Search Results for author: Garud Iyengar

Found 18 papers, 2 papers with code

Model-Free Approximate Bayesian Learning for Large-Scale Conversion Funnel Optimization

no code implementations12 Jan 2024 Garud Iyengar, Raghav Singal

We model consumer behavior by a conversion funnel that captures the state of each consumer (e. g., interaction history with the firm) and allows the consumer behavior to vary as a function of both her state and firm's sequential interventions.

Decision Making Marketing +1

Decentralized Finance: Protocols, Risks, and Governance

no code implementations2 Dec 2023 Agostino Capponi, Garud Iyengar, Jay Sethuraman

Financial markets are undergoing an unprecedented transformation.

A Doubly Robust Approach to Sparse Reinforcement Learning

no code implementations23 Oct 2023 Wonyoung Kim, Garud Iyengar, Assaf Zeevi

We propose a new regret minimization algorithm for episodic sparse linear Markov decision process (SMDP) where the state-transition distribution is a linear function of observed features.

reinforcement-learning

Scalable Computation of Causal Bounds

no code implementations4 Aug 2023 Madhumitha Shridharan, Garud Iyengar

We show that this LP can be significantly pruned, allowing us to compute bounds for significantly larger causal inference problems compared to existing techniques.

Causal Inference

Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization

no code implementations16 Jun 2023 Garud Iyengar, Henry Lam, Tianyu Wang

We develop a general bias correction approach, building on what we call Optimizer's Information Criterion (OIC), that directly approximates the first-order bias and does not require solving any additional optimization problems.

Model Selection

Pareto Front Identification with Regret Minimization

no code implementations31 May 2023 Wonyoung Kim, Garud Iyengar, Assaf Zeevi

The sample complexity of our proposed algorithm is $\tilde{O}(d/\Delta^2)$, where $d$ is the dimension of contexts and $\Delta$ is a measure of problem complexity.

Active Learning

Improved Algorithms for Multi-period Multi-class Packing Problems with Bandit Feedback

no code implementations31 Jan 2023 Wonyoung Kim, Garud Iyengar, Assaf Zeevi

We consider the linear contextual multi-class multi-period packing problem (LMMP) where the goal is to pack items such that the total vector of consumption is below a given budget vector and the total value is as large as possible.

Management Multi-Armed Bandits

Hedging Complexity in Generalization via a Parametric Distributionally Robust Optimization Framework

no code implementations3 Dec 2022 Garud Iyengar, Henry Lam, Tianyu Wang

We propose a simple approach in which the distribution of random perturbations is approximated using a parametric family of distributions.

Generalization Bounds Management +2

Multinomial Logit Contextual Bandits: Provable Optimality and Practicality

no code implementations25 Mar 2021 Min-hwan Oh, Garud Iyengar

We propose upper confidence bound based algorithms for this MNL contextual bandit.

Multi-Armed Bandits

Sparsity-Agnostic Lasso Bandit

1 code implementation16 Jul 2020 Min-hwan Oh, Garud Iyengar, Assaf Zeevi

We consider a stochastic contextual bandit problem where the dimension $d$ of the feature vectors is potentially large, however, only a sparse subset of features of cardinality $s_0 \ll d$ affect the reward function.

Glycan processing in the Golgi -- optimal information coding and constraints on cisternal number and enzyme specificity

no code implementations18 May 2020 Alkesh Yadav, Quentin Vagne, Pierre Sens, Garud Iyengar, Madan Rao

In this paper, we quantitatively analyse the tradeoffs between the number of cisternae and the number and specificity of enzymes, in order to synthesize a prescribed target glycan distribution of a certain complexity.

Specificity

Sequential Anomaly Detection using Inverse Reinforcement Learning

no code implementations22 Apr 2020 Min-hwan Oh, Garud Iyengar

In order to construct a reliable anomaly detection method and take into consideration the confidence of the predicted anomaly score, we adopt a Bayesian approach for IRL.

Anomaly Detection Decision Making +2

Directed Exploration in PAC Model-Free Reinforcement Learning

no code implementations31 Aug 2018 Min-hwan Oh, Garud Iyengar

We study an exploration method for model-free RL that generalizes the counter-based exploration bonus methods and takes into account long term exploratory value of actions rather than a single step look-ahead.

Efficient Exploration Q-Learning +2

Robust Implicit Backpropagation

no code implementations7 Aug 2018 Francois Fagan, Garud Iyengar

Arguably the biggest challenge in applying neural networks is tuning the hyperparameters, in particular the learning rate.

Unbiased scalable softmax optimization

no code implementations ICLR 2018 Francois Fagan, Garud Iyengar

Recent neural network and language models rely on softmax distributions with an extremely large number of categories.

An Asynchronous Distributed Proximal Gradient Method for Composite Convex Optimization

no code implementations30 Sep 2014 Necdet Serhat Aybat, Garud Iyengar, Zi Wang

We propose a distributed first-order augmented Lagrangian (DFAL) algorithm to minimize the sum of composite convex functions, where each term in the sum is a private cost function belonging to a node, and only nodes connected by an edge can directly communicate with each other.

Optimization and Control

Fast First-Order Methods for Stable Principal Component Pursuit

no code implementations11 May 2011 Necdet Serhat Aybat, Donald Goldfarb, Garud Iyengar

The stable principal component pursuit (SPCP) problem is a non-smooth convex optimization problem, the solution of which has been shown both in theory and in practice to enable one to recover the low rank and sparse components of a matrix whose elements have been corrupted by Gaussian noise.

Optimization and Control

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