Most Influential UAI Papers (2025-09 Version)
The Annual Conference on Uncertainty in Artificial Intelligence (UAI) is one of the top machine learning conferences in the world. Paper Digest Team analyzes all papers published on UAI in the past years, and presents the 15 most influential papers for each year. This ranking list is automatically constructed based upon citations from both research papers and granted patents, and will be frequently updated to reflect the most recent changes. To find the latest version of this list or the most influential papers from other conferences/journals, please visit Best Paper Digest page. Note: the most influential papers may or may not include the papers that won the best paper awards. (Version: 2025-09)
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TABLE 1: Most Influential UAI Papers (2025-09 Version)
| Year | Rank | Paper | Author(s) |
|---|---|---|---|
| 2024 | 1 | Discrete Probabilistic Inference As Control in Multi-path Environments IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we extend recent methods correcting the reward in order to guarantee that the marginal distribution induced by the optimal MaxEnt RL policy is proportional to the original reward, regardless of the structure of the underlying MDP. |
Tristan Deleu; Padideh Nouri; Nikolay Malkin; Doina Precup; Yoshua Bengio; |
| 2024 | 2 | BEARS Make Neuro-Symbolic Models Aware of Their Reasoning Shortcuts IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Neuro-Symbolic (NeSy) predictors that conform to symbolic knowledge {–} encoding, e.g., safety constraints {–} can be affected by Reasoning Shortcuts (RSs): They learn concepts consistent with the symbolic knowledge by exploiting unintended semantics. RSs compromise reliability and generalization and, as we show in this paper, they are linked to NeSy models being overconfident about the predicted concepts. |
EMANUELE MARCONATO et. al. |
| 2024 | 3 | Neural Optimal Transport with Lagrangian Costs IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our contributions are of computational interest, where we demonstrate the ability to efficiently compute geodesics and amortize spline-based paths, which has not been done before, even in low dimensional problems. |
Aram-Alexandre Pooladian; Carles Domingo-Enrich; Ricky T. Q. Chen; Brandon Amos; |
| 2024 | 4 | Towards Minimax Optimality of Model-based Robust Reinforcement Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we consider uncertainty sets defined with an $L_p$-ball (recovering the TV case), and study the sample complexity of any planning algorithm (with high accuracy guarantee on the solution) applied to an empirical RMDP estimated using the generative model. |
Pierre Clavier; Erwan Le Pennec; Matthieu Geist; |
| 2024 | 5 | Pix2Code: Learning to Compose Neural Visual Concepts As Programs IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To tackle both the generalizability and interpretability constraints of visual concept learning, we propose Pix2Code, a framework that extends program synthesis to visual relational reasoning by utilizing the abilities of both explicit, compositional symbolic and implicit neural representations. |
Antonia W�st; Wolfgang Stammer; Quentin Delfosse; Devendra Singh Dhami; Kristian Kersting; |
| 2024 | 6 | Amortized Variational Inference: When and Why? IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we study when and why A-VI can be used for approximate Bayesian inference. |
Charles C. Margossian; David M. Blei; |
| 2024 | 7 | Revisiting Convergence of AdaGrad with Relaxed Assumptions IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this study, we revisit the convergence of AdaGrad with momentum (covering AdaGrad as a special case) on non-convex smooth optimization problems. |
Yusu Hong; Junhong Lin; |
| 2024 | 8 | Targeted Reduction of Causal Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose an information theoretic objective to learn TCR from interventional data of simulations, establish identifiability for continuous variables under shift interventions and present a practical algorithm for learning TCRs. |
Armin Kekic; Bernhard Sch�lkopf; Michel Besserve; |
| 2024 | 9 | Polynomial Semantics of Tractable Probabilistic Circuits IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The relationships between these polynomial encodings of distributions is largely unknown. In this paper, we prove that for binary distributions, each of these probabilistic circuit models is equivalent in the sense that any circuit for one of them can be transformed into a circuit for any of the others with only a polynomial increase in size. |
Oliver Broadrick; Honghua Zhang; Guy Van den Broeck; |
| 2024 | 10 | Adjustment Identification Distance: A Gadjid for Causal Structure Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a framework for developing causal distances between graphs which includes the structural intervention distance for directed acyclic graphs as a special case. |
