
Efficient FirstOrder Contextual Bandits: Prediction, Allocation, and Triangular Discrimination
A recurring theme in statistical learning, online learning, and beyond i...
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Bayesian decisionmaking under misspecified priors with applications to metalearning
Thompson sampling and other Bayesian sequential decisionmaking algorith...
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Investigating the Role of Negatives in Contrastive Representation Learning
Noise contrastive learning is a popular technique for unsupervised repre...
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Gone Fishing: Neural Active Learning with Fisher Embeddings
There is an increasing need for effective active learning algorithms tha...
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Modelfree Representation Learning and Exploration in Lowrank MDPs
The low rank MDP has emerged as an important model for studying represen...
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Learning the Linear Quadratic Regulator from Nonlinear Observations
We introduce a new problem setting for continuous control called the LQR...
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Private Reinforcement Learning with PAC and Regret Guarantees
Motivated by highstakes decisionmaking domains like personalized medic...
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Contrastive learning, multiview redundancy, and linear models
Selfsupervised learning is an empirically successful approach to unsupe...
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SampleEfficient Reinforcement Learning of Undercomplete POMDPs
Partial observability is a common challenge in many reinforcement learni...
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Information Theoretic Regret Bounds for Online Nonlinear Control
This work studies the problem of sequential control in an unknown, nonli...
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Open Problem: Model Selection for Contextual Bandits
In statistical learning, algorithms for model selection allow the learne...
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Provably adaptive reinforcement learning in metric spaces
We study reinforcement learning in continuous state and action spaces en...
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FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs
In order to deal with the curse of dimensionality in reinforcement learn...
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Efficient Contextual Bandits with Continuous Actions
We create a computationally tractable algorithm for contextual bandits w...
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Contrastive estimation reveals topic posterior information to linear models
Contrastive learning is an approach to representation learning that util...
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Corrupted Multidimensional Binary Search: Learning in the Presence of Irrational Agents
Standard gametheoretic formulations for settings like contextual pricin...
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Adaptive Estimator Selection for OffPolicy Evaluation
We develop a generic datadriven method for estimator selection in offp...
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RewardFree Exploration for Reinforcement Learning
Exploration is widely regarded as one of the most challenging aspects of...
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Algebraic and Analytic Approaches for Parameter Learning in Mixture Models
We present two different approaches for parameter learning in several mi...
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Scalable Hierarchical Clustering with Tree Grafting
We introduce Grinch, a new algorithm for largescale, nongreedy hierarc...
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Optimism in Reinforcement Learning with Generalized Linear Function Approximation
We design a new provably efficient algorithm for episodic reinforcement ...
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Kinematic State Abstraction and Provably Efficient RichObservation Reinforcement Learning
We present an algorithm, HOMER, for exploration and reinforcement learni...
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Sample Complexity of Learning Mixtures of Sparse Linear Regressions
In the problem of learning mixtures of linear regressions, the goal is t...
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Robust Dynamic Assortment Optimization in the Presence of Outlier Customers
We consider the dynamic assortment optimization problem under the multin...
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Doubly robust offpolicy evaluation with shrinkage
We design a new family of estimators for offpolicy evaluation in contex...
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Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
We design a new algorithm for batch active learning with deep neural net...
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Model selection for contextual bandits
We introduce the problem of model selection for contextual bandits, wher...
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Trace Reconstruction: Generalized and Parameterized
In the beautifully simpletostate problem of trace reconstruction, the ...
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Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
We study contextual bandit learning with an abstract policy class and co...
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Provably efficient RL with Rich Observations via Latent State Decoding
We study the exploration problem in episodic MDPs with rich observations...
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ModelBased Reinforcement Learning in Contextual Decision Processes
We study the sample complexity of modelbased reinforcement learning in ...
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Contextual bandits with surrogate losses: Margin bounds and efficient algorithms
We introduce a new family of marginbased regret guarantees for adversar...
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Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming
We design a new myopic strategy for a wide class of sequential design of...
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Semiparametric Contextual Bandits
This paper studies semiparametric contextual bandits, a generalization o...
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On Polynomial Time PAC Reinforcement Learning with Rich Observations
We study the computational tractability of provably sampleefficient (PA...
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Disagreementbased combinatorial pure exploration: Efficient algorithms and an analysis with localization
We design new algorithms for the combinatorial pure exploration problem ...
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Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning
Knowledge bases (KB), both automatically and manually constructed, are o...
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Asynchronous Parallel Bayesian Optimisation via Thompson Sampling
We design and analyse variations of the classical Thompson sampling (TS)...
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An Online Hierarchical Algorithm for Extreme Clustering
Many modern clustering methods scale well to a large number of data item...
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Active Learning for CostSensitive Classification
We design an active learning algorithm for costsensitive multiclass cla...
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Contextual Decision Processes with Low Bellman Rank are PACLearnable
This paper studies systematic exploration for reinforcement learning wit...
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Offpolicy evaluation for slate recommendation
This paper studies the evaluation of policies that recommend an ordered ...
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Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains
Highdimensional observations and complex realworld dynamics present ma...
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PAC Reinforcement Learning with Rich Observations
We propose and study a new model for reinforcement learning with rich ob...
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Minimax Structured Normal Means Inference
We provide a unified treatment of a broad class of noisy structure recov...
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Extreme Compressive Sampling for Covariance Estimation
This paper studies the problem of estimating the covariance of a collect...
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Contextual Semibandits via Supervised Learning Oracles
We study an online decision making problem where on each round a learner...
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Learning to Search Better Than Your Teacher
Methods for learning to search for structured prediction typically imita...
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Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations
We propose and analyze estimators for statistical functionals of one or ...
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On Estimating L_2^2 Divergence
We give a comprehensive theoretical characterization of a nonparametric ...
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Akshay Krishnamurthy
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Assistant Professor in the College of Information and Computer Sciences at the University of Massachusetts, Amherst