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PAC-Bayesian Bounds on Rate-Efficient Classifiers | https://proceedings.mlr.press/v162/abbas22a.html | [
"Alhabib Abbas",
"Yiannis Andreopoulos"
] | null | null | We derive analytic bounds on the noise invariance of majority vote classifiers operating on compressed inputs. Specifically, starting from recent bounds on the true risk of majority vote classifiers, we extend the applicability of PAC-Bayesian theory to quantify the resilience of majority votes to input noise stemming ... | [] | null | 1 | null | null | [
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Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning | https://proceedings.mlr.press/v162/abbas22b.html | [
"Momin Abbas",
"Quan Xiao",
"Lisha Chen",
"Pin-Yu Chen",
"Tianyi Chen"
] | null | null | Model-agnostic meta learning (MAML) is currently one of the dominating approaches for few-shot meta-learning. Albeit its effectiveness, the optimization of MAML can be challenging due to the innate bilevel problem structure. Specifically, the loss landscape of MAML is much more complex with possibly more saddle points... | [] | null | 2 | 2206.03996 | title_snapshot | [
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An Initial Alignment between Neural Network and Target is Needed for Gradient Descent to Learn | https://proceedings.mlr.press/v162/abbe22a.html | [
"Emmanuel Abbe",
"Elisabetta Cornacchia",
"Jan Hazla",
"Christopher Marquis"
] | null | null | This paper introduces the notion of “Initial Alignment” (INAL) between a neural network at initialization and a target function. It is proved that if a network and a Boolean target function do not have a noticeable INAL, then noisy gradient descent with normalized i.i.d. initialization will not learn in polynomial time... | [] | null | 3 | 2202.12846 | title_snapshot | [
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Active Sampling for Min-Max Fairness | https://proceedings.mlr.press/v162/abernethy22a.html | [
"Jacob D Abernethy",
"Pranjal Awasthi",
"Matthäus Kleindessner",
"Jamie Morgenstern",
"Chris Russell",
"Jie Zhang"
] | null | null | We propose simple active sampling and reweighting strategies for optimizing min-max fairness that can be applied to any classification or regression model learned via loss minimization. The key intuition behind our approach is to use at each timestep a datapoint from the group that is worst off under the current model ... | [] | null | 4 | 2006.06879 | title_snapshot | [
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Meaningfully debugging model mistakes using conceptual counterfactual explanations | https://proceedings.mlr.press/v162/abid22a.html | [
"Abubakar Abid",
"Mert Yuksekgonul",
"James Zou"
] | null | null | Understanding and explaining the mistakes made by trained models is critical to many machine learning objectives, such as improving robustness, addressing concept drift, and mitigating biases. However, this is often an ad hoc process that involves manually looking at the model’s mistakes on many test samples and guessi... | [] | null | 5 | 2106.12723 | title_snapshot | [
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Batched Dueling Bandits | https://proceedings.mlr.press/v162/agarwal22a.html | [
"Arpit Agarwal",
"Rohan Ghuge",
"Viswanath Nagarajan"
] | null | null | The K-armed dueling bandit problem, where the feedback is in the form of noisy pairwise comparisons, has been widely studied. Previous works have only focused on the sequential setting where the policy adapts after every comparison. However, in many applications such as search ranking and recommendation systems, it is ... | [] | null | 6 | 2202.10660 | title_snapshot | [
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Hierarchical Shrinkage: Improving the accuracy and interpretability of tree-based models. | https://proceedings.mlr.press/v162/agarwal22b.html | [
"Abhineet Agarwal",
"Yan Shuo Tan",
"Omer Ronen",
"Chandan Singh",
"Bin Yu"
] | null | null | Decision trees and random forests (RF) are a cornerstone of modern machine learning practice. Due to their tendency to overfit, trees are typically regularized by a variety of techniques that modify their structure (e.g. pruning). We introduce Hierarchical Shrinkage (HS), a post-hoc algorithm which regularizes the tree... | [] | null | 7 | 2202.00858 | title_judge | [
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Deep equilibrium networks are sensitive to initialization statistics | https://proceedings.mlr.press/v162/agarwala22a.html | [
"Atish Agarwala",
"Samuel S Schoenholz"
] | null | null | Deep equilibrium networks (DEQs) are a promising way to construct models which trade off memory for compute. However, theoretical understanding of these models is still lacking compared to traditional networks, in part because of the repeated application of a single set of weights. We show that DEQs are sensitive to th... | [] | null | 8 | 2207.09432 | title_snapshot | [
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Learning of Cluster-based Feature Importance for Electronic Health Record Time-series | https://proceedings.mlr.press/v162/aguiar22a.html | [
"Henrique Aguiar",
"Mauro Santos",
"Peter Watkinson",
"Tingting Zhu"
] | null | null | The recent availability of Electronic Health Records (EHR) has allowed for the development of algorithms predicting inpatient risk of deterioration and trajectory evolution. However, prediction of disease progression with EHR is challenging since these data are sparse, heterogeneous, multi-dimensional, and multi-modal ... | [] | null | 9 | null | null | [
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On the Convergence of the Shapley Value in Parametric Bayesian Learning Games | https://proceedings.mlr.press/v162/agussurja22a.html | [
"Lucas Agussurja",
"Xinyi Xu",
"Bryan Kian Hsiang Low"
] | null | null | Measuring contributions is a classical problem in cooperative game theory where the Shapley value is the most well-known solution concept. In this paper, we establish the convergence property of the Shapley value in parametric Bayesian learning games where players perform a Bayesian inference using their combined data,... | [] | null | 10 | 2205.07428 | title_snapshot | [
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Individual Preference Stability for Clustering | https://proceedings.mlr.press/v162/ahmadi22a.html | [
"Saba Ahmadi",
"Pranjal Awasthi",
"Samir Khuller",
"Matthäus Kleindessner",
"Jamie Morgenstern",
"Pattara Sukprasert",
"Ali Vakilian"
] | null | null | In this paper, we propose a natural notion of individual preference (IP) stability for clustering, which asks that every data point, on average, is closer to the points in its own cluster than to the points in any other cluster. Our notion can be motivated from several perspectives, including game theory and algorithmi... | [] | null | 11 | 2207.03600 | title_snapshot | [
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Understanding the unstable convergence of gradient descent | https://proceedings.mlr.press/v162/ahn22a.html | [
"Kwangjun Ahn",
"Jingzhao Zhang",
"Suvrit Sra"
] | null | null | Most existing analyses of (stochastic) gradient descent rely on the condition that for $L$-smooth costs, the step size is less than $2/L$. However, many works have observed that in machine learning applications step sizes often do not fulfill this condition, yet (stochastic) gradient descent still converges, albeit in ... | [] | null | 12 | 2204.01050 | title_snapshot | [
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Minimum Cost Intervention Design for Causal Effect Identification | https://proceedings.mlr.press/v162/akbari22a.html | [
"Sina Akbari",
"Jalal Etesami",
"Negar Kiyavash"