Leonard Henckel; Theo W�rtzen; Sebastian Weichwald; |
| 2024 | 11 | End-to-end Conditional Robust Optimization IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Recently, a risk sensitive variant of CO, known as Conditional Robust Optimization (CRO), combines uncertainty quantification with robust optimization in order to promote safety and reliability in high stake applications. Exploiting modern differentiable optimization methods, we propose a novel end-to-end approach to train a CRO model in a way that accounts for both the empirical risk of the prescribed decisions and the quality of conditional coverage of the contextual uncertainty set that supports them. |
Abhilash Reddy Chenreddy; Erick Delage; |
| 2024 | 12 | Label-wise Aleatoric and Epistemic Uncertainty Quantification IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We present a novel approach to uncertainty quantification in classification tasks based on label-wise decomposition of uncertainty measures. |
YUSUF SALE et. al. |
| 2023 | 1 | Jana: Jointly Amortized Neural Approximation of Complex Bayesian Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This work proposes “jointly amortized neural approximation” (JANA) of intractable likelihood functions and posterior densities arising in Bayesian surrogate modeling and simulation-based inference. |
STEFAN T. RADEV et. al. |
| 2023 | 2 | Stochastic Generative Flow Networks IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, existing GFlowNets can be applied only to deterministic environments, and fail in more general tasks with stochastic dynamics, which can limit their applicability. To overcome this challenge, this paper introduces Stochastic GFlowNets, a new algorithm that extends GFlowNets to stochastic environments. |
Ling Pan; Dinghuai Zhang; Moksh Jain; Longbo Huang; Yoshua Bengio; |
| 2023 | 3 | BISCUIT: Causal Representation Learning from Binary Interactions IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we show that causal variables can still be identified for many common setups, e.g., additive Gaussian noise models, if the agent’s interactions with a causal variable can be described by an unknown binary variable. |
PHILLIP LIPPE et. al. |
| 2023 | 4 | Learning To Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: These defenses have so far been very effective, in particular those based on gradient compression that allow the model to maintain high accuracy while greatly reducing the effectiveness of attacks. In this work, we argue that such findings underestimate the privacy risk in FL. |
Ruihan Wu; Xiangyu Chen; Chuan Guo; Kilian Q. Weinberger; |
| 2023 | 5 | Is The Volume of A Credal Set A Good Measure for Epistemic Uncertainty? IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we show that the volume of the geometric representation of a credal set is a meaningful measure of epistemic uncertainty in the case of binary classification, but less so for multi-class classification. |
Yusuf Sale; Michele Caprio; Eyke H�llermeier; |
| 2023 | 6 | Neural Probabilistic Logic Programming in Discrete-continuous Domains IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Hence, we introduce DeepSeaProbLog, a neural probabilistic logic programming language that incorporates DPP techniques into NeSy. |
LENNERT DE SMET et. al. |
| 2023 | 7 | Approximate Thompson Sampling Via Epistemic Neural Networks IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We compare a range of ENNs through computational experiments that assess their performance in approximating TS across bandit and reinforcement learning environments. |
IAN OSBAND et. al. |
| 2023 | 8 | SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we show the benefits of using unstructured weight sparsity to train only a subset of weights during pre-training (Sparse Pre-training) and then recover the representational capacity by allowing the zeroed weights to learn (Dense Fine-tuning). |
VITHURSAN THANGARASA et. al. |
| 2023 | 9 | Probabilistically Robust Conformal Prediction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel adaptive PRCP (aPRCP) algorithm to achieve probabilistically robust coverage. |
SUBHANKAR GHOSH et. al. |
| 2023 | 10 | Causal Discovery for Time Series from Multiple Datasets with Latent Contexts IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Pooling the datasets and considering the joint causal graph among system, context, and certain auxiliary variables enables us to overcome such latent confounding of system variables. In this work, we present a non-parametric time series causal discovery method, J(oint)-PCMCI$^+$, that efficiently learns such joint causal time series graphs when both observed and latent contexts are present, including time lags. |
Wiebke G�nther; Urmi Ninad; Jakob Runge; |
