Search Results for author: Ronny Luss

Found 21 papers, 6 papers with code

Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI

no code implementations22 Jun 2022 Q. Vera Liao, Yunfeng Zhang, Ronny Luss, Finale Doshi-Velez, Amit Dhurandhar

We argue that one way to close the gap is to develop evaluation methods that account for different user requirements in these usage contexts.

Local Explanations for Reinforcement Learning

no code implementations8 Feb 2022 Ronny Luss, Amit Dhurandhar, Miao Liu

Many works in explainable AI have focused on explaining black-box classification models.

reinforcement-learning

Auto-Transfer: Learning to Route Transferrable Representations

1 code implementation2 Feb 2022 Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar

Knowledge transfer between heterogeneous source and target networks and tasks has received a lot of attention in recent times as large amounts of quality labeled data can be difficult to obtain in many applications.

Transfer Learning

Auto-Transfer: Learning to Route Transferable Representations

no code implementations ICLR 2022 Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar

Knowledge transfer between heterogeneous source and target networks and tasks has received a lot of attention in recent times as large amounts of quality labelled data can be difficult to obtain in many applications.

Transfer Learning

Interpreting Reinforcement Policies through Local Behaviors

no code implementations29 Sep 2021 Ronny Luss, Amit Dhurandhar, Miao Liu

Many works in explainable AI have focused on explaining black-box classification models.

Let the CAT out of the bag: Contrastive Attributed explanations for Text

no code implementations16 Sep 2021 Saneem Chemmengath, Amar Prakash Azad, Ronny Luss, Amit Dhurandhar

Contrastive explanations for understanding the behavior of black box models has gained a lot of attention recently as they provide potential for recourse.

Language Modelling

Towards Better Model Understanding with Path-Sufficient Explanations

no code implementations13 Sep 2021 Ronny Luss, Amit Dhurandhar

To overcome these limitations, we propose a novel method called Path-Sufficient Explanations Method (PSEM) that outputs a sequence of sufficient explanations for a given input of strictly decreasing size (or value) -- from original input to a minimally sufficient explanation -- which can be thought to trace the local boundary of the model in a smooth manner, thus providing better intuition about the local model behavior for the specific input.

Explainable artificial intelligence

Leveraging Simple Model Predictions for Enhancing its Performance

no code implementations25 Sep 2019 Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss

Our method also leverages the per sample hardness estimate of the simple model which is not the case with the prior works which primarily consider the complex model's confidences/predictions and is thus conceptually novel.

Enhancing Simple Models by Exploiting What They Already Know

no code implementations ICML 2020 Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss

Our method also leverages the per sample hardness estimate of the simple model which is not the case with the prior works which primarily consider the complex model's confidences/predictions and is thus conceptually novel.

Small Data Image Classification

Leveraging Latent Features for Local Explanations

3 code implementations29 May 2019 Ronny Luss, Pin-Yu Chen, Amit Dhurandhar, Prasanna Sattigeri, Yunfeng Zhang, Karthikeyan Shanmugam, Chun-Chen Tu

As the application of deep neural networks proliferates in numerous areas such as medical imaging, video surveillance, and self driving cars, the need for explaining the decisions of these models has become a hot research topic, both at the global and local level.

General Classification Self-Driving Cars

Stochastic Gradient Descent with Biased but Consistent Gradient Estimators

1 code implementation31 Jul 2018 Jie Chen, Ronny Luss

The theory assumes that one can easily compute an unbiased gradient estimator, which is usually the case due to the sample average nature of empirical risk minimization.

Stochastic Optimization

Improving Simple Models with Confidence Profiles

no code implementations NeurIPS 2018 Amit Dhurandhar, Karthikeyan Shanmugam, Ronny Luss, Peder Olsen

Our transfer method involves a theoretically justified weighting of samples during the training of the simple model using confidence scores of these intermediate layers.

Beyond Backprop: Online Alternating Minimization with Auxiliary Variables

1 code implementation24 Jun 2018 Anna Choromanska, Benjamin Cowen, Sadhana Kumaravel, Ronny Luss, Mattia Rigotti, Irina Rish, Brian Kingsbury, Paolo DiAchille, Viatcheslav Gurev, Ravi Tejwani, Djallel Bouneffouf

Despite significant recent advances in deep neural networks, training them remains a challenge due to the highly non-convex nature of the objective function.

A Formal Framework to Characterize Interpretability of Procedures

no code implementations12 Jul 2017 Amit Dhurandhar, Vijay Iyengar, Ronny Luss, Karthikeyan Shanmugam

We provide a novel notion of what it means to be interpretable, looking past the usual association with human understanding.

TIP: Typifying the Interpretability of Procedures

no code implementations9 Jun 2017 Amit Dhurandhar, Vijay Iyengar, Ronny Luss, Karthikeyan Shanmugam

This leads to the insight that the improvement in the target model is not only a function of the oracle model's performance, but also its relative complexity with respect to the target model.

Knowledge Distillation

Sparse Quantile Huber Regression for Efficient and Robust Estimation

no code implementations19 Feb 2014 Aleksandr Y. Aravkin, Anju Kambadur, Aurelie C. Lozano, Ronny Luss

We consider new formulations and methods for sparse quantile regression in the high-dimensional setting.

Variable Selection

Decomposing Isotonic Regression for Efficiently Solving Large Problems

no code implementations NeurIPS 2010 Ronny Luss, Saharon Rosset, Moni Shahar

A new algorithm for isotonic regression is presented based on recursively partitioning the solution space.

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