96 citations · 298 across the 54 of their papers we have counts for
57 papers
AutoRSR: Automated Real-to-Sim-to-Real Data Generation for Scalable Robot Learning
Jiawei Zhang, Chang Liu, Litao Deng +2
Scaling embodied robot learning requires large and diverse manipulation datasets, but collecting real-world demonstrations remains expensive in human effort and robot time. Simulat…
Lifting Finite-Sample Gaussian Approximation of Wide Neural Networks to Functional Convergence
Luis Wiederhold, Nil Ayday, Debarghya Ghoshdastidar
Randomly initialised neural networks of width converge to Gaussian processes as , with two types of quantitative bounds characterising this convergence. Finite-sa…
An Analysis of Self-supervised Pre-training with Dependent Samples
Maximilian Fleissner, Debarghya Ghoshdastidar, Samory Kpotufe
Self-supervised learning relies on so-called data augmentations of unlabeled datapoints --- for example, masking random pixels in an image --- that should leave the…
Different Statistical Perspectives for Understanding Generalisation in Graph Neural Networks
Nil Ayday, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar
Graph Neural Networks (GNN) are currently the most popular approach for learning and prediction on graph-structured data and are deployed in various fields, from social network ana…
Exact Certification of Neural Networks and Partition Aggregation Ensembles against Label Poisoning
Ajinkya Mohgaonkar, Lukas Gosch, Mahalakshmi Sabanayagam +2
Label-flipping attacks, which corrupt training labels to induce misclassifications at inference, remain a major threat to supervised learning models. This drives the need for robus…
Gaussian Process Limit Reveals Structural Benefits of Graph Transformers
Nil Ayday, Lingchu Yang, Debarghya Ghoshdastidar
Graph transformers are the state-of-the-art for learning from graph-structured data and are empirically known to avoid several pitfalls of message-passing architectures. However, t…
Nonparametric Kernel Clustering with Bandit Feedback
Victor Thuot, Sebastian Vogt, Debarghya Ghoshdastidar +1
Clustering with bandit feedback refers to the problem of partitioning the arms of an -armed stochastic bandit according to their underlying distributions, based on noisy observa…
Robustness Certificates for Neural Networks Against Data Poisoning and Evasion Attacks
Sara Taheri, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar +1
The increasing use of machine learning in safety-critical domains amplifies the risk of adversarial threats, especially data poisoning attacks that corrupt training data to degrade…
Non-Singularity of the Gradient Descent map for Neural Networks with Piecewise Analytic Activations
Alexandru Crăciun, Debarghya Ghoshdastidar
The theory of training deep networks has become a central question of modern machine learning and has inspired many practical advancements. In particular, the gradient descent (GD)…
Theoretical Foundations of Representation Learning using Unlabeled Data: Statistics and Optimization
Pascal Esser, Maximilian Fleissner, Debarghya Ghoshdastidar
Representation learning from unlabeled data has been extensively studied in statistics, data science and signal processing with a rich literature on techniques for dimension reduct…
Interpretable Self-Supervised Learning via Representer Landmarks and Nyström Approximation
Maedeh Zarvandi, Michael Timothy, Theresa Wasserer +1
Self-supervised learning (SSL) learns representations from massive unlabeled data, yet the resulting models typically operate as black boxes, necessitating domain-specific explanat…
Exact Generalisation Error Exposes Benchmarks Skew Graph Neural Networks Success (or Failure)
Nil Ayday, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar
Graph Neural Networks (GNNs) have become the standard method for learning from networks across fields ranging from biology to social systems, yet a principled understanding of what…
Impact of Bottleneck Layers and Skip Connections on the Generalization of Linear Denoising Autoencoders
Jonghyun Ham, Maximilian Fleissner, Debarghya Ghoshdastidar
Modern deep neural networks exhibit strong generalization even in highly overparameterized regimes. Significant progress has been made to understand this phenomenon in the context…
Generalization Certificates for Adversarially Robust Bayesian Linear Regression
Mahalakshmi Sabanayagam, Russell Tsuchida, Cheng Soon Ong +1
Adversarial robustness of machine learning models is critical to ensuring reliable performance under data perturbations. Recent progress has been on point estimators, and this pape…
Transformers Provably Learn Sparse XOR with Polylogarithmic Parameters
Yaomengxi Han, Debarghya Ghoshdastidar
Learning sparse parity functions has become a theoretical testbed for studying feature learning in neural networks. However, existing analyses primarily focus on Feed-Forward Neura…
Recovering Imbalanced Clusters via Gradient-Based Projection Pursuit
Martin Eppert, Satyaki Mukherjee, Debarghya Ghoshdastidar
Projection Pursuit is a classic exploratory technique for finding interesting projections of a dataset. We propose a method for recovering projections containing either Imbalanced…
A Probabilistic Model for Non-Contrastive Learning
Maximilian Fleissner, Pascal Esser, Debarghya Ghoshdastidar
Self-supervised learning (SSL) aims to find meaningful representations from unlabeled data by encoding semantic similarities through data augmentations. Despite its current popular…
Tight PAC-Bayesian Risk Certificates for Contrastive Learning
Anna Van Elst, Debarghya Ghoshdastidar
Contrastive representation learning is a modern paradigm for learning representations of unlabeled data via augmentations -- precisely, contrastive models learn to embed semantical…
Exact Certification of (Graph) Neural Networks Against Label Poisoning
Mahalakshmi Sabanayagam, Lukas Gosch, Stephan Günnemann +1
Machine learning models are highly vulnerable to label flipping, i.e., the adversarial modification (poisoning) of training labels to compromise performance. Thus, deriving robustn…
Infinite Width Limits of Self Supervised Neural Networks
Maximilian Fleissner, Gautham Govind Anil, Debarghya Ghoshdastidar
The NTK is a widely used tool in the theoretical analysis of deep learning, allowing us to look at supervised deep neural networks through the lenses of kernel regression. Recently…
A Theoretical Characterization of Optimal Data Augmentations in Self-Supervised Learning
Shlomo Libo Feigin, Maximilian Fleissner, Debarghya Ghoshdastidar
Data augmentations play an important role in the recent success of self-supervised learning (SSL). While augmentations are commonly understood to encode invariances between differe…
Explainable Clustering of Mixture Models
Maximilian Fleissner, Maedeh Zarvandi, Debarghya Ghoshdastidar
The explainable clustering problem was first posed by Moshkovitz et al. (ICML 2020) and studies how well an axis-aligned decision tree with leaves can approximate a given clust…
Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks
Lukas Gosch, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar +1
Generalization of machine learning models can be severely compromised by data poisoning, where adversarial changes are applied to the training data. This vulnerability has led to i…
When can we Approximate Wide Contrastive Models with Neural Tangent Kernels and Principal Component Analysis?
Gautham Govind Anil, Pascal Esser, Debarghya Ghoshdastidar
Contrastive learning is a paradigm for learning representations from unlabelled data that has been highly successful for image and text data. Several recent works have examined con…