| 2023 | 11 | CrysMMNet: Multimodal Representation for Crystal Property Prediction IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we leverage textual descriptions of materials to model global structural information into graph structure and learn a more robust and enriched representation of crystalline materials. |
Kishalay Das; Pawan Goyal; Seung-Cheol Lee; Satadeep Bhattacharjee; Niloy Ganguly; |
| 2023 | 12 | Robust Statistical Comparison of Random Variables with Locally Varying Scale of Measurement IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Nevertheless, it is still understood as an open question how to exploit the entire information encoded in them properly. We address this problem by considering an order based on (sets of) expectations of random variables mapping into such non-standard spaces. |
Christoph Jansen; Georg Schollmeyer; Hannah Blocher; Julian Rodemann; Thomas Augustin; |
| 2023 | 13 | When Are Post-hoc Conceptual Explanations Identifiable? IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: For dependent concepts, we propose two novel approaches that exploit functional compositionality properties of image-generating processes. |
Tobias Leemann; Michael Kirchhof; Yao Rong; Enkelejda Kasneci; Gjergji Kasneci; |
| 2023 | 14 | Copula-based Deep Survival Models for Dependent Censoring IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper presents a parametric model of survival that extends modern non-linear survival analysis by relaxing the assumption of conditional independence. |
Ali Hossein Foomani Gharari; Michael Cooper; Russell Greiner; Rahul G Krishnan; |
| 2023 | 15 | Probabilistic Flow Circuits: Towards Unified Deep Models for Tractable Probabilistic Inference IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We consider the problem of increasing the expressivity of probabilistic circuits by augmenting them with the successful generative models of normalizing flows. |
Sahil Sidheekh; Kristian Kersting; Sriraam Natarajan; |
| 2022 | 1 | Bayesian Structure Learning with Generative Flow Networks IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose to use a GFlowNet as an alternative to MCMC for approximating the posterior distribution over the structure of Bayesian networks, given a dataset of observations. |
TRISTAN DELEU et. al. |
| 2022 | 2 | Multi-objective Bayesian Optimization Over High-dimensional Search Spaces IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work we propose MORBO, a scalable method for multi-objective BO over high-dimensional search spaces. |
Samuel Daulton; David Eriksson; Maximilian Balandat; Eytan Bakshy; |
| 2022 | 3 | On The Effectiveness of Adversarial Training Against Common Corruptions IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Then we explain why adversarial training performs better than data augmentation with simple Gaussian noise which has been observed to be a meaningful baseline on common corruptions. Related to this, we identify the sigma-overfitting phenomenon when Gaussian augmentation overfits to a particular standard deviation used for training which has a significant detrimental effect on common corruption accuracy. |
Klim Kireev; Maksym Andriushchenko; Nicolas Flammarion; |
| 2022 | 4 | Fedvarp: Tackling The Variance Due to Partial Client Participation in Federated Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We find that among these, only the former has received significant attention in the literature. To remedy this, we propose FedVARP, a novel variance reduction algorithm applied at the server that eliminates error due to partial client participation. |
Divyansh Jhunjhunwala; Pranay Sharma; Aushim Nagarkatti; Gauri Joshi; |
| 2022 | 5 | Greedy Relaxations of The Sparsest Permutation Algorithm IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We extend the methods of the latter by a permutation-based operation tuck, and develop a class of algorithms, namely GRaSP, that are computationally efficient and pointwise consistent under increasingly weaker assumptions than faithfulness. |
Wai-Yin Lam; Bryan Andrews; Joseph Ramsey; |
| 2022 | 6 | Data Augmentation in Bayesian Neural Networks and The Cold Posterior Effect IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a “finite orbit” setting which allows valid likelihoods to be computed exactly, and for the more usual “full orbit” setting we derive multi-sample bounds tighter than those used previously. |
SETH NABARRO et. al. |
| 2022 | 7 | Calibrated Ensembles Can Mitigate Accuracy Tradeoffs Under Distribution Shift IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we find that a simple approach of ensembling the standard and robust models, after calibrating on only ID data, outperforms prior state-of-the-art both ID and OOD. |
Ananya Kumar; Tengyu Ma; Percy Liang; Aditi Raghunathan; |
