Alessio Russo | Boston University

General Deep Learning

Year	Paper	Venue
2023	Your diffusion model secretly knows the dimension of the data manifold
2022	Regularising Inverse Problems with Generative Machine Learning Models
2021	Why flatness correlates with generalization for Deep NN
2021	The Principles of Deep Learning Theory
2021	The Modern Mathematics of Deep Learning
2021	Slot Machines: Discovering Winning Combinations of Random Weights in Neural Networks
2021	SCORE-BASED GENERATIVE MODELING THROUGH STOCHASTIC DIFFERENTIAL EQUATIONS
2021	Loss landscapes and optimization in over-parameterized non-linear systems and neural networks
2021	Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck
2020	Neural tangent kernel
2018	Lipschitz regularity of deep neural networks: analysis and efficient estimation
2015	Weight Uncertainty in Neural Networks
1998	Efficient BackProp

Conformal Prediction

Year	Paper	Venue
2022	Conformal Prediction: a Unified Review of Theory and New Challenges
2022	Conformal Off-Policy Prediction in Contextual Bandits
2020	Conformal Prediction Under Covariate Shift
2019	Conformalized Quantile Regression
2005	Algorithmic Learning in a Random World

Differential Geometry in Deep Learning

Year	Paper	Venue
2020	Neural Ordinary Differential Equations on Manifolds
2019	Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds
2019	Diffeomorphic Learning
2019	Deep ReLU network approximation of functions on a manifold
2016	Deep nets for local manifold learning

Dimensionality Reduction

Year	Paper	Venue
2024	On Sparsity and Sub-Gaussianity in the Johnson-Lindenstrauss Lemma
2020	Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and Survey
2015	Parametric nonlinear dimensionality reduction using kernel t-SNE
2009	Learning a Parametric Embedding by Preserving Local Structure

Thompson Sampling

Year	Paper	Venue
2020	Neural Thompson Sampling
2020	A Tutorial on Thompson Sampling Bayesian Learning
2018	Deep Contextual Multi-armed Bandits

Deep Reinforcement Learning

Year	Paper	Venue
2025	Discovering state-of-the-art reinforcement learning algorithms Meta-Learning	Nature
2022	Exploring through Random Curiosity with General Value Functions Exploration
2022	CICERO Famous Applications
2021	Mastering Atari with Discrete World-Models Dreamer Algorithm
2021	GMAC: A Distributional Perspective on Actor-Critic Framework Distributional RL
2021	Enforcing Robust Control Guarantees with Neural Network Policies Stability
2021	Adversarial Intrinsic Motivation for Reinforcement Learning Goal Conditioned RL
2020	Sample-Based Distributional Policy Gradient Distributional RL
2020	Planning to Explore via Supervised World-Models Dreamer Algorithm
2020	Planning go explore via self-supervised world models Exploration
2020	Hypermodels for Exploration Exploration
2020	Dream to Control Dreamer Algorithm
2020	A Theoretic Analysis of DQN DQN Convergence
2020	A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation DQN Convergence
2019	Towards Characterizing Divergence in Deep Q-Learning DQN Convergence
2019	Statistics and Samples in Distributional Reinforcement Learning Distributional RL
2019	Learning Latent Dynamics from Pixels Dreamer Algorithm
2018	Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor Algorithms
2018	Equivalence Between Policy Gradients and Soft Q-Learning Soft Q Learning Theory
2018	DORA The Explorer: Directed Outreaching Reinforcement Action-Selection Exploration
2018	Distributed Distributional Deterministic Policy Gradients Distributional RL
2018	Control-Theoretic Analysis of Smoothness for Stability-Certified Reinforcement Learning Stability
2017	Reinforcement Learning with Deep Energy-Based Policies Algorithms
2017	#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Exploration
2016	Unifying Count-Based Exploration and Intrinsic Motivation Exploration
	List of algorithms Algorithms

