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Found 40 papers, 13 papers with code
Conformal Language Modeling
Translating this process to conformal prediction, we calibrate a stopping rule for sampling different outputs from the LM that get added to a growing set of candidates until we are confident that the output set is sufficient.
DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models
Understanding how proteins structurally interact is crucial to modern biology, with applications in drug discovery and protein design.
PEOPL: Characterizing Privately Encoded Open Datasets with Public Labels
Allowing organizations to share their data for training of machine learning (ML) models without unintended information leakage is an open problem in practice.
Adversarial Support Alignment
Furthermore, we show that our approach can be viewed as a limit of existing notions of alignment by increasing transportation assignment tolerance.
Syfer: Neural Obfuscation for Private Data Release
We quantify privacy as the number of attacker guesses required to re-identify a single image (guesswork).
Understanding Interlocking Dynamics of Cooperative Rationalization
The selection mechanism is commonly integrated into the model itself by specifying a two-component cascaded system consisting of a rationale generator, which makes a binary selection of the input features (which is the rationale), and a predictor, which predicts the output based only on the selected features.
Text Style Transfer with Confounders
Existing methods for style transfer operate either with paired sentences or distributionally matched corpora which differ only in the desired style.
Trading Coverage for Precision: Conformal Prediction with Limited False Discoveries
In this paper, we develop a new approach to conformal prediction in which we aim to output a precise set of promising prediction candidates that is guaranteed to contain a limited number of incorrect answers.
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles
Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery.
NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Training
We propose to approximate this family of encoding functions through random deep neural networks.
Fundamental Limits and Tradeoffs in Invariant Representation Learning
A wide range of machine learning applications such as privacy-preserving learning, algorithmic fairness, and domain adaptation/generalization among others, involve learning invariant representations of the data that aim to achieve two competing goals: (a) maximize information or accuracy with respect to a target response, and (b) maximize invariance or independence with respect to a set of protected features (e. g., for fairness, privacy, etc).
Locally Constant Networks
We show how neural models can be used to realize piece-wise constant functions such as decision trees.
Invariant Rationalization
Selective rationalization improves neural network interpretability by identifying a small subset of input features -- the rationale -- that best explains or supports the prediction.
Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces
This paper focuses on the problem of unsupervised alignment of hierarchical data such as ontologies or lexical databases.
Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control
Moreover, we explicitly control the rationale complement via an adversary so as not to leave any useful information out of the selection.
A Game Theoretic Approach to Class-wise Selective Rationalization
Selection of input features such as relevant pieces of text has become a common technique of highlighting how complex neural predictors operate.
Learning to Make Generalizable and Diverse Predictions for Retrosynthesis
We propose a new model for making generalizable and diverse retrosynthetic reaction predictions.
Oblique Decision Trees from Derivatives of ReLU Networks
We show how neural models can be used to realize piece-wise constant functions such as decision trees.
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Towards Robust, Locally Linear Deep Networks
In this paper, we propose a new learning problem to encourage deep networks to have stable derivatives over larger regions.
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers
Specifically, an $\ell_2$ bounded adversary cannot alter the ensemble prediction generated by an additive isotropic Gaussian noise, where the radius for the adversary depends on both the variance of the distribution as well as the ensemble margin at the point of interest.
Functional Transparency for Structured Data: a Game-Theoretic Approach
We provide a new approach to training neural models to exhibit transparency in a well-defined, functional manner.
Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling
We consider the problem of inferring the values of an arbitrary set of variables (e. g., risk of diseases) given other observed variables (e. g., symptoms and diagnosed diseases) and high-dimensional signals (e. g., MRI images or EEG).
A graph-convolutional neural network model for the prediction of chemical reactivity
We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s).
Gromov-Wasserstein Alignment of Word Embedding Spaces
Cross-lingual or cross-domain correspondences play key roles in tasks ranging from machine translation to transfer learning.
Game-Theoretic Interpretability for Temporal Modeling
In contrast, we focus on temporal modeling and the problem of tailoring the predictor, functionally, towards an interpretable family.
Towards Optimal Transport with Global Invariances
Many problems in machine learning involve calculating correspondences between sets of objects, such as point clouds or images.
On the Robustness of Interpretability Methods
We argue that robustness of explanations---i. e., that similar inputs should give rise to similar explanations---is a key desideratum for interpretability.
Towards Robust Interpretability with Self-Explaining Neural Networks
Most recent work on interpretability of complex machine learning models has focused on estimating $\textit{a posteriori}$ explanations for previously trained models around specific predictions.
Structured Optimal Transport
Optimal Transport has recently gained interest in machine learning for applications ranging from domain adaptation, sentence similarities to deep learning.
Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture
We focus on predicting sleep stages from radio measurements without any attached sensors on subjects.
A causal framework for explaining the predictions of black-box sequence-to-sequence models
We interpret the predictions of any black-box structured input-structured output model around a specific input-output pair.
Word Embeddings as Metric Recovery in Semantic Spaces
Continuous word representations have been remarkably useful across NLP tasks but remain poorly understood.
From random walks to distances on unweighted graphs
Using these techniques we generalize results on the degeneracy of hitting times and analyze a metric based on the Laplace transformed hitting time (LTHT).
Word, graph and manifold embedding from Markov processes
Continuous vector representations of words and objects appear to carry surprisingly rich semantic content.
Metric recovery from directed unweighted graphs
We demonstrate empirically that the estimator performs well on simulated examples as well as on real-world co-purchasing graphs even with a small number of points and degree scaling as low as $\log(n)$.
Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models
Furthermore, instead of obtaining a single solution for a specific regularization parameter, our algorithm finds the whole solution path.
Convergence Rate Analysis of MAP Coordinate Minimization Algorithms
We also provide a simple dual to primal mapping that yields feasible primal solutions with a guaranteed rate of convergence.
More data means less inference: A pseudo-max approach to structured learning
The problem of learning to predict structured labels is of key importance in many applications.
Clusters and Coarse Partitions in LP Relaxations
We propose a new class of consistency constraints for Linear Programming (LP) relaxations for finding the most probable (MAP) configuration in graphical models.
Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations
We present a novel message passing algorithm for approximating the MAP problem in graphical models.
