
Treatment Effect Estimation using Invariant Risk Minimization
Inferring causal individual treatment effect (ITE) from observational da...
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Efficient Encrypted Inference on Ensembles of Decision Trees
Data privacy concerns often prevent the use of cloudbased machine learn...
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A Lyapunov Theory for FiniteSample Guarantees of Asynchronous QLearning and TDLearning Variants
This paper develops an unified framework to study finitesample converge...
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Stochastic Linear Bandits with Protected Subspace
We study a variant of the stochastic linear bandit problem wherein we op...
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Active Structure Learning of Causal DAGs via Directed Clique Tree
A growing body of work has begun to study intervention design for effici...
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Empirical or Invariant Risk Minimization? A Sample Complexity Perspective
Recently, invariant risk minimization (IRM) was proposed as a promising ...
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Linear Regression Games: Convergence Guarantees to Approximate OutofDistribution Solutions
Recently, invariant risk minimization (IRM) (Arjovsky et al.) was propos...
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Fair Data Integration
The use of machine learning (ML) in highstakes societal decisions has e...
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Combinatorial BlackBox Optimization with Expert Advice
We consider the problem of blackbox function optimization over the bool...
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A MultiChannel Neural Graphical Event Model with Negative Evidence
Event datasets are sequences of events of various types occurring irregu...
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Warm Starting Bandits with Side Information from Confounded Data
We study a variant of the multiarmed bandit problem where side informat...
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Learning Global Transparent Models from Local Contrastive Explanations
There is a rich and growing literature on producing local point wise con...
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Invariant Risk Minimization Games
The standard risk minimization paradigm of machine learning is brittle w...
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FiniteSample Analysis of Stochastic Approximation Using Smooth Convex Envelopes
Stochastic Approximation (SA) is a popular approach for solving fixed po...
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Differentially Private Distributed Data Summarization under Covariate Shift
We envision AI marketplaces to be platforms where consumers, with very l...
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One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques
As artificial intelligence and machine learning algorithms make further ...
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Mix and Match: An Optimistic TreeSearch Approach for Learning Models from Mixture Distributions
We consider a covariate shift problem where one has access to several m...
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Model Agnostic Contrastive Explanations for Structured Data
Recently, a method [7] was proposed to generate contrastive explanations...
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Leveraging Simple Model Predictions for Enhancing its Performance
There has been recent interest in improving performance of simple models...
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Generating Contrastive Explanations with Monotonic Attribute Functions
Explaining decisions of deep neural networks is a hot research topic wit...
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Size of Interventional Markov Equivalence Classes in Random DAG Models
Directed acyclic graph (DAG) models are popular for capturing causal rel...
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Improving Simple Models with Confidence Profiles
In this paper, we propose a new method called ProfWeight for transferrin...
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Mimic and Classify : A metaalgorithm for Conditional Independence Testing
Given independent samples generated from the joint distribution p(x,y,z)...
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Structure Learning from Time Series with False Discovery Control
We consider the Granger causal structure learning problem from time seri...
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Confidence Scoring Using Whitebox Metamodels with Linear Classifier Probes
We propose a confidence scoring mechanism for multilayer neural network...
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Contextual Bandits with Stochastic Experts
We consider the problem of contextual bandits with stochastic experts, w...
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Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
In this paper we propose a novel method that provides contrastive explan...
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From Centralized to Decentralized Coded Caching
We consider the problem of designing decentralized schemes for coded cac...
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ModelPowered Conditional Independence Test
We consider the problem of nonparametric Conditional Independence testi...
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A Formal Framework to Characterize Interpretability of Procedures
We provide a novel notion of what it means to be interpretable, looking ...
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TIP: Typifying the Interpretability of Procedures
We provide a novel notion of what it means to be interpretable, looking ...
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Sparse Quadratic Logistic Regression in Subquadratic Time
We consider support recovery in the quadratic logistic regression settin...
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Identifying Best Interventions through Online Importance Sampling
Motivated by applications in computational advertising and systems biolo...
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Contextual Bandits with Latent Confounders: An NMF Approach
Motivated by online recommendation and advertising systems, we consider ...
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Learning Causal Graphs with Small Interventions
We consider the problem of learning causal networks with interventions, ...
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Karthikeyan Shanmugam
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