Reinforcement Learning

Year	Paper	Venue
2024	Stop Regressing: Training Value Functions via Classification for Scalable Deep RL General Papers
2024	Correlated Proxies: A New Definition and Improved Mitigation for Reward Hacking Constrained / Safe RL
2023	Empirical Design in Reinforcement Learning General Papers
2023	Does Zero-Shot Reinforcement Learning Exist? Zero-Shot RL
2023	BACKSTEPPING TEMPORAL DIFFERENCE LEARNING General Papers
2023	An Analysis of Quantile Temporal-Difference Learning General Papers
2022	Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error General Papers
2022	Understanding Policy Gradient Algorithms: A Sensitivity-Based Approach General Papers
2022	TRC: Trust Region Conditional Value at Risk for Safe Reinforcement Learning Constrained / Safe RL
2022	Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk Constrained / Safe RL
2022	Safety-constrained Reinforcement Learning with a Distributional Safety Critic Constrained / Safe RL
2022	SAAC: Safe Reinforcement Learning as an Adversarial Game of Actor-Critics Constrained / Safe RL
2022	Constrained Variational Policy Optimization for Safe Reinforcement Learning Constrained / Safe RL
2022	Conformal Off-Policy Prediction in Contextual Bandits Off-Policy Evaluation
2022	A Review of Off-Policy Evaluation in Reinforcement Learning Off-Policy Evaluation
2021	Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction General Papers
2021	Learning Successor States and Goal-Dependent Values: A Mathematical Viewpoint General Papers
2021	Learning One Representation to Optimize All Rewards Zero-Shot RL
2021	Hoeffding’s Inequality for General Markov Chains and Its Applications to Statistical Learning MDPs
2021	Adaptive Sampling for Best Policy Identification in MDPs General Papers
2020	Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning General Papers
2020	On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces General Papers
2020	Fast active learning for pure exploration in reinforcement learning General Papers
2020	CoinDICE: Off-Policy Confidence Interval Estimation Off-Policy Evaluation
2019	Revisiting the Softmax Bellman Operator: New Benefits and New Perspective General Papers
2019	Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP General Papers
2019	Provably Efficient Reinforcement Learning with Linear Function Approximation General Papers
2019	Benchmarking Safe Exploration in Deep Reinforcement Learning Constrained / Safe RL
2018	Is Q-learning Provably Efficient? General Papers
2018	Deep Reinforcement Learning that Matters General Papers
2018	Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation Off-Policy Evaluation
2018	Adaptive Sampling for Policy Identification General Papers
2017	Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning Zero-Shot RL
2017	Risk-Constrained Reinforcement Learning with Percentile Risk Criteria Constrained / Safe RL
2017	Constrained Policy Optimization Constrained / Safe RL
2016	Learning the Variance of the Reward-To-Go General Papers
2015	Variance-Constrained Actor-Critic Algorithms for Discounted and Average Reward MDPs Constrained / Safe RL
2015	Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach Constrained / Safe RL
2015	High Confidence Policy Improvement Off-Policy Evaluation
2015	High Confidence Off-Policy Evaluation Off-Policy Evaluation
2015	A Comprehensive Survey on Safe Reinforcement Learning Constrained / Safe RL
2012	Policy Gradients with Variance Related Risk Criteria General Papers
2009	An Analysis of Reinforcement Learning with Function Approximation General Papers
2008	An Analysis of Model-Based Interval Estimation for Markov Decision Processes General Papers
2006	PAC Model-Free Reinforcement Learning General Papers
2004	Bias and Variance in Value Function Estimation General Papers
2001	TD Algorithm for the Variance of Return and Mean-Variance Reinforcement Learning General Papers
2001	Convergence of Optimistic and Incremental Q-Learning General Papers
2000	Eligibility Traces for Off-Policy Policy Evaluation Off-Policy Evaluation
2000	Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms General Papers
1993	Convergence of Stochastic Iterative Dynamic Programming Algorithms General Papers
1992	Reinforcement Learning Applied to Linear Quadratic Regulation General Papers
1982	The Variance of Discounted Markov Decision Processes General Papers

Bandit Algorithms

Year	Paper	Venue
2023	Quantile Bandits for Best Arms Identification
2020	Neural Contextual Bandits with UCB-based Exploration
2020	Neural Contextual Bandits with Deep Representation and Shallow Exploration
2020	Contributions to the Optimal Solution of Several Bandit Problems
2016	Optimal Best Arm Identification with Fixed Confidence
2016	Explore First, Exploit Next: The True Shape of Regret in Bandit Problems
2016	Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks
2011	Pure exploration in finitely-armed and continuous-armed bandits
2011	Online Least Squares Estimation with Self-Normalized Processes: An Application to Bandit Problems
2010	Best Arm Identification in Multi-Armed Bandits
2002	Using Confidence Bounds for Exploitation-Exploration Trade-offs
2002	THE NONSTOCHASTIC MULTIARMED BANDIT PROBLEM∗
2002	Finite-time Analysis of the Multiarmed Bandit Problem

