2 citations · 7 across the 16 of their papers we have counts for
18 papers
Definition Grounding in Theory-Level Auto-formalization
Leyan Pan, Christian Szegedy, Frederick Rajasekaran +3
LLM-based auto-formalization systems can now generate formally verified Lean code at unprecedented rates. However, a formalization advances mathematical research only when further…
Informative Post-Hoc Explanations Only Exist for Simple Functions
Eric Günther, Balázs Szabados, Robi Bhattacharjee +2
Many researchers have suggested that local post-hoc explanation algorithms can be used to gain insights into the behavior of complex machine learning models. However, theoretical g…
How to safely discard features based on aggregate SHAP values
Robi Bhattacharjee, Karolin Frohnapfel, Ulrike von Luxburg
SHAP is one of the most popular local feature-attribution methods. Given a function f and an input x, it quantifies each feature's contribution to f(x). Recently, SHAP has been inc…
Auditing Local Explanations is Hard
Robi Bhattacharjee, Ulrike von Luxburg
In sensitive contexts, providers of machine learning algorithms are increasingly required to give explanations for their algorithms' decisions. However, explanation receivers might…
Beyond Discrepancy: A Closer Look at the Theory of Distribution Shift
Robi Bhattacharjee, Nick Rittler, Kamalika Chaudhuri
Many machine learning models appear to deploy effortlessly under distribution shift, and perform well on a target distribution that is considerably different from the training dist…
Effective resistance in metric spaces
Robi Bhattacharjee, Alexander Cloninger, Yoav Freund +1
Effective resistance (ER) is an attractive way to interrogate the structure of graphs. It is an alternative to computing the eigenvectors of the graph Laplacian. One attractive app…
Data-Copying in Generative Models: A Formal Framework
Robi Bhattacharjee, Sanjoy Dasgupta, Kamalika Chaudhuri
There has been some recent interest in detecting and addressing memorization of training data by deep neural networks. A formal framework for memorization in generative models, cal…
Robust Empirical Risk Minimization with Tolerance
Robi Bhattacharjee, Max Hopkins, Akash Kumar +2
Developing simple, sample-efficient learning algorithms for robust classification is a pressing issue in today's tech-dominated world, and current theoretical techniques requiring…
Structure from Voltage
Robi Bhattacharjee, Alex Cloninger, Yoav Freund +1
Effective resistance (ER) is an attractive way to interrogate the structure of graphs. It is an alternative to computing the eigen-vectors of the graph Laplacian. Graph laplacians…
An Exploration of Multicalibration Uniform Convergence Bounds
Harrison Rosenberg, Robi Bhattacharjee, Kassem Fawaz +1
Recent works have investigated the sample complexity necessary for fair machine learning. The most advanced of such sample complexity bounds are developed by analyzing multicalibra…
Learning what to remember
Robi Bhattacharjee, Gaurav Mahajan
We consider a lifelong learning scenario in which a learner faces a neverending and arbitrary stream of facts and has to decide which ones to retain in its limited memory. We intro…
Online -means Clustering on Arbitrary Data Streams
Robi Bhattacharjee, Jacob Imola, Michal Moshkovitz +1
We consider online -means clustering where each new point is assigned to the nearest cluster center, after which the algorithm may update its centers. The loss incurred is the s…
Consistent Non-Parametric Methods for Maximizing Robustness
Robi Bhattacharjee, Kamalika Chaudhuri
Learning classifiers that are robust to adversarial examples has received a great deal of recent attention. A major drawback of the standard robust learning framework is there is a…
No-substitution k-means Clustering with Adversarial Order
Robi Bhattacharjee, Michal Moshkovitz
We investigate -means clustering in the online no-substitution setting when the input arrives in \emph{arbitrary} order. In this setting, points arrive one after another, and th…
Sample Complexity of Adversarially Robust Linear Classification on Separated Data
Robi Bhattacharjee, Somesh Jha, Kamalika Chaudhuri
We consider the sample complexity of learning with adversarial robustness. Most prior theoretical results for this problem have considered a setting where different classes in the…
When are Non-Parametric Methods Robust?
Robi Bhattacharjee, Kamalika Chaudhuri
A growing body of research has shown that many classifiers are susceptible to {\em{adversarial examples}} -- small strategic modifications to test inputs that lead to misclassifica…
Optimally Perturbed Identity Matrices of Rank 2
Robi Bhattacharjee
The problem of optimal antipodal codes can be framed as finding low rank Gram matrices with and for . In 2018, Bukh and C…
What relations are reliably embeddable in Euclidean space?
Robi Bhattacharjee, Sanjoy Dasgupta
We consider the problem of embedding a relation, represented as a directed graph, into Euclidean space. For three types of embeddings motivated by the recent literature on knowledg…