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История изменения показателей | Авторизация |
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Alekh Agarwal
alekh, alekh agarwal, Alekh, Alekh Agarwal
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Внешние ссылки главной страницы ( 84 ) | |
cs.berkeley.edu/~bartlett | Peter Bartlett |
cs.berkeley.edu/~wainwrig | Martin Wainwright |
rltheorybook.github.io | monograph |
arxiv.org/abs/2102.07035 | Model-free Representation Learning and Exploration in Low-rank MDPs |
arxiv.org/abs/2003.12880 | Federated Residual Learning |
arxiv.org/abs/1606.03966 | A Multiworld Testing Decision Service |
ds.microsoft.com/ | here |
microsoft.com/en-us/research/project/multi-world-testing-mwt... | here |
arxiv.org/abs/1708.01799 | Efficient Contextual Bandits in Non-stationary Worlds |
arxiv.org/pdf/1908.00261 | On the Theory of Policy Gradient Methods: Optimality, Approximation, and Distribution Shift |
jmlr.org/papers/volume20/17-681/17-681.pdf | Active Learning for Cost-Sensitive Classification |
arxiv.org/pdf/1310.7991v1 | Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization |
arxiv.org/pdf/1309.1952v1 | Exact Recovery of Sparsely Used Overcomplete Dictionaries |
arxiv.org/abs/1110.4198 | A Reliable Effective Terascale Linear Learning System |
arxiv.org/abs/1110.2529 | The Generalization Ability of Online Algorithms for Dependent Data |
arxiv.org/abs/1107.1744 | Stochastic convex optimization with bandit feedback |
arxiv.org/abs/1104.4824 | Fast global convergence of gradient methods for high-dimensional statistical recovery |
arxiv.org/abs/1102.4807 | Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions |
scholar.google.com/citations?user=9nnDvooAAAAJ&hl=en | Google Scholar |
arxiv.org/abs/2203.08248 | Non-Linear Reinforcement Learning in Large Action Spaces: Structural Conditions and Sample-efficiency of Posterior Sampling |
arxiv.org/abs/2202.05436 | Minimax Regret Optimization for Robust Machine Learning under Distribution Shift |
arxiv.org/abs/2202.02446 | Adversarially Trained Actor Critic for Offline Reinforcement Learning |
arxiv.org/abs/2202.00063 | Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach |
arxiv.org/abs/2110.08847 | Provable RL with Exogenous Distractors via Multistep Inverse Dynamics |
arxiv.org/abs/2106.06926 | Bellman-consistent Pessimism for Offline Reinforcement Learning |
arxiv.org/abs/2103.11559 | Provably Correct Optimization and Exploration with Non-linear Policies |
arxiv.org/abs/2103.12923 | Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation |
arxiv.org/abs/2103.10620 | Towards a Dimension-Free Understanding of Adaptive Linear Control |
arxiv.org/abs/2007.08459 | PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning |
arxiv.org/abs/2007.08202 | Provably Good Batch Reinforcement Learning Without Great Exploration |
arxiv.org/abs/2007.00795 | In NeurIPS 2020 |
arxiv.org/abs/2006.12136 | In NeurIPS 2020 |
arxiv.org/abs/2006.10814 | FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs |
arxiv.org/abs/2003.01922 | In COLT 2020 |
arxiv.org/pdf/1906.03804 | On the Optimality of Sparse Model-Based Planning for Markov Decision Processes |
arxiv.org/abs/1906.03671 | Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds |
arxiv.org/pdf/1905.05179 | Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representa... |
arxiv.org/pdf/1904.08473 | Off-Policy Policy Gradient with State Distribution Correction |
arxiv.org/pdf/1905.12843 | Fair Regression: Quantitative Definitions and Reduction-based Algorithms |
arxiv.org/abs/1901.09018 | Provably efficient RL with Rich Observations via Latent State Decoding |
arxiv.org/abs/1901.00301 | Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback |
arxiv.org/abs/1811.08540 | Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches |
arxiv.org/abs/1803.00606 | On Polynomial Time PAC Reinforcement Learning with Rich Observations |
arxiv.org/abs/1803.02453 | A Reductions Approach to Fair Classification |
arxiv.org/abs/1803.01088 | Practical Contextual Bandits with Regression Oracles |
arxiv.org/abs/1803.00590 | Hierarchical Imitation and Reinfocement Learning |
