4 citations · 19 across the 11 of their papers we have counts for
18 papers
Beyond Interaction Capacity: Estimator Scaling with Recursive Models for CTR Prediction
Shivang Chopra, Fotis Iliopoulos, Zsolt Kira +1
Click-Through Rate prediction, a core task in recommendation and advertising systems, relies on modeling interactions among sparse categorical features. Explicit cross networks are…
Gemma 4 Technical Report
Gemma Team, Sherif El Abd, Vaibhav Aggarwal +320
We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemm…
Linear Projections of Teacher Embeddings for Few-Class Distillation
Noel Loo, Fotis Iliopoulos, Wei Hu +1
Knowledge Distillation (KD) has emerged as a promising approach for transferring knowledge from a larger, more complex teacher model to a smaller student model. Traditionally, KD i…
LayerNAS: Neural Architecture Search in Polynomial Complexity
Yicheng Fan, Dana Alon, Jingyue Shen +7
Neural Architecture Search (NAS) has become a popular method for discovering effective model architectures, especially for target hardware. As such, NAS methods that find optimal a…
SLaM: Student-Label Mixing for Distillation with Unlabeled Examples
Vasilis Kontonis, Fotis Iliopoulos, Khoa Trinh +3
Knowledge distillation with unlabeled examples is a powerful training paradigm for generating compact and lightweight student models in applications where the amount of labeled dat…
Weighted Distillation with Unlabeled Examples
Fotis Iliopoulos, Vasilis Kontonis, Cenk Baykal +3
Distillation with unlabeled examples is a popular and powerful method for training deep neural networks in settings where the amount of labeled data is limited: A large ''teacher''…
Robust Active Distillation
Cenk Baykal, Khoa Trinh, Fotis Iliopoulos +2
Distilling knowledge from a large teacher model to a lightweight one is a widely successful approach for generating compact, powerful models in the semi-supervised learning setting…
Group testing and local search: is there a computational-statistical gap?
Fotis Iliopoulos, Ilias Zadik
In this work we study the fundamental limits of approximate recovery in the context of group testing. One of the most well-known, theoretically optimal, and easy to implement testi…
A new notion of commutativity for the algorithmic Lovász Local Lemma
David G. Harris, Fotis Iliopoulos, Vladimir Kolmogorov
The Lovász Local Lemma (LLL) is a powerful tool in probabilistic combinatorics which can be used to establish the existence of objects that satisfy certain properties. The breakthr…
Improved bounds for coloring locally sparse hypergraphs
Fotis Iliopoulos
We show that, for every , every -uniform hypergaph of degree and girth at least is efficiently -list colorable. As an ap…
Efficiently list-edge coloring multigraphs asymptotically optimally
Fotis Iliopoulos, Alistair Sinclair
We give polynomial time algorithms for the seminal results of Kahn, who showed that the Goldberg-Seymour and List-Coloring conjectures for (list-)edge coloring multigraphs hold asy…
A Local Lemma for Focused Stochastic Algorithms
Dimitris Achlioptas, Fotis Iliopoulos, Vladimir Kolmogorov
We develop a framework for the rigorous analysis of focused stochastic local search algorithms. These are algorithms that search a state space by repeatedly selecting some constrai…
Simple Local Computation Algorithms for the General Lovasz Local Lemma
Dimitris Achlioptas, Themis Gouleakis, Fotis Iliopoulos
We consider the task of designing Local Computation Algorithms (LCA) for applications of the Lovász Local Lemma (LLL). LCA is a class of sublinear algorithms proposed by Rubinfeld…
Beyond the Lovasz Local Lemma: Point to Set Correlations and Their Algorithmic Applications
Dimitris Achlioptas, Fotis Iliopoulos, Alistair Sinclair
Following the groundbreaking algorithm of Moser and Tardos for the Lovasz Local Lemma (LLL), there has been a plethora of results analyzing local search algorithms for various cons…
Commutative Algorithms Approximate the LLL-distribution
Fotis Iliopoulos
Following the groundbreaking Moser-Tardos algorithm for the Lovasz Local Lemma (LLL), a series of works have exploited a key ingredient of the original analysis, the witness tree l…
Stochastic Control via Entropy Compression
Dimitris Achlioptas, Fotis Iliopoulos, Nikos Vlassis
We consider an agent trying to bring a system to an acceptable state by repeated probabilistic action. Several recent works on algorithmizations of the Lovasz Local Lemma (LLL) can…
Focused Stochastic Local Search and the Lovász Local Lemma
Dimitris Achlioptas, Fotis Iliopoulos
We develop tools for analyzing focused stochastic local search algorithms. These are algorithms which search a state space probabilistically by repeatedly selecting a constraint th…
Random Walks that Find Perfect Objects and the Lovász Local Lemma
Dimitris Achlioptas, Fotis Iliopoulos
We give an algorithmic local lemma by establishing a sufficient condition for the uniform random walk on a directed graph to reach a sink quickly. Our work is inspired by Moser's e…