] | null | null | Pearl’s do calculus is a complete axiomatic approach to learn the identifiable causal effects from observational data. When such an effect is not identifiable, it is necessary to perform a collection of often costly interventions in the system to learn the causal effect. In this work, we consider the problem of designi... | [] | null | 13 | null | null | [
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How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models | https://proceedings.mlr.press/v162/alaa22a.html | [
"Ahmed Alaa",
"Boris Van Breugel",
"Evgeny S. Saveliev",
"Mihaela van der Schaar"
] | null | null | Devising domain- and model-agnostic evaluation metrics for generative models is an important and as yet unresolved problem. Most existing metrics, which were tailored solely to the image synthesis setup, exhibit a limited capacity for diagnosing the different modes of failure of generative models across broader applica... | [] | null | 14 | 2102.08921 | title_snapshot | [
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A Natural Actor-Critic Framework for Zero-Sum Markov Games | https://proceedings.mlr.press/v162/alacaoglu22a.html | [
"Ahmet Alacaoglu",
"Luca Viano",
"Niao He",
"Volkan Cevher"
] | null | null | We introduce algorithms based on natural actor-critic and analyze their sample complexity for solving two player zero-sum Markov games in the tabular case. Our results improve the best-known sample complexities of policy gradient/actor-critic methods for convergence to Nash equilibrium in the multi-agent setting. We us... | [] | null | 15 | null | null | [
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Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations | https://proceedings.mlr.press/v162/alam22a.html | [
"Mohammad Mahmudul Alam",
"Edward Raff",
"Tim Oates",
"James Holt"
] | null | null | Due to the computational cost of running inference for a neural network, the need to deploy the inferential steps on a third party’s compute environment or hardware is common. If the third party is not fully trusted, it is desirable to obfuscate the nature of the inputs and outputs, so that the third party can not easi... | [] | null | 16 | 2206.05893 | title_snapshot | [
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Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer | https://proceedings.mlr.press/v162/alegre22a.html | [
"Lucas Nunes Alegre",
"Ana Bazzan",
"Bruno C. Da Silva"
] | null | null | In many real-world applications, reinforcement learning (RL) agents might have to solve multiple tasks, each one typically modeled via a reward function. If reward functions are expressed linearly, and the agent has previously learned a set of policies for different tasks, successor features (SFs) can be exploited to c... | [] | null | 17 | 2206.11326 | title_snapshot | [
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Structured Stochastic Gradient MCMC | https://proceedings.mlr.press/v162/alexos22a.html | [
"Antonios Alexos",
"Alex J Boyd",
"Stephan Mandt"
] | null | null | Stochastic gradient Markov Chain Monte Carlo (SGMCMC) is a scalable algorithm for asymptotically exact Bayesian inference in parameter-rich models, such as Bayesian neural networks. However, since mixing can be slow in high dimensions, practitioners often resort to variational inference (VI). Unfortunately, VI makes st... | [] | null | 18 | 2107.09028 | title_snapshot | [
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XAI for Transformers: Better Explanations through Conservative Propagation | https://proceedings.mlr.press/v162/ali22a.html | [
"Ameen Ali",
"Thomas Schnake",
"Oliver Eberle",
"Grégoire Montavon",
"Klaus-Robert Müller",
"Lior Wolf"
] | null | null | Transformers have become an important workhorse of machine learning, with numerous applications. This necessitates the development of reliable methods for increasing their transparency. Multiple interpretability methods, often based on gradient information, have been proposed. We show that the gradient in a Transformer... | [] | null | 19 | 2202.07304 | title_snapshot | [
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RUMs from Head-to-Head Contests | https://proceedings.mlr.press/v162/almanza22a.html | [
"Matteo Almanza",
"Flavio Chierichetti",
"Ravi Kumar",
"Alessandro Panconesi",
"Andrew Tomkins"
] | null | null | Random utility models (RUMs) encode the likelihood that a particular item will be selected from a slate of competing items. RUMs are well-studied objects in both discrete choice theory and, more recently, in the machine learning community, as they encode a fairly broad notion of rational user behavior. In this paper, w... | [] | null | 20 | null | null | [
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Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval | https://proceedings.mlr.press/v162/alon22a.html | [
"Uri Alon",
"Frank Xu",
"Junxian He",
"Sudipta Sengupta",
"Dan Roth",
"Graham Neubig"
] | null | null | Retrieval-based language models (R-LM) model the probability of natural language text by combining a standard language model (LM) with examples retrieved from an external datastore at test time. While effective, a major bottleneck of using these models in practice is the computationally costly datastore search, which c... | [] | null | 21 | 2201.12431 | title_snapshot | [
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Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees | https://proceedings.mlr.press/v162/alvarez22a.html | [
"Verónica Álvarez",
"Santiago Mazuelas",
"Jose A Lozano"
] | null | null | The statistical characteristics of instance-label pairs often change with time in practical scenarios of supervised classification. Conventional learning techniques adapt to such concept drift accounting for a scalar rate of change by means of a carefully chosen learning rate, forgetting factor, or window size. However... | [] | null | 22 | 2205.15942 | title_snapshot | [
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Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation | https://proceedings.mlr.press/v162/ament22a.html | [
"Sebastian E Ament",
"Carla P Gomes"
] | null | null | Bayesian Optimization (BO) has shown great promise for the global optimization of functions that are expensive to evaluate, but despite many successes, standard approaches can struggle in high dimensions. To improve the performance of BO, prior work suggested incorporating gradient information into a Gaussian process s... | [] | null | 23 | 2206.08366 | title_snapshot | [
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Public Data-Assisted Mirror Descent for Private Model Training | https://proceedings.mlr.press/v162/amid22a.html | [
"Ehsan Amid",
"Arun Ganesh",
"Rajiv Mathews",
"Swaroop Ramaswamy",
"Shuang Song",
"Thomas Steinke",
"Thomas Steinke",
"Vinith M Suriyakumar",
"Om Thakkar",
"Abhradeep Thakurta"
] | null | null | In this paper, we revisit the problem of using in-distribution public data to improve the privacy/utility trade-offs for differentially private (DP) model training. (Here, public data refers to auxiliary data sets that have no privacy concerns.) We design a natural variant of DP mirror descent, where the DP gradients o... | [] | null | 24 | 2112.00193 | title_snapshot | [
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On Last-Iterate Convergence Beyond Zero-Sum Games | https://proceedings.mlr.press/v162/anagnostides22a.html | [
"Ioannis Anagnostides",
"Ioannis Panageas",
"Gabriele Farina",
"Tuomas Sandholm"
] | null | null | Most existing results about last-iterate convergence of learning dynamics are limited to two-player zero-sum games, and only apply under rigid assumptions about what dynamics the players follow. In this paper we provide new results and techniques that apply to broader families of games and learning dynamics. First, we ... | [] | null | 25 | 2203.12056 | title_snapshot | [
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Online Algorithms with Multiple Predictions | https://proceedings.mlr.press/v162/anand22a.html | [
