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06/30/2023
Global Optimality in Bivariate Gradient-based DAG Learning
Recently, a new class of non-convex optimization problems motivated by t...
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06/30/2023
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models
Structural causal models (SCMs) are widely used in various disciplines t...
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06/04/2023
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing
We study the problem of learning causal representations from unknown, la...
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06/01/2023
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation
Good data augmentation is one of the key factors that lead to the empiri...
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05/31/2023
Representer Point Selection for Explaining Regularized High-dimensional Models
We introduce a novel class of sample-based explanations we term high-dim...
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05/26/2023
Optimizing NOTEARS Objectives via Topological Swaps
Recently, an intriguing class of non-convex optimization problems has em...
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03/25/2023
Learning with Explanation Constraints
While supervised learning assumes the presence of labeled data, we may h...
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02/16/2023
Individual Fairness Guarantee in Learning with Censorship
Algorithmic fairness, studying how to make machine learning (ML) algorit...
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10/07/2022
Label Propagation with Weak Supervision
Semi-supervised learning and weakly supervised learning are important pa...
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09/16/2022
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization
The combinatorial problem of learning directed acyclic graphs (DAGs) fro...
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08/31/2022
Concept Gradient: Concept-based Interpretation Without Linear Assumption
Concept-based interpretations of black-box models are often more intuiti...
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06/20/2022
Identifiability of deep generative models under mixture priors without auxiliary information
We prove identifiability of a broad class of deep latent variable models...
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03/02/2022
Faith-Shap: The Faithful Shapley Interaction Index
Shapley values, which were originally designed to assign attributions to...
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02/25/2022
Human-Centered Concept Explanations for Neural Networks
Understanding complex machine learning models such as deep neural networ...
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02/24/2022
Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations
A popular explainable AI (XAI) approach to quantify feature importance o...
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02/24/2022
First is Better Than Last for Training Data Influence
The ability to identify influential training examples enables us to debu...
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02/18/2022
Masked prediction tasks: a parameter identifiability view
The vast majority of work in self-supervised learning, both theoretical ...
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02/14/2022
Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization
A common explanation for the failure of deep networks to generalize out-...
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01/28/2022
Understanding Why Generalized Reweighting Does Not Improve Over ERM
Empirical risk minimization (ERM) is known in practice to be non-robust ...
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10/26/2021
Boosted CVaR Classification
Many modern machine learning tasks require models with high tail perform...
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10/21/2021
Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation
Noise-contrastive estimation (NCE) is a statistically consistent method ...
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10/12/2021
FILM: Following Instructions in Language with Modular Methods
Recent methods for embodied instruction following are typically trained ...
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08/25/2021
Heavy-tailed Streaming Statistical Estimation
We consider the task of heavy-tailed statistical estimation given stream...
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06/29/2021
Learning latent causal graphs via mixture oracles
We study the problem of reconstructing a causal graphical model from dat...
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06/19/2021
Improving Compositional Generalization in Classification Tasks via Structure Annotations
Compositional generalization is the ability to generalize systematically...
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06/11/2021
DORO: Distributional and Outlier Robust Optimization
Many machine learning tasks involve subpopulation shift where the testin...
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04/15/2021
Iterative Barycenter Flows
The task of mapping two or more distributions to a shared representation...
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03/03/2021
Contrastive learning of strong-mixing continuous-time stochastic processes
Contrastive learning is a family of self-supervised methods where a mode...
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02/25/2021
An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization
A popular assumption for out-of-distribution generalization is that the ...
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02/20/2021
On Proximal Policy Optimization's Heavy-tailed Gradients
Modern policy gradient algorithms, notably Proximal Policy Optimization ...
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01/01/2021
When Is Generalizable Reinforcement Learning Tractable?
Agents trained by reinforcement learning (RL) often fail to generalize b...
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12/19/2020
Fundamental Limits and Tradeoffs in Invariant Representation Learning
Many machine learning applications involve learning representations that...
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10/12/2020
The Risks of Invariant Risk Minimization
Invariant Causal Prediction (Peters et al., 2016) is a technique for out...
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06/29/2020
Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification
Adversarial robustness has become a fundamental requirement in modern ma...
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06/19/2020
Learning Minimax Estimators via Online Learning
We consider the problem of designing minimax estimators for estimating t...
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06/14/2020
Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances
Sub-seasonal climate forecasting (SSF) focuses on predicting key climate...
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05/31/2020
Evaluations and Methods for Explanation through Robustness Analysis
Among multiple ways of interpreting a machine learning model, measuring ...
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05/26/2020
Class-Weighted Classification: Trade-offs and Robust Approaches
We address imbalanced classification, the problem in which a label may h...
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04/12/2020
Minimizing FLOPs to Learn Efficient Sparse Representations
Deep representation learning has become one of the most widely adopted a...
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02/07/2020
Certified Robustness to Label-Flipping Attacks via Randomized Smoothing
Machine learning algorithms are known to be susceptible to data poisonin...
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01/08/2020
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius
Adversarial training is one of the most popular ways to learn robust mod...
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12/12/2019
Game Design for Eliciting Distinguishable Behavior
The ability to inferring latent psychological traits from human behavior...
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12/02/2019
Diagnostic Curves for Black Box Models
In safety-critical applications of machine learning, it is often necessa...
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10/30/2019
Optimal Analysis of Subset-Selection Based L_p Low Rank Approximation
We study the low rank approximation problem of any given matrix A over R...
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10/17/2019
On Concept-Based Explanations in Deep Neural Networks
Deep neural networks (DNNs) build high-level intelligence on low-level r...
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09/29/2019
Learning Sparse Nonparametric DAGs
We develop a framework for learning sparse nonparametric directed acycli...
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07/01/2019
A Unified Approach to Robust Mean Estimation
In this paper, we develop connections between two seemingly disparate, b...
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03/19/2019
Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression
We study the problem of robust linear regression with response variable ...
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01/27/2019
How Sensitive are Sensitivity-Based Explanations?
We propose a simple objective evaluation measure for explanations of a c...
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01/20/2019
