7 citations · 29 across the 42 of their papers we have counts for
9 papers · 1 filter
A Sketch Based Game Theoretic Approach to Detect Anomalous Dense Sub-Communities in Large Data Streams
Prateek Chanda, Aadirupa Saha
Detecting anomalous subgraphs in a dynamic graph in an online or streaming fashion is an important requirement in industrial settings for intrusion detection or denial of service a…
Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability
Aadirupa Saha, Akshay Krishnamurthy
We study the -armed contextual dueling bandit problem, a sequential decision making setting in which the learner uses contextual information to make two decisions, but only obse…
Dueling RL: Reinforcement Learning with Trajectory Preferences
Aldo Pacchiano, Aadirupa Saha, Jonathan Lee
We consider the problem of preference based reinforcement learning (PbRL), where, unlike traditional reinforcement learning, an agent receives feedback only in terms of a 1 bit (0/…
Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits
Aadirupa Saha, Shubham Gupta
We study the problem of \emph{dynamic regret minimization} in -armed Dueling Bandits under non-stationary or time varying preferences. This is an online learning setup where the…
Strategically Efficient Exploration in Competitive Multi-agent Reinforcement Learning
Robert Loftin, Aadirupa Saha, Sam Devlin +1
High sample complexity remains a barrier to the application of reinforcement learning (RL), particularly in multi-agent systems. A large body of work has demonstrated that explorat…
Dueling Bandits with Adversarial Sleeping
Aadirupa Saha, Pierre Gaillard
We introduce the problem of sleeping dueling bandits with stochastic preferences and adversarial availabilities (DB-SPAA). In almost all dueling bandit applications, the decision s…
Pure Exploration with Structured Preference Feedback
Shubham Gupta, Aadirupa Saha, Sumeet Katariya
We consider the problem of pure exploration with subset-wise preference feedback, which contains arms with features. The learner is allowed to query subsets of size and rec…
Optimal Regret Algorithm for Pseudo-1d Bandit Convex Optimization
Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli +1
We study online learning with bandit feedback (i.e. learner has access to only zeroth-order oracle) where cost/reward functions $\f_t$ admit a "pseudo-1d" structure, i.e. $\f_t(\w)…
Confidence-Budget Matching for Sequential Budgeted Learning
Yonathan Efroni, Nadav Merlis, Aadirupa Saha +1
A core element in decision-making under uncertainty is the feedback on the quality of the performed actions. However, in many applications, such feedback is restricted. For example…