"Keerti Anand",
"Rong Ge",
"Amit Kumar",
"Debmalya Panigrahi"
] | null | null | This paper studies online algorithms augmented withmultiplemachine-learned predictions. We give a generic algorithmic framework for online covering problems with multiple predictions that obtains an online solution that is competitive against the performance of thebestsolution obtained from the predictions. Our algorit... | [] | null | 26 | 2205.03921 | title_snapshot | [
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Learning to Hash Robustly, Guaranteed | https://proceedings.mlr.press/v162/andoni22a.html | [
"Alexandr Andoni",
"Daniel Beaglehole"
] | null | null | The indexing algorithms for the high-dimensional nearest neighbor search (NNS) with the best worst-case guarantees are based on the randomized Locality Sensitive Hashing (LSH), and its derivatives. In practice, many heuristic approaches exist to "learn" the best indexing method in order to speed-up NNS, crucially adapt... | [] | null | 27 | 2108.05433 | title_snapshot | [
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Set Based Stochastic Subsampling | https://proceedings.mlr.press/v162/andreis22a.html | [
"Bruno Andreis",
"Seanie Lee",
"A. Tuan Nguyen",
"Juho Lee",
"Eunho Yang",
"Sung Ju Hwang"
] | null | null | Deep models are designed to operate on huge volumes of high dimensional data such as images. In order to reduce the volume of data these models must process, we propose a set-based two-stage end-to-end neural subsampling model that is jointly optimized with anarbitrarydownstream task network (e.g. classifier). In the f... | [] | null | 28 | 2006.14222 | title_snapshot | [
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Towards Understanding Sharpness-Aware Minimization | https://proceedings.mlr.press/v162/andriushchenko22a.html | [
"Maksym Andriushchenko",
"Nicolas Flammarion"
] | null | null | Sharpness-Aware Minimization (SAM) is a recent training method that relies on worst-case weight perturbations which significantly improves generalization in various settings. We argue that the existing justifications for the success of SAM which are based on a PAC-Bayes generalization bound and the idea of convergence ... | [] | null | 29 | 2206.06232 | title_snapshot | [
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Fair and Fast k-Center Clustering for Data Summarization | https://proceedings.mlr.press/v162/angelidakis22a.html | [
"Haris Angelidakis",
"Adam Kurpisz",
"Leon Sering",
"Rico Zenklusen"
] | null | null | We consider two key issues faced by many clustering methods when used for data summarization, namely (a) an unfair representation of "demographic groups” and (b) distorted summarizations, where data points in the summary represent subsets of the original data of vastly different sizes. Previous work made important step... | [] | null | 30 | null | null | [
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Interactive Correlation Clustering with Existential Cluster Constraints | https://proceedings.mlr.press/v162/angell22a.html | [
"Rico Angell",
"Nicholas Monath",
"Nishant Yadav",
"Andrew Mccallum"
] | null | null | We consider the problem of clustering with user feedback. Existing methods express constraints about the input data points, most commonly through must-link and cannot-link constraints on data point pairs. In this paper, we introduce existential cluster constraints: a new form of feedback where users indicate the featur... | [] | null | 31 | null | null | [
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Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging | https://proceedings.mlr.press/v162/angelopoulos22a.html | [
"Anastasios N Angelopoulos",
"Amit Pal Kohli",
"Stephen Bates",
"Michael Jordan",
"Jitendra Malik",
"Thayer Alshaabi",
"Srigokul Upadhyayula",
"Yaniv Romano"
] | null | null | Image-to-image regression is an important learning task, used frequently in biological imaging. Current algorithms, however, do not generally offer statistical guarantees that protect against a model’s mistakes and hallucinations. To address this, we develop uncertainty quantification techniques with rigorous statistic... | [] | null | 32 | 2202.05265 | title_snapshot | [
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AdaGrad Avoids Saddle Points | https://proceedings.mlr.press/v162/antonakopoulos22a.html | [
"Kimon Antonakopoulos",
"Panayotis Mertikopoulos",
"Georgios Piliouras",
"Xiao Wang"
] | null | null | Adaptive first-order methods in optimization have widespread ML applications due to their ability to adapt to non-convex landscapes. However, their convergence guarantees are typically stated in terms of vanishing gradient norms, which leaves open the issue of converging to undesirable saddle points (or even local maxi... | [] | null | 33 | null | null | [
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UnderGrad: A Universal Black-Box Optimization Method with Almost Dimension-Free Convergence Rate Guarantees | https://proceedings.mlr.press/v162/antonakopoulos22b.html | [
"Kimon Antonakopoulos",
"Dong Quan Vu",
"Volkan Cevher",
"Kfir Levy",
"Panayotis Mertikopoulos"
] | null | null | Universal methods achieve optimal convergence rate guarantees in convex optimization without any prior knowledge of the problem’s regularity parameters or the attributes of the gradient oracle employed by the method. In this regard, existing state-of-the-art algorithms achieve an $O(1/T^2)$ convergence rate in Lipschit... | [] | null | 34 | 2206.09352 | title_judge | [
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Adapting the Linearised Laplace Model Evidence for Modern Deep Learning | https://proceedings.mlr.press/v162/antoran22a.html | [
"Javier Antoran",
"David Janz",
"James U Allingham",
"Erik Daxberger",
"Riccardo Rb Barbano",
"Eric Nalisnick",
"Jose Miguel Hernandez-Lobato"
] | null | null | The linearised Laplace method for estimating model uncertainty has received renewed attention in the Bayesian deep learning community. The method provides reliable error bars and admits a closed-form expression for the model evidence, allowing for scalable selection of model hyperparameters. In this work, we examine th... | [] | null | 35 | 2206.08900 | title_snapshot | [
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EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning | https://proceedings.mlr.press/v162/ao22a.html | [
"Shuang Ao",
"Tianyi Zhou",
"Jing Jiang",
"Guodong Long",
"Xuan Song",
"Chengqi Zhang"
] | null | null | Reinforcement learning (RL) is inefficient on long-horizon tasks due to sparse rewards and its policy can be fragile to slightly perturbed environments. We address these challenges via a curriculum of tasks with coupled environments, generated by two policies trained jointly with RL: (1) a co-operative planning policy ... | [] | null | 36 | null | null | [
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Online Balanced Experimental Design | https://proceedings.mlr.press/v162/arbour22a.html | [
"David Arbour",
"Drew Dimmery",
"Tung Mai",
"Anup Rao"
] | null | null | We consider the experimental design problem in an online environment, an important practical task for reducing the variance of estimates in randomized experiments which allows for greater precision, and in turn, improved decision making. In this work, we present algorithms that build on recent advances in online discre... | [] | null | 37 | 2203.02025 | title_snapshot | [
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VariGrow: Variational Architecture Growing for Task-Agnostic Continual Learning based on Bayesian Novelty | https://proceedings.mlr.press/v162/ardywibowo22a.html | [
"Randy Ardywibowo",
"Zepeng Huo",
"Zhangyang Wang",
"Bobak J Mortazavi",
"Shuai Huang",
"Xiaoning Qian"
