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Ambuj Tewari
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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) - [c99]Kihyuk Hong, Yuhang Li, Ambuj Tewari:
A Primal-Dual-Critic Algorithm for Offline Constrained Reinforcement Learning. AISTATS 2024: 280-288 - [c98]Jacob Trauger, Ambuj Tewari:
Sequence Length Independent Norm-Based Generalization Bounds for Transformers. AISTATS 2024: 1405-1413 - [c97]Chinmaya Kausik, Yangyi Lu, Kevin Tan, Maggie Makar, Yixin Wang, Ambuj Tewari:
Offline Policy Evaluation and Optimization Under Confounding. AISTATS 2024: 1459-1467 - [c96]Yash P. Patel, Sahana Rayan, Ambuj Tewari:
Conformal Contextual Robust Optimization. AISTATS 2024: 2485-2493 - [c95]Ananth Raman, Vinod Raman, Unique Subedi, Idan Mehalel, Ambuj Tewari:
Multiclass Online Learnability under Bandit Feedback. ALT 2024: 997-1012 - [c94]Unique Subedi, Vinod Raman, Ambuj Tewari:
Online Infinite-Dimensional Regression: Learning Linear Operators. ALT 2024: 1113-1133 - [c93]Vinod Raman, Unique Subedi, Ananth Raman, Ambuj Tewari:
Apple Tasting: Combinatorial Dimensions and Minimax Rates. COLT 2024: 4358-4380 - [c92]Vinod Raman, Unique Subedi, Ambuj Tewari:
Online Learning with Set-valued Feedback. COLT 2024: 4381-4412 - [c91]Ziping Xu, Zifan Xu, Runxuan Jiang, Peter Stone, Ambuj Tewari:
Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks. ICLR 2024 - [i106]Chinmaya Kausik, Mirco Mutti, Aldo Pacchiano, Ambuj Tewari:
A Framework for Partially Observed Reward-States in RLHF. CoRR abs/2402.03282 (2024) - [i105]Kihyuk Hong, Ambuj Tewari:
A Primal-Dual Algorithm for Offline Constrained Reinforcement Learning with Low-Rank MDPs. CoRR abs/2402.04493 (2024) - [i104]Vinod Raman, Unique Subedi, Ambuj Tewari:
The Complexity of Sequential Prediction in Dynamical Systems. CoRR abs/2402.06614 (2024) - [i103]Eduardo Ochoa Rivera, Ambuj Tewari:
Optimal Thresholding Linear Bandit. CoRR abs/2402.09467 (2024) - [i102]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) - [i101]Vinod Raman, Ambuj Tewari:
Online Classification with Predictions. CoRR abs/2405.14066 (2024) - [i100]Kihyuk Hong, Yufan Zhang, Ambuj Tewari:
Provably Efficient Reinforcement Learning for Infinite-Horizon Average-Reward Linear MDPs. CoRR abs/2405.15050 (2024) - [i99]Vinod Raman, Unique Subedi, Ambuj Tewari:
Smoothed Online Classification can be Harder than Batch Classification. CoRR abs/2405.15424 (2024) - [i98]Yash P. Patel, Sahana Rayan, Ambuj Tewari:
Conformal Robust Control of Linear Systems. CoRR abs/2405.16250 (2024) - [i97]Chinmaya Kausik, Kevin Tan, Ambuj Tewari:
Leveraging Offline Data in Linear Latent Bandits. CoRR abs/2405.17324 (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
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