27 citations · 87 across the 12 of their papers we have counts for
12 papers
Multilingual Contrastive Decoding via Language-Agnostic Layers Skipping
Wenhao Zhu, Sizhe Liu, Shujian Huang +3
Decoding by contrasting layers (DoLa), is designed to improve the generation quality of large language models (LLMs) by contrasting the prediction probabilities between an early ex…
Do Llamas Work in English? On the Latent Language of Multilingual Transformers
Chris Wendler, Veniamin Veselovsky, Giovanni Monea +1
We ask whether multilingual language models trained on unbalanced, English-dominated corpora use English as an internal pivot language -- a question of key importance for understan…
Sketch-Guided Constrained Decoding for Boosting Blackbox Large Language Models without Logit Access
Saibo Geng, Berkay Döner, Chris Wendler +2
Constrained decoding, a technique for enforcing constraints on language model outputs, offers a way to control text generation without retraining or architectural modifications. It…
The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation data
Mathieu Chevalley, Jacob Sackett-Sanders, Yusuf Roohani +15
In drug discovery, mapping interactions between genes within cellular systems is a crucial early step. Such maps are not only foundational for understanding the molecular mechanism…
Learning DAGs from Data with Few Root Causes
Panagiotis Misiakos, Chris Wendler, Markus Püschel
We present a novel perspective and algorithm for learning directed acyclic graphs (DAGs) from data generated by a linear structural equation model (SEM). First, we show that a line…
Causal Fourier Analysis on Directed Acyclic Graphs and Posets
Bastian Seifert, Chris Wendler, Markus Püschel
We present a novel form of Fourier analysis, and associated signal processing concepts, for signals (or data) indexed by edge-weighted directed acyclic graphs (DAGs). This means th…
Instance-wise algorithm configuration with graph neural networks
Romeo Valentin, Claudio Ferrari, Jérémy Scheurer +3
We present our submission for the configuration task of the Machine Learning for Combinatorial Optimization (ML4CO) NeurIPS 2021 competition. The configuration task is to predict a…
Discrete Signal Processing on Meet/Join Lattices
Markus Püschel, Bastian Seifert, Chris Wendler
A lattice is a partially ordered set supporting a meet (or join) operation that returns the largest lower bound (smallest upper bound) of two elements. Just like graphs, lattices a…
Learning Set Functions that are Sparse in Non-Orthogonal Fourier Bases
Chris Wendler, Andisheh Amrollahi, Bastian Seifert +2
Many applications of machine learning on discrete domains, such as learning preference functions in recommender systems or auctions, can be reduced to estimating a set function tha…
Fourier Analysis-based Iterative Combinatorial Auctions
Jakob Weissteiner, Chris Wendler, Sven Seuken +2
Recent advances in Fourier analysis have brought new tools to efficiently represent and learn set functions. In this paper, we bring the power of Fourier analysis to the design of…
Discrete Signal Processing with Set Functions
Markus Püschel, Chris Wendler
Set functions are functions (or signals) indexed by the powerset (set of all subsets) of a finite set N. They are fundamental and ubiquitous in many application domains and have be…
Powerset Convolutional Neural Networks
Chris Wendler, Dan Alistarh, Markus Püschel
We present a novel class of convolutional neural networks (CNNs) for set functions, i.e., data indexed with the powerset of a finite set. The convolutions are derived as linear, sh…