] | null | null | Continual Learning (CL) is the problem of sequentially learning a set of tasks and preserving all the knowledge acquired. Many existing methods assume that the data stream is explicitly divided into a sequence of known contexts (tasks), and use this information to know when to transfer knowledge from one context to ano... | [] | null | 38 | null | null | [
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Thresholded Lasso Bandit | https://proceedings.mlr.press/v162/ariu22a.html | [
"Kaito Ariu",
"Kenshi Abe",
"Alexandre Proutiere"
] | null | null | In this paper, we revisit the regret minimization problem in sparse stochastic contextual linear bandits, where feature vectors may be of large dimension $d$, but where the reward function depends on a few, say $s_0\ll d$, of these features only. We present Thresholded Lasso bandit, an algorithm that (i) estimates the ... | [] | null | 39 | 2010.11994 | title_snapshot | [
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Gradient Based Clustering | https://proceedings.mlr.press/v162/armacki22a.html | [
"Aleksandar Armacki",
"Dragana Bajovic",
"Dusan Jakovetic",
"Soummya Kar"
] | null | null | We propose a general approach for distance based clustering, using the gradient of the cost function that measures clustering quality with respect to cluster assignments and cluster center positions. The approach is an iterative two step procedure (alternating between cluster assignment and cluster center updates) and ... | [] | null | 40 | 2202.00720 | title_snapshot | [
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Understanding Gradient Descent on the Edge of Stability in Deep Learning | https://proceedings.mlr.press/v162/arora22a.html | [
"Sanjeev Arora",
"Zhiyuan Li",
"Abhishek Panigrahi"
] | null | null | Deep learning experiments by \citet{cohen2021gradient} using deterministic Gradient Descent (GD) revealed anEdge of Stability (EoS)phase when learning rate (LR) and sharpness (i.e., the largest eigenvalue of Hessian) no longer behave as in traditional optimization. Sharpness stabilizes around $2/$LR and loss goes up an... | [] | null | 41 | 2205.09745 | title_judge | [
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Private optimization in the interpolation regime: faster rates and hardness results | https://proceedings.mlr.press/v162/asi22a.html | [
"Hilal Asi",
"Karan Chadha",
"Gary Cheng",
"John Duchi"
] | null | null | In non-private stochastic convex optimization, stochastic gradient methods converge much faster on interpolation problems—namely, problems where there exists a solution that simultaneously minimizes all of the sample losses—than on non-interpolating ones; similar improvements are not known in the private setting. In th... | [] | null | 42 | 2210.17070 | title_snapshot | [
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Optimal Algorithms for Mean Estimation under Local Differential Privacy | https://proceedings.mlr.press/v162/asi22b.html | [
"Hilal Asi",
"Vitaly Feldman",
"Kunal Talwar"
] | null | null | We study the problem of mean estimation of $\ell_2$-bounded vectors under the constraint of local differential privacy. While the literature has a variety of algorithms that achieve the (asymptotic) optimal rates for this problem, the performance of these algorithms in practice can vary significantly due to varying (an... | [] | null | 43 | 2205.02466 | title_snapshot | [
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Asymptotically-Optimal Gaussian Bandits with Side Observations | https://proceedings.mlr.press/v162/atsidakou22a.html | [
"Alexia Atsidakou",
"Orestis Papadigenopoulos",
"Constantine Caramanis",
"Sujay Sanghavi",
"Sanjay Shakkottai"
] | null | null | We study the problem of Gaussian bandits with general side information, as first introduced by Wu, Szepesvári, and György. In this setting, the play of an arm reveals information about other arms, according to an arbitrarya prioriknownside informationmatrix: each element of this matrix encodes the fidelity of the infor... | [] | null | 44 | 2505.10698 | title_snapshot | [
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Congested Bandits: Optimal Routing via Short-term Resets | https://proceedings.mlr.press/v162/awasthi22a.html | [
"Pranjal Awasthi",
"Kush Bhatia",
"Sreenivas Gollapudi",
"Kostas Kollias"
] | null | null | For traffic routing platforms, the choice of which route to recommend to a user depends on the congestion on these routes – indeed, an individual’s utility depends on the number of people using the recommended route at that instance. Motivated by this, we introduce the problem of Congested Bandits where each arm’s rewa... | [] | null | 45 | 2301.09251 | title_snapshot | [
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Do More Negative Samples Necessarily Hurt In Contrastive Learning? | https://proceedings.mlr.press/v162/awasthi22b.html | [
"Pranjal Awasthi",
"Nishanth Dikkala",
"Pritish Kamath"
] | null | null | Recent investigations in noise contrastive estimation suggest, both empirically as well as theoretically, that while having more “negative samples” in the contrastive loss improves downstream classification performance initially, beyond a threshold, it hurts downstream performance due to a “collision-coverage” trade-of... | [] | null | 46 | 2205.01789 | title_snapshot | [
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H-Consistency Bounds for Surrogate Loss Minimizers | https://proceedings.mlr.press/v162/awasthi22c.html | [
"Pranjal Awasthi",
"Anqi Mao",
"Mehryar Mohri",
"Yutao Zhong"
] | null | null | We present a detailed study of estimation errors in terms of surrogate loss estimation errors. We refer to such guarantees as H-consistency bounds, since they account for the hypothesis set H adopted. These guarantees are significantly stronger than H-calibration or H-consistency. They are also more informative than si... | [] | null | 47 | 2205.08017 | title_judge | [
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Iterative Hard Thresholding with Adaptive Regularization: Sparser Solutions Without Sacrificing Runtime | https://proceedings.mlr.press/v162/axiotis22a.html | [
"Kyriakos Axiotis",
"Maxim Sviridenko"
] | null | null | We propose a simple modification to the iterative hard thresholding (IHT) algorithm, which recovers asymptotically sparser solutions as a function of the condition number. When aiming to minimize a convex function f(x) with condition number $\kappa$ subject to x being an s-sparse vector, the standard IHT guarantee is a... | [] | null | 48 | 2204.08274 | title_snapshot | [
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Proving Theorems using Incremental Learning and Hindsight Experience Replay | https://proceedings.mlr.press/v162/aygun22a.html | [
"Eser Aygün",
"Ankit Anand",
"Laurent Orseau",
"Xavier Glorot",
"Stephen M Mcaleer",
"Vlad Firoiu",
"Lei M Zhang",
"Doina Precup",
"Shibl Mourad"
] | null | null | Traditional automated theorem proving systems for first-order logic depend on speed-optimized search and many handcrafted heuristics designed to work over a wide range of domains. Machine learning approaches in the literature either depend on these traditional provers to bootstrap themselves, by leveraging these heuris... | [] | null | 49 | 2112.10664 | title_snapshot | [
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Near-optimal rate of consistency for linear models with missing values | https://proceedings.mlr.press/v162/ayme22a.html | [
"Alexis Ayme",
"Claire Boyer",
"Aymeric Dieuleveut",
"Erwan Scornet"
] | null | null | Missing values arise in most real-world data sets due to the aggregation of multiple sources and intrinsically missing information (sensor failure, unanswered questions in surveys...). In fact, the very nature of missing values usually prevents us from running standard learning algorithms. In this paper, we focus on th... | [] | null | 50 | 2202.01463 | title_judge | [
