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Pradeep Ravikumar (CMU) on A Parallel Primal-Dual Sparse Method for Extreme Classification
Pradeep Ravikumar
Fri Dec 08 01:30 PM -- 02:00 PM (PST) @
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Pradeep Ravikumar (Carnegie Mellon University)
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2022 : Domain-Adjusted Regression or: ERM May Already Learn Features Sufficient for Out-of-Distribution Generalization »
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2023 Poster: Responsible AI (RAI) Games and Ensembles »
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2023 Poster: Global Optimality in Bivariate Gradient-based DAG Learning »
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2022 Spotlight: Identifiability of deep generative models without auxiliary information »
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2022 Workshop: Human in the Loop Learning (HiLL) Workshop at NeurIPS 2022 »
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2022 Poster: DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization »
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2022 Poster: Identifiability of deep generative models without auxiliary information »
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2022 Poster: Masked Prediction: A Parameter Identifiability View »
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2021 Poster: Learning latent causal graphs via mixture oracles »
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2021 Poster: When Is Generalizable Reinforcement Learning Tractable? »
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2020 Poster: On Learning Ising Models under Huber's Contamination Model »
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2020 Poster: On Completeness-aware Concept-Based Explanations in Deep Neural Networks »
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2020 Poster: Generalized Boosting »
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2019 Poster: On the (In)fidelity and Sensitivity of Explanations »
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2019 Poster: On Human-Aligned Risk Minimization »
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2019 Poster: Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation »
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2019 Poster: Game Design for Eliciting Distinguishable Behavior »
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2018 Poster: The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models »
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2018 Poster: Connecting Optimization and Regularization Paths »
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2018 Poster: DAGs with NO TEARS: Continuous Optimization for Structure Learning »
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2018 Poster: MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization »
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2018 Poster: Representer Point Selection for Explaining Deep Neural Networks »
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2017 Poster: The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities »
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2017 Poster: On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models »
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2016 Poster: Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain »
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2015 Poster: Fast Classification Rates for High-dimensional Gaussian Generative Models »
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2015 Poster: Collaborative Filtering with Graph Information: Consistency and Scalable Methods »
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2015 Poster: Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs »
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2015 Poster: Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent »
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2015 Poster: Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial »
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2015 Poster: Consistent Multilabel Classification »
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2015 Poster: Closed-form Estimators for High-dimensional Generalized Linear Models »
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2014 Poster: QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models »
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2014 Poster: Consistent Binary Classification with Generalized Performance Metrics »
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2014 Poster: Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space »
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2014 Poster: Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings »
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2012 Workshop: Discrete Optimization in Machine Learning (DISCML): Structure and Scalability »
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2011 Workshop: Discrete Optimization in Machine Learning (DISCML): Uncertainty, Generalization and Feedback »
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2011 Poster: Greedy Algorithms for Structurally Constrained High Dimensional Problems »
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2010 Workshop: Discrete Optimization in Machine Learning: Structures, Algorithms and Applications »
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2010 Session: Oral Session 14 »
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2009 Poster: Information-theoretic lower bounds on the oracle complexity of convex optimization »
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2009 Poster: A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers »
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2009 Oral: A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers »
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2008 Poster: Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images »
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