| 2022 | 8 | Stability of SGD: Tightness Analysis and Improved Bounds IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Stochastic Gradient Descent (SGD) based methods have been widely used for training large-scale machine learning models that also generalize well in practice. |
YIKAI ZHANG et. al. |
| 2022 | 9 | Quantification of Credal Uncertainty in Machine Learning: A Critical Analysis and Empirical Comparison IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we elaborate on uncertainty measures for credal sets from the perspective of machine learning. |
Eyke H�llermeier; S�bastien Destercke; Mohammad Hossein Shaker; |
| 2022 | 10 | Offline Reinforcement Learning Under Value and Density-ratio Realizability: The Power of Gaps IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We consider a challenging theoretical problem in offline reinforcement learning (RL): obtaining sample-efficiency guarantees with a dataset lacking sufficient coverage, under only realizability-type assumptions for the function approximators. |
Jinglin Chen; Nan Jiang; |
| 2022 | 11 | CounteRGAN: Generating Counterfactuals for Real-time Recourse and Interpretability Using Residual GANs IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Model interpretability, fairness, and recourse for end users have increased as machine learning models have become increasingly popular in areas including criminal justice, finance, healthcare, and job marketplaces. This work presents a novel method of addressing these issues by producing meaningful counterfactuals that are aimed at providing recourse to users and highlighting potential model biases. |
Daniel Nemirovsky; Nicolas Thiebaut; Ye Xu; Abhishek Gupta; |
| 2022 | 12 | Privacy-aware Compression for Federated Data Analysis IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we take a holistic look at the problem and design a family of privacy-aware compression mechanisms that work for any given communication budget. |
Kamalika Chaudhuri; Chuan Guo; Mike Rabbat; |
| 2022 | 13 | Learning Invariant Weights in Neural Networks IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a weight-space equivalent to this approach, by minimizing a lower bound on the marginal likelihood to learn invariances in neural networks, resulting in naturally higher performing models. |
Tycho F.A. van der Ouderaa; Mark van der Wilk; |
| 2022 | 14 | Physics Guided Neural Networks for Spatio-temporal Super-resolution of Turbulent Flows IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Reconstructing DNS from low-resolution LES is critical for large-scale simulation in many scientific and engineering disciplines, but it poses many challenges to existing super-resolution methods due to the complexity of turbulent flows and computational cost of generating frequent LES data. We propose a physics-guided neural network for reconstructing frequent DNS from sparse LES data by enhancing its spatial resolution and temporal frequency. |
TIANSHU BAO et. al. |
| 2022 | 15 | Neuro-symbolic Entropy Regularization IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Conversely, neuro-symbolic approaches exploit the knowledge that not every prediction corresponds to a valid structure in the output space. Yet, they do not further restrict the learned output distribution.This paper introduces a framework that unifies both approaches. |
Kareem Ahmed; Eric Wang; Kai-Wei Chang; Guy Van den Broeck; |
| 2021 | 1 | ReZero Is All You Need: Fast Convergence at Large Depth IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We apply this technique to language modeling and find that we can easily train 120-layer Transformers. |
Thomas Bachlechner; Bodhisattwa Prasad Majumder; Henry Mao; Gary Cottrell; Julian McAuley; |
| 2021 | 2 | Towards A Unified Framework for Fair and Stable Graph Representation Learning IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we establish a key connection between counterfactual fairness and stability and leverage it to propose a novel framework, NIFTY (uNIfying Fairness and stabiliTY), which can be used with any GNN to learn fair and stable representations. |
Chirag Agarwal; Himabindu Lakkaraju; Marinka Zitnik; |
| 2021 | 3 | High-dimensional Bayesian Optimization with Sparse Axis-aligned Subspaces IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In an extensive suite of experiments comparing to existing methods for high-dimensional BO we demonstrate that our algorithm, Sparse Axis-Aligned Subspace BO (SAASBO), achieves excellent performance on several synthetic and real-world problems without the need to set problem-specific hyperparameters. |
David Eriksson; Martin Jankowiak; |
| 2021 | 4 | The Promises and Pitfalls of Deep Kernel Learning IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: However, we identify pathological behavior, including overfitting, on a simple toy example. We explore this pathology, explaining its origins and considering how it applies to real datasets. |
Sebastian W. Ober; Carl E. Rasmussen; Mark van der Wilk; |