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How Tempering Fixes Data Augmentation in Bayesian Neural Networks | https://proceedings.mlr.press/v162/bachmann22a.html | [
"Gregor Bachmann",
"Lorenzo Noci",
"Thomas Hofmann"
] | null | null | While Bayesian neural networks (BNNs) provide a sound and principled alternative to standard neural networks, an artificial sharpening of the posterior usually needs to be applied to reach comparable performance. This is in stark contrast to theory, dictating that given an adequate prior and a well-specified model, the... | [] | null | 51 | 2205.13900 | title_snapshot | [
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ASAP.SGD: Instance-based Adaptiveness to Staleness in Asynchronous SGD | https://proceedings.mlr.press/v162/backstrom22a.html | [
"Karl Bäckström",
"Marina Papatriantafilou",
"Philippas Tsigas"
] | null | null | Concurrent algorithmic implementations of Stochastic Gradient Descent (SGD) give rise to critical questions for compute-intensive Machine Learning (ML). Asynchrony implies speedup in some contexts, and challenges in others, as stale updates may lead to slower, or non-converging executions. While previous works showed a... | [] | null | 52 | null | null | [
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From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model | https://proceedings.mlr.press/v162/bae22a.html | [
"Heesun Bae",
"Seungjae Shin",
"Byeonghu Na",
"Joonho Jang",
"Kyungwoo Song",
"Il-Chul Moon"
] | null | null | Noisy labels are inevitable yet problematic in machine learning society. It ruins the generalization of a classifier by making the classifier over-fitted to noisy labels. Existing methods on noisy label have focused on modifying the classifier during the training procedure. It has two potential problems. First, these m... | [] | null | 53 | 2205.00690 | title_snapshot | [
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data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language | https://proceedings.mlr.press/v162/baevski22a.html | [
"Alexei Baevski",
"Wei-Ning Hsu",
"Qiantong Xu",
"Arun Babu",
"Jiatao Gu",
"Michael Auli"
] | null | null | While the general idea of self-supervised learning is identical across modalities, the actual algorithms and objectives differ widely because they were developed with a single modality in mind. To get us closer to general self-supervised learning, we present data2vec, a framework that uses the same learning method for ... | [] | null | 54 | 2202.03555 | title_snapshot | [
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End-to-End Balancing for Causal Continuous Treatment-Effect Estimation | https://proceedings.mlr.press/v162/bahadori22a.html | [
"Taha Bahadori",
"Eric Tchetgen Tchetgen",
"David Heckerman"
] | null | null | We study the problem of observational causal inference with continuous treatment. We focus on the challenge of estimating the causal response curve for infrequently-observed treatment values. We design a new algorithm based on the framework of entropy balancing which learns weights that directly maximize causal inferen... | [] | null | 55 | 2107.13068 | title_snapshot | [
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A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs | https://proceedings.mlr.press/v162/bai22a.html | [
"Lu Bai",
"Lixin Cui",
"Hancock Edwin"
] | null | null | In this paper, we develop a new graph kernel, namely the Hierarchical Transitive-Aligned Kernel, by transitively aligning the vertices between graphs through a family of hierarchical prototype graphs. Comparing to most existing state-of-the-art graph kernels, the proposed kernel has three theoretical advantages. First,... | [] | null | 56 | 2002.04425 | title_snapshot | [
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Near-Optimal Learning of Extensive-Form Games with Imperfect Information | https://proceedings.mlr.press/v162/bai22b.html | [
"Yu Bai",
"Chi Jin",
"Song Mei",
"Tiancheng Yu"
] | null | null | This paper resolves the open question of designing near-optimal algorithms for learning imperfect-information extensive-form games from bandit feedback. We present the first line of algorithms that require only $\widetilde{\mathcal{O}}((XA+YB)/\varepsilon^2)$ episodes of play to find an $\varepsilon$-approximate Nash e... | [] | null | 57 | 2202.01752 | title_snapshot | [
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Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification | https://proceedings.mlr.press/v162/bai22c.html | [
"Junwen Bai",
"Shufeng Kong",
"Carla P Gomes"
] | null | null | Multi-label classification (MLC) is a prediction task where each sample can have more than one label. We propose a novel contrastive learning boosted multi-label prediction model based on a Gaussian mixture variational autoencoder (C-GMVAE), which learns a multimodal prior space and employs a contrastive loss. Many exi... | [] | null | 58 | 2112.00976 | title_snapshot | [
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A$^3$T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing | https://proceedings.mlr.press/v162/bai22d.html | [
"He Bai",
"Renjie Zheng",
"Junkun Chen",
"Mingbo Ma",
"Xintong Li",
"Liang Huang"
] | null | null | Recently, speech representation learning has improved many speech-related tasks such as speech recognition, speech classification, and speech-to-text translation. However, all the above tasks are in the direction of speech understanding, but for the inverse direction, speech synthesis, the potential of representation l... | [] | null | 59 | 2203.09690 | title_snapshot | [
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Stability Based Generalization Bounds for Exponential Family Langevin Dynamics | https://proceedings.mlr.press/v162/banerjee22a.html | [
"Arindam Banerjee",
"Tiancong Chen",
"Xinyan Li",
"Yingxue Zhou"
] | null | null | Recent years have seen advances in generalization bounds for noisy stochastic algorithms, especially stochastic gradient Langevin dynamics (SGLD) based on stability (Mou et al., 2018; Li et al., 2020) and information theoretic approaches (Xu & Raginsky, 2017; Negrea et al., 2019; Steinke & Zakynthinou, 2020). In this p... | [] | null | 60 | 2201.03064 | title_snapshot | [
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Certified Neural Network Watermarks with Randomized Smoothing | https://proceedings.mlr.press/v162/bansal22a.html | [
"Arpit Bansal",
"Ping-Yeh Chiang",
"Michael J Curry",
"Rajiv Jain",
"Curtis Wigington",
"Varun Manjunatha",
"John P Dickerson",
"Tom Goldstein"
] | null | null | Watermarking is a commonly used strategy to protect creators’ rights to digital images, videos and audio. Recently, watermarking methods have been extended to deep learning models – in principle, the watermark should be preserved when an adversary tries to copy the model. However, in practice, watermarks can often be r... | [] | null | 61 | 2207.07972 | title_snapshot | [
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Data Scaling Laws in NMT: The Effect of Noise and Architecture | https://proceedings.mlr.press/v162/bansal22b.html | [
"Yamini Bansal",
"Behrooz Ghorbani",
"Ankush Garg",
"Biao Zhang",
"Colin Cherry",
"Behnam Neyshabur",
"Orhan Firat"
] | null | null | In this work, we study the effect of varying the architecture and training data quality on the data scaling properties of Neural Machine Translation (NMT). First, we establish that the test loss of encoder-decoder transformer models scales as a power law in the number of training samples, with a dependence on the model... | [] | null | 62 | 2202.01994 | title_snapshot | [
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Learning Stable Classifiers by Transferring Unstable Features | https://proceedings.mlr.press/v162/bao22a.html | [
"Yujia Bao",
"Shiyu Chang",
"Dr.Regina Barzilay"