In-Context Learning

Year	Paper	Venue
2024	Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective Bayesian Learning	ICML

Optimization

Year	Paper	Venue
2021	The Limits of Min-Max Optimization Algorithms: Convergence to Spurious Non-Critical Sets Min-Max Optimization
2021	A Mean-Field Analysis of Two-Player Zero-Sum Games Min-Max Optimization
2020	Robust Reinforcement Learning via Adversarial Training with Langevin Dynamics Min-Max Optimization
2020	On the Convergence of Single-Call Stochastic Extra-Gradient Methods Min-Max Optimization
2020	On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems Min-Max Optimization
2020	Non-convex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances Min-Max Optimization
2018	Finding Mixed Nash Equilibria of Generative Adversarial Networks Min-Max Optimization
2009	Subgradient Methods for Saddle-Point Problems Min-Max Optimization

Statistics

Year	Paper	Venue
2024	Information Lower Bounds for Robust Mean Estimation
2022	A Short Note on an Inequality between KL and TV
2020	A Tutorial on Quantile Estimation via Monte Carlo
2019	Safe Testing
2012	Concentration Inequalities for Order Statistics
1996	Importance Sampling for Monte Carlo Estimation of Quantiles
1987	Better Bootstrap Confidence Intervals
1982	Some Methods for Testing the Homogeneity of Rainfall Records

Probability Modeling & Inference

Year	Paper	Venue
2024	The Bayesian Learning Rule
2023	Semi-Implicit Variational Inference via Score Matching
2021	Normalizing Flows for Probabilistic Modeling and Inference
2020	Improved Techniques for Training Score-Based Generative Models
2019	Variational Approximations using Fisher Divergence
2018	Variational Inference: A Review for Statisticians
2018	Semi-Implicit Variational Inference
2017	Variational Hamiltonian Monte Carlo via Score Matching
2013	Stochastic Variational Inference
2013	Auto-Encoding Variational Bayes
2005	Estimation of Non-Normalized Statistical Models by Score Matching

Uncertainty Estimation

Year	Paper	Venue
2025	Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization
2023	Epistemic Neural Networks
2022	Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping

Statistical Learning

Year	Paper	Venue
2025	User-friendly Introduction to PAC-Bayes Bounds
2009	Agnostic Online Learning

Lecture Notes, Books and Courses

Year	Paper	Venue
2024	Hypothesis Testing with E-values (Book)
2021	Regularization in RL, Google
2021	Lecture Notes for Statistics 311/Electrical Engineering 377
2015	Rademacher Complexities and VC Dimension
2013	An Introduction to Stochastic Approximation
2008	Graphical Models, Exponential Families, and Variational Inference, Wainwright and Jordan
2006	System Identification and the Limits of Learning from Data
	The Elements of Statistical Learning, Hastie, Tibshirani, and Friedman, 2009
	RL Book Theory
	Reinforcement Learning: An Introduction
	Probability Theory: The Logic of Science, E. T. Jaynes, 2003
	Machine Learning: A Probabilistic Perspective, Murphy, 2012
	Deep Learning, Goodfellow et al., 2016
	CS285 at UC Berkeley, Deep Reinforcement Learning
	CS234 at Stanford University, Reinforcement Learning
	CS 6789: Foundations of Reinforcement Learning
	Bandit Algorithms
	15.097 at MIT, Prediction: Machine Learning and Statistics

Blogs

Year	Paper	Venue
	Lil'Log: Blog on Machine Learning
	Distill's Publication on Feature Visualization
	Deep Reinforcement Learning Doesn't Work Yet

Schools

Year	Paper	Venue
	School of Mathematics, UniPr
	Prairie Summer School
	Math Unipd - Vargiolu
	Machine Learning Schools

Bayesian Learning

Year	Paper	Venue
2024	Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective In-Context Learning	ICML
2021	Martingale posterior distributions
2020	A Tutorial on Thompson Sampling Thompson Sampling

Reading List

General Deep Learning

Conformal Prediction

Differential Geometry in Deep Learning

Dimensionality Reduction

Thompson Sampling

Deep Reinforcement Learning

Reinforcement Learning

Bandit Algorithms

In-Context Learning

Optimization

Statistics

Probability Modeling & Inference

Uncertainty Estimation

Statistical Learning

Lecture Notes, Books and Courses

Blogs

Schools

Bayesian Learning