Pradeep Ravikumar

research

∙ 03/25/2023

Learning with Explanation Constraints

While supervised learning assumes the presence of labeled data, we may h...

0 Rattana Pukdee, et al. ∙

research

∙ 02/16/2023

Individual Fairness Guarantee in Learning with Censorship

Algorithmic fairness, studying how to make machine learning (ML) algorit...

0 Wenbin Zhang, et al. ∙

research

∙ 10/07/2022

Label Propagation with Weak Supervision

Semi-supervised learning and weakly supervised learning are important pa...

0 Rattana Pukdee, et al. ∙

research

∙ 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...

0 Kevin Bello, et al. ∙

research

∙ 08/31/2022

Concept Gradient: Concept-based Interpretation Without Linear Assumption

Concept-based interpretations of black-box models are often more intuiti...

0 Andrew Bai, et al. ∙

research

∙ 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...

0 Bohdan Kivva, et al. ∙

research

∙ 03/02/2022

Faith-Shap: The Faithful Shapley Interaction Index

Shapley values, which were originally designed to assign attributions to...

0 Che-Ping Tsai, et al. ∙

research

∙ 02/25/2022

Human-Centered Concept Explanations for Neural Networks

Understanding complex machine learning models such as deep neural networ...

0 Chih-Kuan Yeh, et al. ∙

research

∙ 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...

0 Chih-Kuan Yeh, et al. ∙

research

∙ 02/24/2022

First is Better Than Last for Training Data Influence

The ability to identify influential training examples enables us to debu...

0 Chih-Kuan Yeh, et al. ∙

research

∙ 02/18/2022

Masked prediction tasks: a parameter identifiability view

The vast majority of work in self-supervised learning, both theoretical ...

0 Bingbin Liu, et al. ∙

research

∙ 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-...

0 Elan Rosenfeld, et al. ∙

research

∙ 01/28/2022

Understanding Why Generalized Reweighting Does Not Improve Over ERM

Empirical risk minimization (ERM) is known in practice to be non-robust ...

0 Runtian Zhai, et al. ∙

research

∙ 10/26/2021

Boosted CVaR Classification

Many modern machine learning tasks require models with high tail perform...

0 Runtian Zhai, et al. ∙

research

∙ 10/21/2021

Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation

Noise-contrastive estimation (NCE) is a statistically consistent method ...

0 Bingbin Liu, et al. ∙

research

∙ 10/12/2021

FILM: Following Instructions in Language with Modular Methods

Recent methods for embodied instruction following are typically trained ...

1 So Yeon Min, et al. ∙

research

∙ 08/25/2021

Heavy-tailed Streaming Statistical Estimation

We consider the task of heavy-tailed statistical estimation given stream...

0 Che-Ping Tsai, et al. ∙

research

∙ 06/29/2021

Learning latent causal graphs via mixture oracles

We study the problem of reconstructing a causal graphical model from dat...

0 Bohdan Kivva, et al. ∙

research

∙ 06/19/2021

Improving Compositional Generalization in Classification Tasks via Structure Annotations

Compositional generalization is the ability to generalize systematically...

0 Juyong Kim, et al. ∙

research

∙ 06/11/2021

DORO: Distributional and Outlier Robust Optimization

Many machine learning tasks involve subpopulation shift where the testin...

0 Runtian Zhai, et al. ∙

research

∙ 04/15/2021

Iterative Barycenter Flows

The task of mapping two or more distributions to a shared representation...

0 David I. Inouye, et al. ∙

research

∙ 03/03/2021

Contrastive learning of strong-mixing continuous-time stochastic processes

Contrastive learning is a family of self-supervised methods where a mode...

0 Bingbin Liu, et al. ∙

research

∙ 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 ...

0 Elan Rosenfeld, et al. ∙

research

∙ 02/20/2021

On Proximal Policy Optimization's Heavy-tailed Gradients

Modern policy gradient algorithms, notably Proximal Policy Optimization ...

0 Saurabh Garg, et al. ∙

research

∙ 01/01/2021

When Is Generalizable Reinforcement Learning Tractable?

Agents trained by reinforcement learning (RL) often fail to generalize b...

0 Dhruv Malik, et al. ∙

research

∙ 12/19/2020

Fundamental Limits and Tradeoffs in Invariant Representation Learning

Many machine learning applications involve learning representations that...