| 2021 | 5 | BayLIME: Bayesian Local Interpretable Model-agnostic Explanations IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we develop a novel Bayesian extension to the LIME framework, one of the most widely used approaches in XAI – which we call BayLIME. |
Xingyu Zhao; Wei Huang; Xiaowei Huang; Valentin Robu; David Flynn; |
| 2021 | 6 | TreeBERT: A Tree-based Pre-trained Model for Programming Language IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we present TreeBERT, a tree-based pre-trained model for improving programming language-oriented generation tasks. |
Xue Jiang; Zhuoran Zheng; Chen Lyu; Liang Li; Lei Lyu; |
| 2021 | 7 | Distribution-free Uncertainty Quantification for Classification Under Label Shift IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Piggybacking on recent progress in addressing label shift (for better prediction), we examine the right way to achieve UQ by reweighting the aforementioned conformal and calibration procedures whenever some unlabeled data from the target distribution is available. We examine these techniques theoretically in a distribution-free framework and demonstrate their excellent practical performance. |
Aleksandr Podkopaev; Aaditya Ramdas; |
| 2021 | 8 | Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In our work, we introduce a new symmetric integration scheme for split HMC that does not rely on stochastic gradients. |
Adam D. Cobb; Brian Jalaian; |
| 2021 | 9 | The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To mitigate this issue, it is commonly believed that more training data will eventually help such adversarially robust models generalize better on the benign/unperturbed test data. In this paper, however, we challenge this conventional belief and show that more training data can hurt the generalization of adversarially robust models in classification problems. |
Yifei Min; Lin Chen; Amin Karbasi; |
| 2021 | 10 | Local Explanations Via Necessity and Sufficiency: Unifying Theory and Practice IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Building on work in logic, probability, and causality, we establish the central role of necessity and sufficiency in XAI, unifying seemingly disparate methods in a single formal framework. We provide a sound and complete algorithm for computing explanatory factors with respect to a given context, and demonstrate its flexibility and competitive performance against state of the art alternatives on various tasks. |
David S. Watson; Limor Gultchin; Ankur Taly; Luciano Floridi; |
| 2021 | 11 | The Complexity of Nonconvex-strongly-concave Minimax Optimization IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our result reveals substantial gaps between these limits and best-known upper bounds in the literature. To close these gaps, we introduce a generic acceleration scheme that deploys existing gradient-based methods to solve a sequence of crafted strongly-convex-strongly-concave subproblems. |
Siqi Zhang; Junchi Yang; Crist�bal Guzm�n; Negar Kiyavash; Niao He; |
| 2021 | 12 | Measuring Data Leakage in Machine-learning Models with Fisher Information IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Consequently, when the training data contains sensitive attributes, assessing the amount of information leakage is paramount. We propose a method to quantify this leakage using the Fisher information of the model about the data. |
Awni Hannun; Chuan Guo; Laurens van der Maaten; |
| 2021 | 13 | Trumpets: Injective Flows for Inference and Inverse Problems IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose injective generative models called Trumpets that generalize invertible normalizing flows. |
Konik Kothari; AmirEhsan Khorashadizadeh; Maarten de Hoop; Ivan Dokmanic; |
| 2021 | 14 | Most: Multi-source Domain Adaptation Via Optimal Transport for Student-teacher Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: To this end, we propose in this paper a novel model for multi-source DA using the theory of optimal transport and imitation learning. |
TUAN NGUYEN et. al. |
| 2021 | 15 | Sketching Curvature for Efficient Out-of-distribution Detection for Deep Neural Networks IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Building on recent work leveraging the local curvature of DNNs to reason about epistemic uncertainty, we propose Sketching Curvature for OoD Detection (SCOD), an architecture-agnostic framework for equipping any trained DNN with a task-relevant epistemic uncertainty estimate. |
Apoorva Sharma; Navid Azizan; Marco Pavone; |
| 2020 | 1 | Discovering Contemporaneous And Lagged Causal Relations In Autocorrelated Nonlinear Time Series Datasets IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The paper introduces a novel conditional independence (CI) based method for linear and nonlinear, lagged and contemporaneous causal discovery from observational time series in the causally sufficient case. |
Jakob Runge; |