arxiv.org/abs/1605.04812 | Off-policy evaluation for slate recommendation |
arxiv.org/abs/1612.06246 | Corralling a Band of Bandit Algorithms |
arxiv.org/abs/1703.01014 | Active Learning for Cost-Sensitive Classification |
arxiv.org/abs/1610.09512 | Contextual Decision Processes with Low Bellman Rank are PAC-Learnable |
arxiv.org/abs/1612.01205 | Optimal and Adaptive Off-policy Evaluation in Contextual Bandits |
arxiv.org/abs/1602.02722 | Contextual-MDPs for PAC-Reinforcement Learning with Rich Observations |
arxiv.org/abs/1602.02202 | Efficient Second Order Online Learning by Sketching |
arxiv.org/abs/1502.05890 | Efficient Contextual Semi-Bandit Learning |
arxiv.org/abs/1507.00407 | Fast Convergence of Regularized Learning in Games |
arxiv.org/abs/1506.08669 | Efficient and Parsimonious Agnostic Active Learning |
arxiv.org/abs/1502.02206 | Learning to Search Better Than Your Teacher |
arxiv.org/abs/1410.0723 | A Lower Bound for the Optimization of Finite Sums |
arxiv.org/pdf/1410.0440 | Scalable Nonlinear Learning with Adaptive Polynomial Expansions |
arxiv.org/abs/1402.0555 | Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits |
arxiv.org/pdf/1310.1949v2 | Least Squares Revisited: Scalable Approaches for Multi-class Prediction |
arxiv.org/abs/1207.4421 | Long version |
arxiv.org/abs/1202.1334 | Contextual Bandit Learning with Predictable Rewards |
arxiv.org/abs/1104.5525 | Long version |
arxiv.org/abs/1105.4681 | Ergodic Subgradient Descent |
eecs.berkeley.edu/~arostami/papers/corrupted_features.pdf | Learning with Missing Features |
jmlr.csail.mit.edu/proceedings/papers/v19/agarwal11a/agarwal... | Oracle inequalities for computationally budgeted model selection |
arxiv.org/abs/1208.0129 | Long version |
arxiv.org/abs/0903.5328 | A Stochastic View of Optimal Regret through Minimax Duality |
books.nips.cc/papers/files/nips20/NIPS2007_0780.pdf | An Analysis of Inference with the Universum |
nips.cc/ | NIPS 2007 |
cse.iitb.ac.in/~soumen/doc/netrank | Learning to Rank Networked Entities |
oregonstate.edu/conferences/icml2007/ | ICML 2007 |
ecmlpkdd2006.org/ | ECML/PKDD 2006 |
kdd2006.com/ | SIGKDD 2006 |
courses.cs.washington.edu/courses/cse599m/19sp/ | CSE 599: Reinforcement Learning and Bandits |
homes.cs.washington.edu/~sham/ | Sham Kakade |
microsoft.com/en-us/research/people/slivkins/ | Alex Slivkins |
neurips.cc/Conferences/2022 | NeurIPS 2022 |
aistats.org/ | AISTATS 2016. |
opt-ml.org/ | Optimization for Machine Learning |
opt.kyb.tuebingen.mpg.de/index.html | Optimization for Machine Learning |
sites.google.come/site/costnips | Computational Trade-offs in Statistical Learning |
lccc.eecs.berkeley.edu | Learning on Cores, Clusters and Clouds |
Внутренние ссылки главной страницы ( 16 ) | |
thesismain.pdf | Computational Trade-offs in Statistical Learning |
DuchiAgJoJo12.pdf | Ergodic Mirror Descent |
AOS1000.pdf | (Annals formatted version) |
CameraReady_IEEE.pdf | Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization |
dist_notes_ieee.pdf | Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling |
ravikumar10b.pdf | Message-passing for graph structured linear programs: Proximal projections, convergence and rounding schemes |
dict-learning-colt.pdf | Learning sparsely used overcomplete dictionaries |
manager.pdf | Robust Multi-Objective Learning with Mentor Feedback |
multiclass.pdf | Selective sampling algorithms for cost-sensitive multiclass prediction |
distopt_nips.pdf | DIStributed Dual Averaging In Networks |
sparseopt_nips.pdf | Convergence rates of gradient methods for high-dimensional statistical recovery |
bandits-colt.pdf | Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback |
300_paper.pdf | Optimal Allocation Strategies for the Dark Pool Problem |
1005_paper.pdf | Information-theoretic lower bounds on the oracle complexity of convex optimization |
I_prox08_tech.pdf | Message-passing for graph structured linear programs: Proximal projections, convergence and rounding schemes |
alekhagarwal.net/bandits_and_rl/ | Bandits and Reinforcement Learning |
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