] | null | null | While unbiased machine learning models are essential for many applications, bias is a human-defined concept that can vary across tasks. Given only input-label pairs, algorithms may lack sufficient information to distinguish stable (causal) features from unstable (spurious) features. However, related tasks often share s... | [] | null | 63 | 2106.07847 | title_snapshot | [
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Fast Composite Optimization and Statistical Recovery in Federated Learning | https://proceedings.mlr.press/v162/bao22b.html | [
"Yajie Bao",
"Michael Crawshaw",
"Shan Luo",
"Mingrui Liu"
] | null | null | As a prevalent distributed learning paradigm, Federated Learning (FL) trains a global model on a massive amount of devices with infrequent communication. This paper investigates a class of composite optimization and statistical recovery problems in the FL setting, whose loss function consists of a data-dependent smooth... | [] | null | 64 | 2207.08204 | title_snapshot | [
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Generative Modeling for Multi-task Visual Learning | https://proceedings.mlr.press/v162/bao22c.html | [
"Zhipeng Bao",
"Martial Hebert",
"Yu-Xiong Wang"
] | null | null | Generative modeling has recently shown great promise in computer vision, but it has mostly focused on synthesizing visually realistic images. In this paper, motivated by multi-task learning of shareable feature representations, we consider a novel problem of learning a shared generative model that is useful across vari... | [] | null | 65 | 2106.13409 | title_snapshot | [
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Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models | https://proceedings.mlr.press/v162/bao22d.html | [
"Fan Bao",
"Chongxuan Li",
"Jiacheng Sun",
"Jun Zhu",
"Bo Zhang"
] | null | null | Diffusion probabilistic models (DPMs) are a class of powerful deep generative models (DGMs). Despite their success, the iterative generation process over the full timesteps is much less efficient than other DGMs such as GANs. Thus, the generation performance on a subset of timesteps is crucial, which is greatly influen... | [] | null | 66 | 2206.07309 | title_snapshot | [
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On the Surrogate Gap between Contrastive and Supervised Losses | https://proceedings.mlr.press/v162/bao22e.html | [
"Han Bao",
"Yoshihiro Nagano",
"Kento Nozawa"
] | null | null | Contrastive representation learning encourages data representation to make semantically similar pairs closer than randomly drawn negative samples, which has been successful in various domains such as vision, language, and graphs. Recent theoretical studies have attempted to explain the benefit of the large negative sam... | [] | null | 67 | 2110.02501 | title_snapshot | [
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Representation Topology Divergence: A Method for Comparing Neural Network Representations. | https://proceedings.mlr.press/v162/barannikov22a.html | [
"Serguei Barannikov",
"Ilya Trofimov",
"Nikita Balabin",
"Evgeny Burnaev"
] | null | null | Comparison of data representations is a complex multi-aspect problem. We propose a method for comparing two data representations. We introduce the Representation Topology Divergence (RTD) score measuring the dissimilarity in multi-scale topology between two point clouds of equal size with a one-to-one correspondence be... | [] | null | 68 | 2201.00058 | title_snapshot | [
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Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxation | https://proceedings.mlr.press/v162/barik22a.html | [
"Adarsh Barik",
"Jean Honorio"
] | null | null | In this paper, we study the problem of sparse mixed linear regression on an unlabeled dataset that is generated from linear measurements from two different regression parameter vectors. Since the data is unlabeled, our task is to not only figure out a good approximation of regression parameter vectors but also label th... | [] | null | 69 | 2206.01167 | title_snapshot | [
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Neural Fisher Discriminant Analysis: Optimal Neural Network Embeddings in Polynomial Time | https://proceedings.mlr.press/v162/bartan22a.html | [
"Burak Bartan",
"Mert Pilanci"
] | null | null | Fisher’s Linear Discriminant Analysis (FLDA) is a statistical analysis method that linearly embeds data points to a lower dimensional space to maximize a discrimination criterion such that the variance between classes is maximized while the variance within classes is minimized. We introduce a natural extension of FLDA ... | [] | null | 70 | null | null | [
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Fictitious Play and Best-Response Dynamics in Identical Interest and Zero-Sum Stochastic Games | https://proceedings.mlr.press/v162/baudin22a.html | [
"Lucas Baudin",
"Rida Laraki"
] | null | null | This paper proposes an extension of a popular decentralized discrete-time learning procedure when repeating a static game called fictitious play (FP) (Brown, 1951; Robinson, 1951) to a dynamic model called discounted stochastic game (Shapley, 1953). Our family of discrete-time FP procedures is proven to converge to the... | [] | null | 71 | 2111.04317 | title_judge | [
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Information Discrepancy in Strategic Learning | https://proceedings.mlr.press/v162/bechavod22a.html | [
"Yahav Bechavod",
"Chara Podimata",
"Steven Wu",
"Juba Ziani"
] | null | null | We initiate the study of the effects of non-transparency in decision rules on individuals’ ability to improve in strategic learning settings. Inspired by real-life settings, such as loan approvals and college admissions, we remove the assumption typically made in the strategic learning literature, that the decision rul... | [] | null | 72 | 2103.01028 | title_snapshot | [
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On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces | https://proceedings.mlr.press/v162/bedi22a.html | [
"Amrit Singh Bedi",
"Souradip Chakraborty",
"Anjaly Parayil",
"Brian M Sadler",
"Pratap Tokekar",
"Alec Koppel"
] | null | null | We focus on parameterized policy search for reinforcement learning over continuous action spaces. Typically, one assumes the score function associated with a policy is bounded, which {fails to hold even for Gaussian policies. } To properly address this issue, one must introduce an exploration tolerance parameter to qua... | [] | null | 73 | 2201.12332 | title_snapshot | [
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Imitation Learning by Estimating Expertise of Demonstrators | https://proceedings.mlr.press/v162/beliaev22a.html | [
"Mark Beliaev",
"Andy Shih",
"Stefano Ermon",
"Dorsa Sadigh",
"Ramtin Pedarsani"
] | null | null | Many existing imitation learning datasets are collected from multiple demonstrators, each with different expertise at different parts of the environment. Yet, standard imitation learning algorithms typically treat all demonstrators as homogeneous, regardless of their expertise, absorbing the weaknesses of any suboptima... | [] | null | 74 | 2202.01288 | title_snapshot | [
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Matching Normalizing Flows and Probability Paths on Manifolds | https://proceedings.mlr.press/v162/ben-hamu22a.html | [
"Heli Ben-Hamu",
"Samuel Cohen",
"Joey Bose",
"Brandon Amos",
"Maximillian Nickel",
"Aditya Grover",
"Ricky T. Q. Chen",
"Yaron Lipman"
] | null | null | Continuous Normalizing Flows (CNFs) are a class of generative models that transform a prior distribution to a model distribution by solving an ordinary differential equation (ODE). We propose to train CNFs on manifolds by minimizing probability path divergence (PPD), a novel family of divergences between the probabilit... | [] | null | 75 | 2207.04711 | title_snapshot | [