| 2020 | 2 | Adapting Text Embeddings For Causal Inference IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: This paper develops a method to estimate such causal effects from observational text data, adjusting for confounding features of the text such as the subject or writing quality. |
Victor Veitch; Dhanya Sridhar; David Blei; |
| 2020 | 3 | On Counterfactual Explanations Under Predictive Multiplicity IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we derive a general upper bound for the costs of counterfactual explanations under predictive multiplicity. |
Martin Pawelczyk; Klaus Broelemann; Gjergji. Kasneci; |
| 2020 | 4 | Greedy Policy Search: A Simple Baseline For Learnable Test-Time Augmentation IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: The primary goal of this paper is to demonstrate that test-time augmentation policies can be successfully learned too. |
Alexander Lyzhov; Yuliya Molchanova; Arsenii Ashukha; Dmitry Molchanov; Dmitry Vetrov; |
| 2020 | 5 | Permutation-Based Causal Structure Learning With Unknown Intervention Targets IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We consider the problem of estimating causal DAG models from a mix of observational and interventional data, when the intervention targets are partially or completely unknown. |
Chandler Squires; Yuhao Wang; Caroline Uhler; |
| 2020 | 6 | Q* Approximation Schemes For Batch Reinforcement Learning: A Theoretical Comparison IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We prove performance guarantees of two algorithms for approximating Q* in batch reinforcement learning. |
Tengyang Xie; Nan Jiang; |
| 2020 | 7 | Dueling Posterior Sampling For Preference-Based Reinforcement Learning IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Building upon ideas from preference-based bandit learning and posterior sampling in RL, we present DUELING POSTERIOR SAMPLING (DPS), which employs preference-based posterior sampling to learn both the system dynamics and the underlying utility function that governs the preference feedback. |
Ellen Novoseller; Yibing Wei; Yanan Sui; Yisong Yue; Joel Burdick; |
| 2020 | 8 | Verifying Individual Fairness In Machine Learning Models IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our objective is to construct verifiers for proving individual fairness of a given model, and we do so by considering appropriate relaxations of the problem. |
Philips George John; Deepak Vijaykeerthy; Diptikalyan Saha; |
| 2020 | 9 | Fair Contextual Multi-Armed Bandits: Theory And Experiments IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We introduce a Multi-Armed Bandit algorithm with fairness constraints, where fairness is defined as a minimum rate at which a task or a resource is assigned to a user. |
YIFANG CHEN et. al. |
| 2020 | 10 | Regret Analysis Of Bandit Problems With Causal Background Knowledge IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose two algorithms, causal upper confidence bound (C-UCB) and causal Thompson Sampling (C-TS), that enjoy improved cumulative regret bounds compared with algorithms that do not use causal information. |
Yangyi Lu; Amirhossein Meisami; Ambuj Tewari; William Yan; |
| 2020 | 11 | Probabilistic Safety For Bayesian Neural Networks IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We apply our methods to BNNs trained on a regression task, airborne collision avoidance, and MNIST, empirically showing that our approach allows one to certify probabilistic safety of BNNs with millions of parameters. |
Matthew Wicker; Luca Laurenti; Andrea Patane; Marta Kwiatkowska; |
| 2020 | 12 | Lagrangian Decomposition For Neural Network Verification IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a novel approach based on Lagrangian Decomposition. |
RUDY BUNEL et. al. |
| 2020 | 13 | Constraint-Based Causal Discovery Using Partial Ancestral Graphs In The Presence Of Cycles IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we show that—surprisingly—the output of the Fast Causal Inference (FCI) algorithm is correct if it is applied to observational data generated by a system that involves feedback. |
Joris M. Mooij; Tom Claassen; |
| 2020 | 14 | Identifying Causal Effects In Maximally Oriented Partially Directed Acyclic Graphs IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We develop a necessary and sufficient causal identification criterion for maximally oriented partially directed acyclic graphs (MPDAGs). |
Emilija Perkovic; |
| 2020 | 15 | Amortized Bayesian Optimization Over Discrete Spaces IF:3 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: Our key insight is that we can train a generative model to generate candidates that maximize the acquisition function. |
Kevin Swersky; Yulia Rubanova; David Dohan; Kevin Murphy; |
