1.3k citations · 3.8k across the 222 of their papers we have counts for
30 papers · 1 filter
The Last Mile to Supervised Performance: Semi-Supervised Domain Adaptation for Semantic Segmentation
Daniel Morales-Brotons, Grigorios Chrysos, Stratis Tzoumas +1
Supervised deep learning requires massive labeled datasets, but obtaining annotations is not always easy or possible, especially for dense tasks like semantic segmentation. To over…
Membership Inference Attacks against Large Vision-Language Models
Zhan Li, Yongtao Wu, Yihang Chen +3
Large vision-language models (VLLMs) exhibit promising capabilities for processing multi-modal tasks across various application scenarios. However, their emergence also raises sign…
μP: Effective Sharpness Aware Minimization Requires Layerwise Perturbation Scaling
Moritz Haas, Jin Xu, Volkan Cevher +1
Sharpness Aware Minimization (SAM) enhances performance across various neural architectures and datasets. As models are continually scaled up to improve performance, a rigorous und…
SAMPa: Sharpness-aware Minimization Parallelized
Wanyun Xie, Thomas Pethick, Volkan Cevher
Sharpness-aware minimization (SAM) has been shown to improve the generalization of neural networks. However, each SAM update requires \emph{sequentially} computing two gradients, e…
Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate
Zhiqi Bu, Xiaomeng Jin, Bhanukiran Vinzamuri +4
Machine unlearning has been used to remove unwanted knowledge acquired by large language models (LLMs). In this paper, we examine machine unlearning from an optimization perspectiv…
Revisiting SMoE Language Models by Evaluating Inefficiencies with Task Specific Expert Pruning
Soumajyoti Sarkar, Leonard Lausen, Volkan Cevher +3
Sparse Mixture of Expert (SMoE) models have emerged as a scalable alternative to dense models in language modeling. These models use conditionally activated feedforward subnetworks…
Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants
Beatriz Borges, Negar Foroutan, Deniz Bayazit +87
AI assistants are being increasingly used by students enrolled in higher education institutions. While these tools provide opportunities for improved teaching and education, they a…
Improving SAM Requires Rethinking its Optimization Formulation
Wanyun Xie, Fabian Latorre, Kimon Antonakopoulos +2
This paper rethinks Sharpness-Aware Minimization (SAM), which is originally formulated as a zero-sum game where the weights of a network and a bounded perturbation try to minimize/…
Inference Optimization of Foundation Models on AI Accelerators
Youngsuk Park, Kailash Budhathoki, Liangfu Chen +7
Powerful foundation models, including large language models (LLMs), with Transformer architectures have ushered in a new era of Generative AI across various industries. Industry an…
Learning to Remove Cuts in Integer Linear Programming
Pol Puigdemont, Stratis Skoulakis, Grigorios Chrysos +1
Cutting plane methods are a fundamental approach for solving integer linear programs (ILPs). In each iteration of such methods, additional linear constraints (cuts) are introduced…
Efficient Continual Finite-Sum Minimization
Ioannis Mavrothalassitis, Stratis Skoulakis, Leello Tadesse Dadi +1
Given a sequence of functions with , finite-sum minimization seeks a point minimizing $\sum_{j=1}^n…
High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization
Yihang Chen, Fanghui Liu, Taiji Suzuki +1
This paper studies kernel ridge regression in high dimensions under covariate shifts and analyzes the role of importance re-weighting. We first derive the asymptotic expansion of h…
REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates
Arshia Afzal, Grigorios Chrysos, Volkan Cevher +1
EEG-based seizure detection models face challenges in terms of inference speed and memory efficiency, limiting their real-time implementation in clinical devices. This paper introd…
Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces
Angeliki Kamoutsi, Peter Schmitt-Förster, Tobias Sutter +2
This work studies discrete-time discounted Markov decision processes with continuous state and action spaces and addresses the inverse problem of inferring a cost function from obs…
Going beyond Compositions, DDPMs Can Produce Zero-Shot Interpolations
Justin Deschenaux, Igor Krawczuk, Grigorios Chrysos +1
Denoising Diffusion Probabilistic Models (DDPMs) exhibit remarkable capabilities in image generation, with studies suggesting that they can generalize by composing latent factors l…
HeNCler: Node Clustering in Heterophilous Graphs via Learned Asymmetric Similarity
Sonny Achten, Zander Op de Beeck, Francesco Tonin +2
Clustering nodes in heterophilous graphs is challenging as traditional methods assume that effective clustering is characterized by high intra-cluster and low inter-cluster connect…
Revisiting Character-level Adversarial Attacks for Language Models
Elias Abad Rocamora, Yongtao Wu, Fanghui Liu +2
Adversarial attacks in Natural Language Processing apply perturbations in the character or token levels. Token-level attacks, gaining prominence for their use of gradient-based met…
Imitation Learning in Discounted Linear MDPs without exploration assumptions
Luca Viano, Stratis Skoulakis, Volkan Cevher
We present a new algorithm for imitation learning in infinite horizon linear MDPs dubbed ILARL which greatly improves the bound on the number of trajectories that the learner needs…
Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks
Fanghui Liu, Leello Dadi, Volkan Cevher
Recent studies show that a reproducing kernel Hilbert space (RKHS) is not a suitable space to model functions by neural networks as the curse of dimensionality (CoD) cannot be evad…
Robust NAS under adversarial training: benchmark, theory, and beyond
Yongtao Wu, Fanghui Liu, Carl-Johann Simon-Gabriel +2
Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence o…
Generalization of Scaled Deep ResNets in the Mean-Field Regime
Yihang Chen, Fanghui Liu, Yiping Lu +2
Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate \…
Efficient local linearity regularization to overcome catastrophic overfitting
Elias Abad Rocamora, Fanghui Liu, Grigorios G. Chrysos +2
Catastrophic overfitting (CO) in single-step adversarial training (AT) results in abrupt drops in the adversarial test accuracy (even down to 0%). For models trained with multi-ste…
Extreme Miscalibration and the Illusion of Adversarial Robustness
Vyas Raina, Samson Tan, Volkan Cevher +3
Deep learning-based Natural Language Processing (NLP) models are vulnerable to adversarial attacks, where small perturbations can cause a model to misclassify. Adversarial Training…
Truly No-Regret Learning in Constrained MDPs
Adrian Müller, Pragnya Alatur, Volkan Cevher +2
Constrained Markov decision processes (CMDPs) are a common way to model safety constraints in reinforcement learning. State-of-the-art methods for efficiently solving CMDPs are bas…