Yair Schiff

Publications

• InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models
  Yingheng Wang, Yair Schiff, Aaron Gokaslan, Weishen Pan, Fei Wang, Christopher De Sa, Volodymyr Kuleshov
  ICML 2023
  [Paper] , [Video]
• Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows
  Phillip Si, Zeyi Chen, Subham Sekhar Sahoo, Yair Schiff, Volodymyr Kuleshov
  ICML 2023
  [Paper] , [Video]
• Learning with Stochastic Orders
  Carles Domingo-Enrich, Yair Schiff, Youssef Mroueh
  ICLR 2023 , Notable Top 25% acceptance
  [Paper] , [Code] , [Slides] , [Video]
• Semi-Parametric Inducing Point Networks and Neural Processes
  Richa Rastogi, Yair Schiff, Alon Hacohen, Zhaozhi Li, Ian Lee, Yuntian Deng, Mert R. Sabuncu, Volodymyr Kuleshov
  ICLR 2023
  [Paper] , [Code] , [Slides] , [Video]
• Cloud-Based Real-Time Molecular Screening Platform with MolFormer
  Brian Belgodere*, Vijil Chenthamarakshan*, Payel Das*, Pierre Dognin*, Toby Kurien*, Igor Melnyk*, Youssef Mroueh*, Inkit Padhi*, Mattia Rigotti*, Jarret Ross*, Yair Schiff*, Richard A. Young* (*equal contribution, alphabetical order)
  ECML PKDD 2022 Demo Track
  [Paper]
• Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks
  David Alvarez-Melis, Yair Schiff, Youssef Mroueh
  Transactions of Machine Learning Research
  OTML NeurIPS Workshop 2021, Spotlight presentation
  [Paper] , [Code] , [Slides] , [Video]
• Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations
  Yair Schiff*, Vijil Chenthamarakshan*, Samuel Hoffman*, Karthikeyan Natesan Ramamurthy*, Payel Das* (*equal contribution)
  ICASSP 2022
  [Paper]
• Predicting Deep Neural Network Generalization with Perturbation Response Curves
  Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen
  NeurIPS 2021
  [Paper] , [Slides] , [Video]
• Tabular Transformers for Modeling Multivariate Time Series
  Inkit Padhi, Yair Schiff, Igor Melnyk, Mattia Rigotti, Youssef Mroueh, Pierre Dognin, Jarret Ross, Ravi Nair, Erik Altman
  ICASSP 2021
  [Paper] , [Code]
• Image Captioning as an Assistive Technology: Lessons Learned from VizWiz 2020 Challenge
  Pierre Dognin*, Igor Melnyk*, Youssef Mroueh*, Inkit Padhi*, Mattia Rigotti*, Jarret Ross*, Yair Schiff*, Richard Young, Brian Belgodere (*equal contribution)
  Journal of AI Research
  [Paper] , [Slides]

Workshops

• Advancing DNA Language Models: The Genomics Long-Range Benchmark
  Evan Trop, Chia-Hsiang Kao, Mckinley Polen, Yair Schiff, Bernardo P. de Almeida, Aaron Gokaslan, Thomas Pierrot, Volodymyr Kuleshov
  LLMs4Bio AAAI Workshop 2024,  MLGenX ICLR Workshop 2024
  [Paper]
• Gi and Pal Scores: Deep Neural Network Generalization Statistics
  Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen
  RobustML ICLR Workshop 2021
  [Paper]
• Characterizing the Latent Space of Molecular Deep Generative Models
  Yair Schiff, Vijil Chenthamarakshan, Karthikeyan Natesan Ramamurthy, Payel Das
  TDA & Beyond NeurIPS Workshop 2020 , Spotlight presentation
  [Paper] , [Slides] , [Video]
• Alleviating Noisy Data in Image Captioning with Cooperative Distillation
  Pierre Dognin*, Igor Melnyk*, Youssef Mroueh*, Inkit Padhi*, Mattia Rigotti*, Jarret Ross*, Yair Schiff* (*equal contribution, alphabetical order)
  VizWiz CVPR Workshop 2020
  [Paper] , [Slides]

Preprints

• Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling
  Yair Schiff, Chia-Hsiang Kao, Aaron Gokaslan, Tri Dao, Albert Gu, Volodymyr Kuleshov
  [Paper] , [Site] , [Code]
• DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems
  Yair Schiff, Zhong Yi Wan, Jeffrey B. Parker, Stephan Hoyer, Volodymyr Kuleshov, Fei Sha, Leonardo Zepeda-Núñez
  [Paper]
• Auditing and Generating Synthetic Data with Controllable Trust Trade-offs
  Brian Belgodere, Pierre Dognin, Adam Ivankay, Igor Melnyk, Youssef Mroueh, Aleksandra Mojsilovic, Jiri Navartil, Apoorva Nitsure, Inkit Padhi, Mattia Rigotti, Jerret Ross, Yair Schiff, Radhika Vedpathak, Richard A. Young
  [Paper]