]Prateek Jain, Ambuj Tewari, Inderjit S. Dhillon:
Orthogonal Matching Pursuit with Replacement. CoRR abs/1106.2774 (2011) - [i7]Nathan Srebro, Karthik Sridharan, Ambuj Tewari:
On the Universality of Online Mirror Descent. CoRR abs/1107.4080 (2011) - 2010
- [c18]Karthik Sridharan, Ambuj Tewari:
Convex Games in Banach Spaces. COLT 2010: 1-13 - [c17]John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, Ambuj Tewari:
Composite Objective Mirror Descent. COLT 2010: 14-26 - [c16]Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online Learning: Random Averages, Combinatorial Parameters, and Learnability. NIPS 2010: 1984-1992 - [c15]Nathan Srebro, Karthik Sridharan, Ambuj Tewari:
Smoothness, Low Noise and Fast Rates. NIPS 2010: 2199-2207 - [c14]Sham M. Kakade, Ohad Shamir, Karthik Sindharan, Ambuj Tewari:
Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity. AISTATS 2010: 381-388 - [i6]Ankan Saha, Ambuj Tewari:
On the Finite Time Convergence of Cyclic Coordinate Descent Methods. CoRR abs/1005.2146 (2010) - [i5]Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online Learning: Random Averages, Combinatorial Parameters, and Learnability. CoRR abs/1006.1138 (2010) - [i4]Nathan Srebro, Karthik Sridharan, Ambuj Tewari:
Smoothness, Low-Noise and Fast Rates. CoRR abs/1009.3896 (2010) - [i3]Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari:
Online Learning: Beyond Regret. CoRR abs/1011.3168 (2010)
2000 – 2009
- 2009
- [c13]Shai Shalev-Shwartz, Ambuj Tewari:
Stochastic methods for l1 regularized loss minimization. ICML 2009: 929-936 - [c12]Peter L. Bartlett, Ambuj Tewari:
REGAL: A Regularization based Algorithm for Reinforcement Learning in Weakly Communicating MDPs. UAI 2009: 35-42 - [i2]Sham M. Kakade, Shai Shalev-Shwartz, Ambuj Tewari:
Applications of strong convexity--strong smoothness duality to learning with matrices. CoRR abs/0910.0610 (2009) - [i1]Sham M. Kakade, Ohad Shamir, Karthik Sridharan, Ambuj Tewari:
Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity. CoRR abs/0911.0054 (2009) - 2008
- [c11]Peter L. Bartlett, Varsha Dani, Thomas P. Hayes, Sham M. Kakade, Alexander Rakhlin, Ambuj Tewari:
High-Probability Regret Bounds for Bandit Online Linear Optimization. COLT 2008: 335-342 - [c10]Jacob D. Abernethy, Peter L. Bartlett, Alexander Rakhlin, Ambuj Tewari:
Optimal Stragies and Minimax Lower Bounds for Online Convex Games. COLT 2008: 415-424 - [c9]Sham M. Kakade, Shai Shalev-Shwartz, Ambuj Tewari:
Efficient bandit algorithms for online multiclass prediction. ICML 2008: 440-447 - [c8]Sham M. Kakade, Karthik Sridharan, Ambuj Tewari:
On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization. NIPS 2008: 793-800 - [c7]Sham M. Kakade, Ambuj Tewari:
On the Generalization Ability of Online Strongly Convex Programming Algorithms. NIPS 2008: 801-808 - 2007
- [j2]Peter L. Bartlett, Ambuj Tewari:
Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results. J. Mach. Learn. Res. 8: 775-790 (2007) - [j1]Ambuj Tewari, Peter L. Bartlett:
On the Consistency of Multiclass Classification Methods. J. Mach. Learn. Res. 8: 1007-1025 (2007) - [c6]Ambuj Tewari, Peter L. Bartlett:
Bounded Parameter Markov Decision Processes with Average Reward Criterion. COLT 2007: 263-277 - [c5]Ambuj Tewari, Peter L. Bartlett:
Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs. NIPS 2007: 1505-1512 - 2006
- [c4]Peter L. Bartlett, Ambuj Tewari:
Sample Complexity of Policy Search with Known Dynamics. NIPS 2006: 97-104 - 2005
- [c3]Ambuj Tewari, Peter L. Bartlett:
On the Consistency of Multiclass Classification Methods. COLT 2005: 143-157 - 2004
- [c2]Peter L. Bartlett, Ambuj Tewari:
Sparseness Versus Estimating Conditional Probabilities: Some Asymptotic Results. COLT 2004: 564-578 - 2002
- [c1]Ambuj Tewari, Utkarsh Srivastava, P. Gupta:
A Parallel DFA Minimization Algorithm. HiPC 2002: 34-40
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-07 21:10 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
