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07/12/2023
Identifiability Guarantees for Causal Disentanglement from Soft Interventions
Causal disentanglement aims to uncover a representation of data using la...
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06/19/2023
Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge
Causal effect estimation from data typically requires assumptions about ...
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06/15/2023
Optimal Best-Arm Identification in Bandits with Access to Offline Data
Learning paradigms based purely on offline data as well as those based s...
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02/15/2023
InfoNCE Loss Provably Learns Cluster-Preserving Representations
The goal of contrasting learning is to learn a representation that prese...
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01/17/2023
Optimal Algorithms for Latent Bandits with Cluster Structure
We consider the problem of latent bandits with cluster structure where t...
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08/26/2022
Causal Bandits for Linear Structural Equation Models
This paper studies the problem of designing an optimal sequence of inter...
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07/14/2022
Causal Graphs Underlying Generative Models: Path to Learning with Limited Data
Training generative models that capture rich semantics of the data and i...
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05/30/2022
PAC Generalization via Invariant Representations
One method for obtaining generalizable solutions to machine learning tas...
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02/08/2022
Fourier Representations for Black-Box Optimization over Categorical Variables
Optimization of real-world black-box functions defined over purely categ...
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02/02/2022
Auto-Transfer: Learning to Route Transferrable Representations
Knowledge transfer between heterogeneous source and target networks and ...
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11/15/2021
Scalable Intervention Target Estimation in Linear Models
This paper considers the problem of estimating the unknown intervention ...
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09/24/2021
AI Explainability 360: Impact and Design
As artificial intelligence and machine learning algorithms become increa...
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07/07/2021
Episodic Bandits with Stochastic Experts
We study a version of the contextual bandit problem where an agent is gi...
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06/24/2021
Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators
In temporal difference (TD) learning, off-policy sampling is known to be...
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06/22/2021
Finding Valid Adjustments under Non-ignorability with Minimal DAG Knowledge
Treatment effect estimation from observational data is a fundamental pro...
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03/13/2021
Treatment Effect Estimation using Invariant Risk Minimization
Inferring causal individual treatment effect (ITE) from observational da...
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03/05/2021
Efficient Encrypted Inference on Ensembles of Decision Trees
Data privacy concerns often prevent the use of cloud-based machine learn...
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02/02/2021
A Lyapunov Theory for Finite-Sample Guarantees of Asynchronous Q-Learning and TD-Learning Variants
This paper develops an unified framework to study finite-sample converge...
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11/02/2020
Stochastic Linear Bandits with Protected Subspace
We study a variant of the stochastic linear bandit problem wherein we op...
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11/01/2020
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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10/30/2020
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective
Recently, invariant risk minimization (IRM) was proposed as a promising ...
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10/28/2020
Linear Regression Games: Convergence Guarantees to Approximate Out-of-Distribution Solutions
Recently, invariant risk minimization (IRM) (Arjovsky et al.) was propos...
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06/10/2020
Fair Data Integration
The use of machine learning (ML) in high-stakes societal decisions has e...
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06/06/2020
Combinatorial Black-Box Optimization with Expert Advice
We consider the problem of black-box function optimization over the bool...
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02/21/2020
A Multi-Channel Neural Graphical Event Model with Negative Evidence
Event datasets are sequences of events of various types occurring irregu...
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02/19/2020
Warm Starting Bandits with Side Information from Confounded Data
We study a variant of the multi-armed bandit problem where side informat...
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02/19/2020
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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02/11/2020
Invariant Risk Minimization Games
The standard risk minimization paradigm of machine learning is brittle w...
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02/03/2020
Finite-Sample Analysis of Stochastic Approximation Using Smooth Convex Envelopes
Stochastic Approximation (SA) is a popular approach for solving fixed po...
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10/28/2019
Differentially Private Distributed Data Summarization under Covariate Shift
We envision AI marketplaces to be platforms where consumers, with very l...
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09/06/2019
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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07/23/2019
Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions
We consider a co-variate shift problem where one has access to several m...
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05/31/2019
Model Agnostic Contrastive Explanations for Structured Data
Recently, a method [7] was proposed to generate contrastive explanations...
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05/30/2019
Leveraging Simple Model Predictions for Enhancing its Performance
There has been recent interest in improving performance of simple models...
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05/29/2019
Generating Contrastive Explanations with Monotonic Attribute Functions
Explaining decisions of deep neural networks is a hot research topic wit...
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03/05/2019
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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07/19/2018
Improving Simple Models with Confidence Profiles
In this paper, we propose a new method called ProfWeight for transferrin...
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06/25/2018
Mimic and Classify : A meta-algorithm for Conditional Independence Testing
Given independent samples generated from the joint distribution p(x,y,z)...
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05/24/2018
Structure Learning from Time Series with False Discovery Control
We consider the Granger causal structure learning problem from time seri...
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05/14/2018
Confidence Scoring Using Whitebox Meta-models with Linear Classifier Probes
We propose a confidence scoring mechanism for multi-layer neural network...
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02/23/2018
Contextual Bandits with Stochastic Experts
We consider the problem of contextual bandits with stochastic experts, w...
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02/21/2018
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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01/23/2018
From Centralized to Decentralized Coded Caching
We consider the problem of designing decentralized schemes for coded cac...
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09/18/2017
Model-Powered Conditional Independence Test
We consider the problem of non-parametric Conditional Independence testi...
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07/12/2017
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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06/09/2017
TIP: Typifying the Interpretability of Procedures
We provide a novel notion of what it means to be interpretable, looking ...
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03/08/2017
Sparse Quadratic Logistic Regression in Sub-quadratic Time
We consider support recovery in the quadratic logistic regression settin...
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01/10/2017
Identifying Best Interventions through Online Importance Sampling
Motivated by applications in computational advertising and systems biolo...
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06/01/2016
Contextual Bandits with Latent Confounders: An NMF Approach
Motivated by online recommendation and advertising systems, we consider ...
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10/30/2015