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Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models | https://proceedings.mlr.press/v162/bengs22a.html | [
"Viktor Bengs",
"Aadirupa Saha",
"Eyke Hüllermeier"
] | null | null | We consider the regret minimization task in a dueling bandits problem with context information. In every round of the sequential decision problem, the learner makes a context-dependent selection of two choice alternatives (arms) to be compared with each other and receives feedback in the form of noisy preference inform... | [] | null | 76 | 2202.04593 | title_snapshot | [
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Neural Inverse Kinematic | https://proceedings.mlr.press/v162/bensadoun22a.html | [
"Raphael Bensadoun",
"Shir Gur",
"Nitsan Blau",
"Lior Wolf"
] | null | null | Inverse kinematic (IK) methods recover the parameters of the joints, given the desired position of selected elements in the kinematic chain. While the problem is well-defined and low-dimensional, it has to be solved rapidly, accounting for multiple possible solutions. In this work, we propose a neural IK method that em... | [] | null | 77 | null | null | [
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Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes | https://proceedings.mlr.press/v162/benton22a.html | [
"Gregory Benton",
"Wesley Maddox",
"Andrew Gordon Wilson"
] | null | null | A broad class of stochastic volatility models are defined by systems of stochastic differential equations, and while these models have seen widespread success in domains such as finance and statistical climatology, they typically lack an ability to condition on historical data to produce a true posterior distribution. ... | [] | null | 78 | 2207.06544 | title_snapshot | [
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Gradient Descent on Neurons and its Link to Approximate Second-order Optimization | https://proceedings.mlr.press/v162/benzing22a.html | [
"Frederik Benzing"
] | null | null | Second-order optimizers are thought to hold the potential to speed up neural network training, but due to the enormous size of the curvature matrix, they typically require approximations to be computationally tractable. The most successful family of approximations are Kronecker-Factored, block-diagonal curvature estima... | [] | null | 79 | 2201.12250 | title_snapshot | [
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Safe Learning in Tree-Form Sequential Decision Making: Handling Hard and Soft Constraints | https://proceedings.mlr.press/v162/bernasconi22a.html | [
"Martino Bernasconi",
"Federico Cacciamani",
"Matteo Castiglioni",
"Alberto Marchesi",
"Nicola Gatti",
"Francesco Trovò"
] | null | null | We study decision making problems in which an agent sequentially interacts with a stochastic environment defined by means of a tree structure. The agent repeatedly faces the environment over time, and, after each round, it perceives a utility and a cost, which are both stochastic. The goal of the agent is to learn an o... | [] | null | 80 | null | null | [
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Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification | https://proceedings.mlr.press/v162/bevan22a.html | [
"Peter Bevan",
"Amir Atapour-Abarghouei"
] | null | null | Convolutional Neural Networks have demonstrated dermatologist-level performance in the classification of melanoma from skin lesion images, but prediction irregularities due to biases seen within the training data are an issue that should be addressed before widespread deployment is possible. In this work, we robustly r... | [] | null | 81 | 2109.09818 | title_snapshot | [
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Approximate Bayesian Computation with Domain Expert in the Loop | https://proceedings.mlr.press/v162/bharti22a.html | [
"Ayush Bharti",
"Louis Filstroff",
"Samuel Kaski"
] | null | null | Approximate Bayesian computation (ABC) is a popular likelihood-free inference method for models with intractable likelihood functions. As ABC methods usually rely on comparing summary statistics of observed and simulated data, the choice of the statistics is crucial. This choice involves a trade-off between loss of inf... | [] | null | 82 | 2201.12090 | title_snapshot | [
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Minimax M-estimation under Adversarial Contamination | https://proceedings.mlr.press/v162/bhatt22a.html | [
"Sujay Bhatt",
"Guanhua Fang",
"Ping Li",
"Gennady Samorodnitsky"
] | null | null | We present a new finite-sample analysis of Catoni’s M-estimator under adversarial contamination, where an adversary is allowed to corrupt a fraction of the samples arbitrarily. We make minimal assumptions on the distribution of the uncontaminated random variables, namely, we only assume the existence of a known upper b... | [] | null | 83 | null | null | [
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Nearly Optimal Catoni’s M-estimator for Infinite Variance | https://proceedings.mlr.press/v162/bhatt22b.html | [
"Sujay Bhatt",
"Guanhua Fang",
"Ping Li",
"Gennady Samorodnitsky"
] | null | null | In this paper, we extend the remarkable M-estimator of Catoni \citep{Cat12} to situations where the variance is infinite. In particular, given a sequence of i.i.d random variables $\{X_i\}_{i=1}^n$ from distribution $\mathcal{D}$ over $\mathbb{R}$ with mean $\mu$, we only assume the existence of a known upper bound $\u... | [] | null | 84 | null | null | [
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Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning | https://proceedings.mlr.press/v162/bietti22a.html | [
"Alberto Bietti",
"Chen-Yu Wei",
"Miroslav Dudik",
"John Langford",
"Steven Wu"
] | null | null | Large-scale machine learning systems often involve data distributed across a collection of users. Federated learning algorithms leverage this structure by communicating model updates to a central server, rather than entire datasets. In this paper, we study stochastic optimization algorithms for a personalized federated... | [] | null | 85 | 2202.05318 | title_snapshot | [
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Non-Vacuous Generalisation Bounds for Shallow Neural Networks | https://proceedings.mlr.press/v162/biggs22a.html | [
"Felix Biggs",
"Benjamin Guedj"
] | null | null | We focus on a specific class of shallow neural networks with a single hidden layer, namely those with $L_2$-normalised data and either a sigmoid-shaped Gaussian error function (“erf”) activation or a Gaussian Error Linear Unit (GELU) activation. For these networks, we derive new generalisation bounds through the PAC-Ba... | [] | null | 86 | 2202.01627 | title_snapshot | [
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Structure-preserving GANs | https://proceedings.mlr.press/v162/birrell22a.html | [
"Jeremiah Birrell",
"Markos Katsoulakis",
"Luc Rey-Bellet",
"Wei Zhu"
] | null | null | Generative adversarial networks (GANs), a class of distribution-learning methods based on a two-player game between a generator and a discriminator, can generally be formulated as a minmax problem based on the variational representation of a divergence between the unknown and the generated distributions. We introduce s... | [] | null | 87 | 2202.01129 | title_snapshot | [
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Scalable Spike-and-Slab | https://proceedings.mlr.press/v162/biswas22a.html | [
"Niloy Biswas",
"Lester Mackey",
"Xiao-Li Meng"
] | null | null | Spike-and-slab priors are commonly used for Bayesian variable selection, due to their interpretability and favorable statistical properties. However, existing samplers for spike-and-slab posteriors incur prohibitive computational costs when the number of variables is large. In this article, we propose Scalable Spike-an... | [] | null | 88 | 2204.01668 | title_snapshot | [
