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06/05/2023
Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences
We study the problem of optimizing biological sequences, e.g., proteins,...
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06/02/2023
EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost
Graph-based models have become increasingly important in various domains...
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06/02/2023
Symmetric Exploration in Combinatorial Optimization is Free!
Recently, deep reinforcement learning (DRL) has shown promise in solving...
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05/30/2023
Hierarchical Graph Generation with K^2-trees
Generating graphs from a target distribution is a significant challenge ...
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03/20/2023
Imitating Graph-Based Planning with Goal-Conditioned Policies
Recently, graph-based planning algorithms have gained much attention to ...
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02/28/2023
A Closer Look at the Intervention Procedure of Concept Bottleneck Models
Concept bottleneck models (CBMs) are a class of interpretable neural net...
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02/21/2023
Diffusion Probabilistic Models for Graph-Structured Prediction
This paper studies graph-structured prediction for supervised learning o...
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10/15/2022
Substructure-Atom Cross Attention for Molecular Representation Learning
Designing a neural network architecture for molecular representation is ...
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06/22/2022
Learning Debiased Classifier with Biased Committee
Neural networks are prone to be biased towards spurious correlations bet...
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10/27/2021
RoMA: Robust Model Adaptation for Offline Model-based Optimization
We consider the problem of searching an input maximizing a black-box obj...
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07/22/2021
Abstract Reasoning via Logic-guided Generation
Abstract reasoning, i.e., inferring complicated patterns from given obse...
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06/09/2021
Self-Improved Retrosynthetic Planning
Retrosynthetic planning is a fundamental problem in chemistry for findin...
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05/03/2021
RetCL: A Selection-based Approach for Retrosynthesis via Contrastive Learning
Retrosynthesis, of which the goal is to find a set of reactants for synt...
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10/15/2020
A Deeper Look at the Layerwise Sparsity of Magnitude-based Pruning
Recent discoveries on neural network pruning reveal that, with a careful...
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07/06/2020
Learning from Failure: Training Debiased Classifier from Biased Classifier
Neural networks often learn to make predictions that overly rely on spur...
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07/04/2020
Guiding Deep Molecular Optimization with Genetic Exploration
De novo molecular design attempts to search over the chemical space for ...
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06/22/2020
QOPT: Optimistic Value Function Decentralization for Cooperative Multi-Agent Reinforcement Learning
We propose a novel value-based algorithm for cooperative multi-agent rei...
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06/17/2020
Learning What to Defer for Maximum Independent Sets
Designing efficient algorithms for combinatorial optimization appears ub...
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04/11/2019
Variational Information Distillation for Knowledge Transfer
Transferring knowledge from a teacher neural network pretrained on the s...
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03/14/2018
Bucket Renormalization for Approximate Inference
Probabilistic graphical models are a key tool in machine learning applic...
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01/05/2018
Gauged Mini-Bucket Elimination for Approximate Inference
Computing the partition function Z of a discrete graphical model is a fu...
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03/03/2017
Gauging Variational Inference
Computing partition function is the most important statistical inference...
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05/29/2016
MCMC assisted by Belief Propagaion
Markov Chain Monte Carlo (MCMC) and Belief Propagation (BP) are the most...
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09/23/2015