| 2019 | 1 | Random Search And Reproducibility For Neural Architecture Search IF:7 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In order to help ground the empirical results in this field, we propose new NAS baselines that build off the following observations: (i) NAS is a specialized hyperparameter optimization problem; and (ii) random search is a competitive baseline for hyperparameter optimization. |
Liam Li; Ameet Talwalkar; |
| 2019 | 2 | Sliced Score Matching: A Scalable Approach To Density And Score Estimation IF:6 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We show this difficulty can be mitigated by projecting the scores onto random vectors before comparing them. |
Yang Song; Sahaj Garg; Jiaxin Shi; Stefano Ermon; |
| 2019 | 3 | Fall Of Empires: Breaking Byzantine-tolerant SGD By Inner Product Manipulation IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we break two prevailing Byzantine-tolerant techniques. |
Cong Xie; Oluwasanmi Koyejo; Indranil Gupta; |
| 2019 | 4 | N-GCN: Multi-scale Graph Convolution For Semi-supervised Node Classification IF:5 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose a model: Network of GCNs (N-GCN), which marries these two lines of work. |
Sami Abu-El-Haija; Amol Kapoor; Bryan Perozzi; Joonseok Lee; |
| 2019 | 5 | Wasserstein Fair Classification IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose an approach to fair classification that enforces independence between the classifier outputs and sensitive information by minimizing Wasserstein-1 distances. |
Ray Jiang; Aldo Pacchiano; Tom Stepleton; Heinrich Jiang; Silvia Chiappa; |
| 2019 | 6 | Low Frequency Adversarial Perturbation IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper we propose to restrict the search for adversarial images to a low frequency domain. |
Chuan Guo; Jared S. Frank; Kilian Q. Weinberger; |
| 2019 | 7 | A Fast Proximal Point Method For Computing Exact Wasserstein Distance IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We address this challenge by developing an Inexact Proximal point method for exact Optimal Transport problem (IPOT) with the proximal operator approximately evaluated at each iteration using projections to the probability simplex. |
Yujia Xie; Xiangfeng Wang; Ruijia Wang; Hongyuan Zha; |
| 2019 | 8 | Subspace Inference For Bayesian Deep Learning IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we construct low-dimensional subspaces of parameter space, such as the first principal components of the stochastic gradient descent (SGD) trajectory, which contain diverse sets of high performing models. |
PAVEL IZMAILOV et. al. |
| 2019 | 9 | Practical Multi-fidelity Bayesian Optimization For Hyperparameter Tuning IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose a highly flexible and practical approach to multi-fidelity Bayesian optimization, focused on efficiently optimizing hyperparameters for iteratively trained supervised learning models. |
Jian Wu; Saul Toscano-Palmerin; Peter I. Frazier; Andrew Gordon Wilson; |
| 2019 | 10 | A Flexible Framework For Multi-Objective Bayesian Optimization Using Random Scalarizations IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this work, we propose a strategy based on random scalarizations of the objectives that addresses this problem. |
Biswajit Paria; Kirthevasan Kandasamy; Barnab�s P�czos; |
| 2019 | 11 | Truly Proximal Policy Optimization IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we show that PPO could neither strictly restrict the probability ratio as it attempts to do nor enforce a well-defined trust region constraint, which means that it may still suffer from the risk of performance instability. |
Yuhui Wang; Hao He; Xiaoyang Tan; |
| 2019 | 12 | Block Neural Autoregressive Flow IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We propose block neural autoregressive flow (B-NAF), a much more compact universal approximator of density functions, where we model a bijection directly using a single feed-forward network. |
Nicola De Cao; Wilker Aziz; Ivan Titov; |
| 2019 | 13 | Random Sum-Product Networks: A Simple And Effective Approach To Probabilistic Deep Learning IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we follow a simple “deep learning” approach, by generating unspecialized random structures, scalable to millions of parameters, and subsequently applying GPU-based optimization. |
ROBERT PEHARZ et. al. |
| 2019 | 14 | Deep Mixture Of Experts Via Shallow Embedding IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: We explore a mixture of experts (MoE) approach to deep dynamic routing, which activates certain experts in the network on a per-example basis. |
XIN WANG et. al. |
| 2019 | 15 | Domain Generalization Via Multidomain Discriminant Analysis IF:4 Related Papers Related Patents Related Grants Related Venues Related Experts View Highlight: In this paper, we propose Multidomain Discriminant Analysis (MDA) to address DG of classification tasks in general situations. |
Shoubo Hu; Kun Zhang; Zhitang Chen; Laiwan Chan; |