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Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities | https://proceedings.mlr.press/v162/bitterwolf22a.html | [
"Julian Bitterwolf",
"Alexander Meinke",
"Maximilian Augustin",
"Matthias Hein"
] | null | null | It is an important problem in trustworthy machine learning to recognize out-of-distribution (OOD) inputs which are inputs unrelated to the in-distribution task. Many out-of-distribution detection methods have been suggested in recent years. The goal of this paper is to recognize common objectives as well as to identify... | [] | null | 89 | 2206.09880 | title_snapshot | [
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A query-optimal algorithm for finding counterfactuals | https://proceedings.mlr.press/v162/blanc22a.html | [
"Guy Blanc",
"Caleb Koch",
"Jane Lange",
"Li-Yang Tan"
] | null | null | We design an algorithm for finding counterfactuals with strong theoretical guarantees on its performance. For any monotone model $f : X^d \to \{0,1\}$ and instance $x^\star$, our algorithm makes \[{S}(f)^{O(\Delta_f(x^\star))}\cdot \log d\]{queries} to $f$ and returns an {\sl optimal} counterfactual for $x^\star$: a ne... | [] | null | 90 | 2207.07072 | title_snapshot | [
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Popular decision tree algorithms are provably noise tolerant | https://proceedings.mlr.press/v162/blanc22b.html | [
"Guy Blanc",
"Jane Lange",
"Ali Malik",
"Li-Yang Tan"
] | null | null | Using the framework of boosting, we prove that all impurity-based decision tree learning algorithms, including the classic ID3, C4.5, and CART, are highly noise tolerant. Our guarantees hold under the strongest noise model of nasty noise, and we provide near-matching upper and lower bounds on the allowable noise rate. ... | [] | null | 91 | 2206.08899 | title_snapshot | [
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Optimizing Sequential Experimental Design with Deep Reinforcement Learning | https://proceedings.mlr.press/v162/blau22a.html | [
"Tom Blau",
"Edwin V. Bonilla",
"Iadine Chades",
"Amir Dezfouli"
] | null | null | Bayesian approaches developed to solve the optimal design of sequential experiments are mathematically elegant but computationally challenging. Recently, techniques using amortization have been proposed to make these Bayesian approaches practical, by training a parameterized policy that proposes designs efficiently at ... | [] | null | 92 | 2202.00821 | title_snapshot | [
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Lagrangian Method for Q-Function Learning (with Applications to Machine Translation) | https://proceedings.mlr.press/v162/bojun22a.html | [
"Huang Bojun"
] | null | null | This paper discusses a new approach to the fundamental problem of learning optimal Q-functions. In this approach, optimal Q-functions are formulated as saddle points of a nonlinear Lagrangian function derived from the classic Bellman optimality equation. The paper shows that the Lagrangian enjoys strong duality, in spi... | [] | null | 93 | 2207.11161 | title_snapshot | [
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Generalized Results for the Existence and Consistency of the MLE in the Bradley-Terry-Luce Model | https://proceedings.mlr.press/v162/bong22a.html | [
"Heejong Bong",
"Alessandro Rinaldo"
] | null | null | Ranking problems based on pairwise comparisons, such as those arising in online gaming, often involve a large pool of items to order. In these situations, the gap in performance between any two items can be significant, and the smallest and largest winning probabilities can be very close to zero or one. Furthermore, ea... | [] | null | 94 | 2110.11487 | title_snapshot | [
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How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective | https://proceedings.mlr.press/v162/boopathy22a.html | [
"Akhilan Boopathy",
"Ila Fiete"
] | null | null | Recent works have examined theoretical and empirical properties of wide neural networks trained in the Neural Tangent Kernel (NTK) regime. Given that biological neural networks are much wider than their artificial counterparts, we consider NTK regime wide neural networks as a possible model of biological neural network... | [] | null | 95 | 2106.08453 | title_snapshot | [
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Improving Language Models by Retrieving from Trillions of Tokens | https://proceedings.mlr.press/v162/borgeaud22a.html | [
"Sebastian Borgeaud",
"Arthur Mensch",
"Jordan Hoffmann",
"Trevor Cai",
"Eliza Rutherford",
"Katie Millican",
"George Bm Van Den Driessche",
"Jean-Baptiste Lespiau",
"Bogdan Damoc",
"Aidan Clark",
"Diego De Las Casas",
"Aurelia Guy",
"Jacob Menick",
"Roman Ring",
"Tom Hennigan",
"Saffr... | null | null | We enhance auto-regressive language models by conditioning on document chunks retrieved from a large corpus, based on local similarity with preceding tokens. With a 2 trillion token database, our Retrieval-Enhanced Transformer (RETRO) obtains comparable performance to GPT-3 and Jurassic-1 on the Pile, despite using 25{... | [] | null | 96 | 2112.04426 | title_snapshot | [
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Lie Point Symmetry Data Augmentation for Neural PDE Solvers | https://proceedings.mlr.press/v162/brandstetter22a.html | [
"Johannes Brandstetter",
"Max Welling",
"Daniel E Worrall"
] | null | null | Neural networks are increasingly being used to solve partial differential equations (PDEs), replacing slower numerical solvers. However, a critical issue is that neural PDE solvers require high-quality ground truth data, which usually must come from the very solvers they are designed to replace. Thus, we are presented ... | [] | null | 97 | 2202.07643 | title_snapshot | [
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An iterative clustering algorithm for the Contextual Stochastic Block Model with optimality guarantees | https://proceedings.mlr.press/v162/braun22a.html | [
"Guillaume Braun",
"Hemant Tyagi",
"Christophe Biernacki"
] | null | null | Real-world networks often come with side information that can help to improve the performance of network analysis tasks such as clustering. Despite a large number of empirical and theoretical studies conducted on network clustering methods during the past decade, the added value of side information and the methods used... | [] | null | 98 | 2112.10467 | title_snapshot | [
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Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems | https://proceedings.mlr.press/v162/brenner22a.html | [
"Manuel Brenner",
"Florian Hess",
"Jonas M Mikhaeil",
"Leonard F Bereska",
"Zahra Monfared",
"Po-Chen Kuo",
"Daniel Durstewitz"
] | null | null | In many scientific disciplines, we are interested in inferring the nonlinear dynamical system underlying a set of observed time series, a challenging task in the face of chaotic behavior and noise. Previous deep learning approaches toward this goal often suffered from a lack of interpretability and tractability. In par... | [] | null | 99 | 2207.02542 | title_snapshot | [
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Learning to Predict Graphs with Fused Gromov-Wasserstein Barycenters | https://proceedings.mlr.press/v162/brogat-motte22a.html | [
"Luc Brogat-Motte",
"Rémi Flamary",
"Celine Brouard",
"Juho Rousu",
"Florence D’Alché-Buc"
] | null | null | This paper introduces a novel and generic framework to solve the flagship task of supervised labeled graph prediction by leveraging Optimal Transport tools. We formulate the problem as regression with the Fused Gromov-Wasserstein (FGW) loss and propose a predictive model relying on a FGW barycenter whose weights depend... | [] | null | 100 | 2202.03813 | title_snapshot | [
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0.0036426368169486523,
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0.... |
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