0 Han Zhao, et al. ∙

research

∙ 10/12/2020

The Risks of Invariant Risk Minimization

Invariant Causal Prediction (Peters et al., 2016) is a technique for out...

0 Elan Rosenfeld, et al. ∙

research

∙ 06/29/2020

Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification

Adversarial robustness has become a fundamental requirement in modern ma...

9 Chen Dan, et al. ∙

research

∙ 06/19/2020

Learning Minimax Estimators via Online Learning

We consider the problem of designing minimax estimators for estimating t...

8 Kartik Gupta, et al. ∙

research

∙ 06/14/2020

Sub-Seasonal Climate Forecasting via Machine Learning: Challenges, Analysis, and Advances

Sub-seasonal climate forecasting (SSF) focuses on predicting key climate...

0 Sijie He, et al. ∙

research

∙ 05/31/2020

Evaluations and Methods for Explanation through Robustness Analysis

Among multiple ways of interpreting a machine learning model, measuring ...

15 Cheng-Yu Hsieh, et al. ∙

research

∙ 05/26/2020

Class-Weighted Classification: Trade-offs and Robust Approaches

We address imbalanced classification, the problem in which a label may h...

2 Ziyu Xu, et al. ∙

research

∙ 04/12/2020

Minimizing FLOPs to Learn Efficient Sparse Representations

Deep representation learning has become one of the most widely adopted a...

20 Biswajit Paria, et al. ∙

research

∙ 02/07/2020

Certified Robustness to Label-Flipping Attacks via Randomized Smoothing

Machine learning algorithms are known to be susceptible to data poisonin...

0 Elan Rosenfeld, et al. ∙

research

∙ 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...

4 Runtian Zhai, et al. ∙

research

∙ 12/12/2019

Game Design for Eliciting Distinguishable Behavior

The ability to inferring latent psychological traits from human behavior...

6 Fan Yang, et al. ∙

research

∙ 12/02/2019

Diagnostic Curves for Black Box Models

In safety-critical applications of machine learning, it is often necessa...

0 David I. Inouye, et al. ∙

research

∙ 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...

0 Chen Dan, et al. ∙

research

∙ 10/17/2019

On Concept-Based Explanations in Deep Neural Networks

Deep neural networks (DNNs) build high-level intelligence on low-level r...

21 Chih-Kuan Yeh, et al. ∙

research

∙ 09/29/2019

Learning Sparse Nonparametric DAGs

We develop a framework for learning sparse nonparametric directed acycli...

18 Xun Zheng, et al. ∙

research

∙ 07/01/2019

A Unified Approach to Robust Mean Estimation

In this paper, we develop connections between two seemingly disparate, b...

0 Adarsh Prasad, et al. ∙

research

∙ 03/19/2019

Adaptive Hard Thresholding for Near-optimal Consistent Robust Regression

We study the problem of robust linear regression with response variable ...

0 Arun Sai Suggala, et al. ∙

research

∙ 01/27/2019

How Sensitive are Sensitivity-Based Explanations?

We propose a simple objective evaluation measure for explanations of a c...

18 Chih-Kuan Yeh, et al. ∙

research

∙ 01/20/2019

Towards Aggregating Weighted Feature Attributions

Current approaches for explaining machine learning models fall into two ...

0 Umang Bhatt, et al. ∙

research

∙ 11/23/2018

Representer Point Selection for Explaining Deep Neural Networks

We propose to explain the predictions of a deep neural network, by point...

0 Chih-Kuan Yeh, et al. ∙

research

∙ 10/30/2018

Word Mover's Embedding: From Word2Vec to Document Embedding

While the celebrated Word2Vec technique yields semantically rich represe...

0 Lingfei Wu, et al. ∙

research

∙ 10/10/2018

Learning Tensor Latent Features

We study the problem of learning latent feature models (LFMs) for tensor...

0 Sung-En Chang, et al. ∙

research

∙ 09/10/2018

Sample Complexity of Nonparametric Semi-Supervised Learning

We study the sample complexity of semi-supervised learning (SSL) and int...

0 Chen Dan, et al. ∙

research

∙ 06/07/2018

On Adversarial Risk and Training

In this work we formally define the notions of adversarial perturbations...

0 Arun Sai Suggala, et al. ∙

research

∙ 06/02/2018

Binary Classification with Karmic, Threshold-Quasi-Concave Metrics

Complex performance measures, beyond the popular measure of accuracy, ar...

0 Bowei Yan, et al. ∙

Pradeep Ravikumar

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