83 citations · 323 across the 16 of their papers we have counts for
19 papers
Probabilistic Counterfactual Inference for Discrete Outcomes in Gaussian-Process Causal Models
Juliette Sinnott, Amir-Hossein Karimi, Mohammad Kohandel
Counterfactual inference in Gaussian-process structural causal models (GP-SCMs) has been developed primarily for continuous endogenous variables, limiting applicability to causal g…
Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces
Ahmad-Reza Ehyaei, Kiarash Mohammadi, Amir-Hossein Karimi +2
As responsible AI gains importance in machine learning algorithms, properties such as fairness, adversarial robustness, and causality have received considerable attention in recent…
A Persian Benchmark for Joint Intent Detection and Slot Filling
Masoud Akbari, Amir Hossein Karimi, Tayyebeh Saeedi +5
Natural Language Understanding (NLU) is important in today's technology as it enables machines to comprehend and process human language, leading to improved human-computer interact…
Robustness Implies Fairness in Causal Algorithmic Recourse
Ahmad-Reza Ehyaei, Amir-Hossein Karimi, Bernhard Schölkopf +1
Algorithmic recourse aims to disclose the inner workings of the black-box decision process in situations where decisions have significant consequences, by providing recommendations…
On the Relationship Between Explanation and Prediction: A Causal View
Amir-Hossein Karimi, Krikamol Muandet, Simon Kornblith +2
Being able to provide explanations for a model's decision has become a central requirement for the development, deployment, and adoption of machine learning models. However, we are…
Dynamic Decision Frequency with Continuous Options
Amirmohammad Karimi, Jun Jin, Jun Luo +3
In classic reinforcement learning algorithms, agents make decisions at discrete and fixed time intervals. The duration between decisions becomes a crucial hyperparameter, as settin…
On the Adversarial Robustness of Causal Algorithmic Recourse
Ricardo Dominguez-Olmedo, Amir-Hossein Karimi, Bernhard Schölkopf
Algorithmic recourse seeks to provide actionable recommendations for individuals to overcome unfavorable classification outcomes from automated decision-making systems. Recourse re…
On the Fairness of Causal Algorithmic Recourse
Julius von Kügelgen, Amir-Hossein Karimi, Umang Bhatt +3
Algorithmic fairness is typically studied from the perspective of predictions. Instead, here we investigate fairness from the perspective of recourse actions suggested to individua…
Scaling Guarantees for Nearest Counterfactual Explanations
Kiarash Mohammadi, Amir-Hossein Karimi, Gilles Barthe +1
Counterfactual explanations (CFE) are being widely used to explain algorithmic decisions, especially in consequential decision-making contexts (e.g., loan approval or pretrial bail…
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf +1
Machine learning is increasingly used to inform decision-making in sensitive situations where decisions have consequential effects on individuals' lives. In these settings, in addi…
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf +1
Recent work has discussed the limitations of counterfactual explanations to recommend actions for algorithmic recourse, and argued for the need of taking causal relationships betwe…
Algorithmic Recourse: from Counterfactual Explanations to Interventions
Amir-Hossein Karimi, Bernhard Schölkopf, Isabel Valera
As machine learning is increasingly used to inform consequential decision-making (e.g., pre-trial bail and loan approval), it becomes important to explain how the system arrived at…
Model-Agnostic Counterfactual Explanations for Consequential Decisions
Amir-Hossein Karimi, Gilles Barthe, Borja Balle +1
Predictive models are being increasingly used to support consequential decision making at the individual level in contexts such as pretrial bail and loan approval. As a result, the…
Deep Variational Sufficient Dimensionality Reduction
Ershad Banijamali, Amir-Hossein Karimi, Ali Ghodsi
We consider the problem of sufficient dimensionality reduction (SDR), where the high-dimensional observation is transformed to a low-dimensional sub-space in which the information…
SRP: Efficient class-aware embedding learning for large-scale data via supervised random projections
Amir-Hossein Karimi, Alexander Wong, Ali Ghodsi
Supervised dimensionality reduction strategies have been of great interest. However, current supervised dimensionality reduction approaches are difficult to scale for situations ch…
JADE: Joint Autoencoders for Dis-Entanglement
Ershad Banijamali, Amir-Hossein Karimi, Alexander Wong +1
The problem of feature disentanglement has been explored in the literature, for the purpose of image and video processing and text analysis. State-of-the-art methods for disentangl…
A Summary Of The Kernel Matrix, And How To Learn It Effectively Using Semidefinite Programming
Amir-Hossein Karimi
Kernel-based learning algorithms are widely used in machine learning for problems that make use of the similarity between object pairs. Such algorithms first embed all data points…
Synthesizing Deep Neural Network Architectures using Biological Synaptic Strength Distributions
A. H. Karimi, M. J. Shafiee, A. Ghodsi +1
In this work, we perform an exploratory study on synthesizing deep neural networks using biological synaptic strength distributions, and the potential influence of different distri…
Key-Value Memory Networks for Directly Reading Documents
Alexander Miller, Adam Fisch, Jesse Dodge +3
Directly reading documents and being able to answer questions from them is an unsolved challenge. To avoid its inherent difficulty, question answering (QA) has been directed toward…