16 citations · 35 across the 20 of their papers we have counts for
10 papers · 1 filter
Inference-time Unlearning Using Conformal Prediction
Somnath Basu Roy Chowdhury, Rahul Kidambi, Avinava Dubey +4
Machine unlearning is the process of efficiently removing specific information from a trained machine learning model without retraining from scratch. Existing unlearning methods, w…
Fundamental Limits of Perfect Concept Erasure
Somnath Basu Roy Chowdhury, Avinava Dubey, Ahmad Beirami +4
Concept erasure is the task of erasing information about a concept (e.g., gender or race) from a representation set while retaining the maximum possible utility -- information from…
Structured Unrestricted-Rank Matrices for Parameter Efficient Fine-tuning
Arijit Sehanobish, Avinava Dubey, Krzysztof Choromanski +4
Recent efforts to scale Transformer models have demonstrated rapid progress across a wide range of tasks (Wei et al., 2022). However, fine-tuning these models for downstream tasks…
Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning
Somnath Basu Roy Chowdhury, Krzysztof Choromanski, Arijit Sehanobish +2
Machine unlearning is the process of efficiently removing the influence of a training data instance from a trained machine learning model without retraining it from scratch. A popu…
Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers
Krzysztof Choromanski, Arijit Sehanobish, Somnath Basu Roy Chowdhury +4
We present a new class of fast polylog-linear algorithms based on the theory of structured matrices (in particular low displacement rank) for integrating tensor fields defined on w…
Robust Concept Erasure via Kernelized Rate-Distortion Maximization
Somnath Basu Roy Chowdhury, Nicholas Monath, Avinava Dubey +2
Distributed representations provide a vector space that captures meaningful relationships between data instances. The distributed nature of these representations, however, entangle…
Enhancing Group Fairness in Online Settings Using Oblique Decision Forests
Somnath Basu Roy Chowdhury, Nicholas Monath, Ahmad Beirami +4
Fairness, especially group fairness, is an important consideration in the context of machine learning systems. The most commonly adopted group fairness-enhancing techniques are in-…
Efficient Graph Field Integrators Meet Point Clouds
Krzysztof Choromanski, Arijit Sehanobish, Han Lin +13
We present two new classes of algorithms for efficient field integration on graphs encoding point clouds. The first class, SeparatorFactorization(SF), leverages the bounded genus o…
Sustaining Fairness via Incremental Learning
Somnath Basu Roy Chowdhury, Snigdha Chaturvedi
Machine learning systems are often deployed for making critical decisions like credit lending, hiring, etc. While making decisions, such systems often encode the user's demographic…
Learning Fair Representations via Rate-Distortion Maximization
Somnath Basu Roy Chowdhury, Snigdha Chaturvedi
Text representations learned by machine learning models often encode undesirable demographic information of the user. Predictive models based on these representations can rely on s…