25 citations · 177 across the 40 of their papers we have counts for
32 papers · 1 filter
On the Minimax Regret for Linear Bandits in a wide variety of Action Spaces
Debangshu Banerjee, Aditya Gopalan
As noted in the works of \cite{lattimore2020bandit}, it has been mentioned that it is an open problem to characterize the minimax regret of linear bandits in a wide variety of acti…
Actor-Critic based Improper Reinforcement Learning
Mohammadi Zaki, Avinash Mohan, Aditya Gopalan +1
We consider an improper reinforcement learning setting where a learner is given base controllers for an unknown Markov decision process, and wishes to combine them optimally to…
Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference
Debangshu Banerjee, Avishek Ghosh, Sayak Ray Chowdhury +1
We present a non-asymptotic lower bound on the eigenspectrum of the design matrix generated by any linear bandit algorithm with sub-linear regret when the action set has well-behav…
Does DQN Learn?
Aditya Gopalan, Gugan Thoppe
A primary requirement for any reinforcement learning method is that it should produce policies that improve upon the initial guess. In this work, we show that the widely used Deep…
Minimum mean-squared error estimation with bandit feedback
Ayon Ghosh, L. A. Prashanth, Dipayan Sen +1
We consider the problem of sequentially learning to estimate, in the mean squared error (MSE) sense, a Gaussian -vector of unknown covariance by observing only of its en…
Bregman Deviations of Generic Exponential Families
Sayak Ray Chowdhury, Patrick Saux, Odalric-Ambrym Maillard +1
We revisit the method of mixture technique, also known as the Laplace method, to study the concentration phenomenon in generic exponential families. Combining the properties of Bre…
On Slowly-varying Non-stationary Bandits
Ramakrishnan Krishnamurthy, Aditya Gopalan
We consider minimisation of dynamic regret in non-stationary bandits with a slowly varying property. Namely, we assume that arms' rewards are stochastic and independent over time,…
Bandit Quickest Changepoint Detection
Aditya Gopalan, Venkatesh Saligrama, Braghadeesh Lakshminarayanan
Many industrial and security applications employ a suite of sensors for detecting abrupt changes in temporal behavior patterns. These abrupt changes typically manifest locally, ren…
Better than the Best: Gradient-based Improper Reinforcement Learning for Network Scheduling
Mohammani Zaki, Avi Mohan, Aditya Gopalan +1
We consider the problem of scheduling in constrained queueing networks with a view to minimizing packet delay. Modern communication systems are becoming increasingly complex, and a…
Improper Reinforcement Learning with Gradient-based Policy Optimization
Mohammadi Zaki, Avinash Mohan, Aditya Gopalan +1
We consider an improper reinforcement learning setting where a learner is given base controllers for an unknown Markov decision process, and wishes to combine them optimally to…
Stochastic Linear Bandits with Protected Subspace
Advait Parulekar, Soumya Basu, Aditya Gopalan +2
We study a variant of the stochastic linear bandit problem wherein we optimize a linear objective function but rewards are accrued only orthogonal to an unknown subspace (which we…
No-regret Algorithms for Multi-task Bayesian Optimization
Sayak Ray Chowdhury, Aditya Gopalan
We consider multi-objective optimization (MOO) of an unknown vector-valued function in the non-parametric Bayesian optimization (BO) setting, with the aim being to learn points on…
Explicit Best Arm Identification in Linear Bandits Using No-Regret Learners
Mohammadi Zaki, Avi Mohan, Aditya Gopalan
We study the problem of best arm identification in linearly parameterised multi-armed bandits. Given a set of feature vectors a confidence paramet…
Best-item Learning in Random Utility Models with Subset Choices
Aadirupa Saha, Aditya Gopalan
We consider the problem of PAC learning the most valuable item from a pool of items using sequential, adaptively chosen plays of subsets of items, when, upon playing a subs…
Regret Minimization in Stochastic Contextual Dueling Bandits
Aadirupa Saha, Aditya Gopalan
We consider the problem of stochastic -armed dueling bandit in the contextual setting, where at each round the learner is presented with a context set of items, each represe…
Sequential Mode Estimation with Oracle Queries
Dhruti Shah, Tuhinangshu Choudhury, Nikhil Karamchandani +1
We consider the problem of adaptively PAC-learning a probability distribution 's mode by querying an oracle for information about a sequence of i.i.d. samples $X_1, X_…
Towards Optimal and Efficient Best Arm Identification in Linear Bandits
Mohammadi Zaki, Avinash Mohan, Aditya Gopalan
We give a new algorithm for best arm identification in linearly parameterised bandits in the fixed confidence setting. The algorithm generalises the well-known LUCB algorithm of Ka…
On Online Learning in Kernelized Markov Decision Processes
Sayak Ray Chowdhury, Aditya Gopalan
We develop algorithms with low regret for learning episodic Markov decision processes based on kernel approximation techniques. The algorithms are based on both the Upper Confidenc…
On Batch Bayesian Optimization
Sayak Ray Chowdhury, Aditya Gopalan
We present two algorithms for Bayesian optimization in the batch feedback setting, based on Gaussian process upper confidence bound and Thompson sampling approaches, along with fre…
On Adaptivity in Information-constrained Online Learning
Siddharth Mitra, Aditya Gopalan
We study how to adapt to smoothly-varying ('easy') environments in well-known online learning problems where acquiring information is expensive. For the problem of label efficient…
Bayesian Optimization under Heavy-tailed Payoffs
Sayak Ray Chowdhury, Aditya Gopalan
We consider black box optimization of an unknown function in the nonparametric Gaussian process setting when the noise in the observed function values can be heavy tailed. This is…
From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model
Aadirupa Saha, Aditya Gopalan
We consider PAC-learning a good item from -subsetwise feedback information sampled from a Plackett-Luce probability model, with instance-dependent sample complexity performance.…
Combinatorial Bandits with Relative Feedback
Aadirupa Saha, Aditya Gopalan
We consider combinatorial online learning with subset choices when only relative feedback information from subsets is available, instead of bandit or semi-bandit feedback which is…
Active Ranking with Subset-wise Preferences
Aadirupa Saha, Aditya Gopalan
We consider the problem of probably approximately correct (PAC) ranking items by adaptively eliciting subset-wise preference feedback. At each round, the learner chooses a subs…