33 citations · 166 across the 51 of their papers we have counts for
10 papers · 1 filter
Learning the Error Patterns of Language Models
Jinwoo Kim, Taylor Berg-KirkPatrick, Loris D'Antoni
When generating outputs for domains with specific validity constraints (e.g., a program should compile), LLMs often fail in a small number of focused ways: for example, by using Py…
Manifold-Guided Attention Steering
Ian Li, Kapilesh Guruprasad, Raunak Sengupta +3
Large language models frequently produce errors in reasoning tasks despite possessing the underlying knowledge required for correct reasoning. One possible approach to improve reas…
Continuous Diffusion Models Can Obey Formal Syntax
Jinwoo Kim, Taylor Berg-Kirkpatrick, Loris D'Antoni
Diffusion language models offer a promising alternative to autoregressive models due to their global, non-causal generation process, but their continuous latent dynamics make discr…
Verified Training for Counterfactual Explanation Robustness under Data Shift
Anna P. Meyer, Yuhao Zhang, Aws Albarghouthi +1
Counterfactual explanations (CEs) enhance the interpretability of machine learning models by describing what changes to an input are necessary to change its prediction to a desired…
The Dataset Multiplicity Problem: How Unreliable Data Impacts Predictions
Anna P. Meyer, Aws Albarghouthi, Loris D'Antoni
We introduce dataset multiplicity, a way to study how inaccuracies, uncertainty, and social bias in training datasets impact test-time predictions. The dataset multiplicity framewo…
Certifying Data-Bias Robustness in Linear Regression
Anna P. Meyer, Aws Albarghouthi, Loris D'Antoni
Datasets typically contain inaccuracies due to human error and societal biases, and these inaccuracies can affect the outcomes of models trained on such datasets. We present a tech…
BagFlip: A Certified Defense against Data Poisoning
Yuhao Zhang, Aws Albarghouthi, Loris D'Antoni
Machine learning models are vulnerable to data-poisoning attacks, in which an attacker maliciously modifies the training set to change the prediction of a learned model. In a trigg…
Certifying Robustness to Programmable Data Bias in Decision Trees
Anna P. Meyer, Aws Albarghouthi, Loris D'Antoni
Datasets can be biased due to societal inequities, human biases, under-representation of minorities, etc. Our goal is to certify that models produced by a learning algorithm are po…
Certified Robustness to Programmable Transformations in LSTMs
Yuhao Zhang, Aws Albarghouthi, Loris D'Antoni
Deep neural networks for natural language processing are fragile in the face of adversarial examples -- small input perturbations, like synonym substitution or word duplication, wh…
Robustness to Programmable String Transformations via Augmented Abstract Training
Yuhao Zhang, Aws Albarghouthi, Loris D'Antoni
Deep neural networks for natural language processing tasks are vulnerable to adversarial input perturbations. In this paper, we present a versatile language for programmatically sp…